CA2462099C - Compositions and methods for modeling saccharomyces cerevisiae metabolism - Google Patents

Abstract

The invention provides an in silica model for determining a S. cerevisiae physiological function. The model includes a data structure relating a plurality of S. cerevisiae reactants to a plurality of S. cerevisiae reactions, a constraint set for the plurality of S. cerevisiae reactions, and commands for determining a distribution of flux through the reactions that is predictive ofa s. cerevisiae physiological function. A model of the invention can further include a gene database containing information characterizing the associated gene or genes. The invention further provides methods for making an in silica S. cerevisiae model and methods for determining a S. cerevisiae physiological function using a model of the invention.

Description

COMPOSITIONS AND METHODS FOR MODELING
SACCHAROMYCES CEREVISIAE METABOLISM

This invention was made with United States Government support under grant NIH ROIHL59234 awarded by the National Institutes of Health. The U.S.
Government has certain rights in this invention.

BACKGROUND OF THE INVENTION

This invention relates generally to analysis of the activity of a chemical reaction network and, more specifically, to computational methods for simulating and predicting the activity of Saccharomyces cer evisiae (S. cerevisiae) reaction networks.

Saccharonryces cerevisiae is one of the best-studied microorganisms and in addition to its significant industrial importance it serves as a model organism.for the study of eukaryotic cells (Winzeler et al. Science 285: 901-906 (1999)). Up to 30% of positionally cloned genes implicated inhuman disease have yeast homologs.

The first eukaryotic genome to be sequenced was that of S. cerevisiae, and about 6400 open reading frames (or genes) have been identified in the genome. S.
cerevisiae was the subject of the first expression profiling experiments and a compendium of expression profiles for many different mutants and different growth conditions has been established.
Furthermore, a protein-protein interaction network has been defined and used to study the interactions between a large number of yeast proteins.

S. cerevisiae is used industrially to produce fuel ethanol, technical ethanol, beer, wine, spirits and baker's yeast, and is used as a host for production of many pharmaceutical proteins (hormones and vaccines). Furthermore, S. cerevisiae is currently being exploited as a cell factory for many different bioproducts including insulin.

Genetic manipulations, as well as changes in various fermentation conditions, are being considered in an attempt to improve the yield of industrially important products made by S. cerevisiae. However, these approaches are currently not guided by a clear understanding of how a change in a particular parameter, or combination of parameters, is likely to affect cellular behavior, such as the growth of the organism, the production of the desired product or the production of unwanted by-products. It would be valuable to be able to predict how changes in fermentation conditions, such as an increase or decrease in the supply of oxygen or a media component, would affect cellular behavior and, therefore, fermentation performance. Likewise, before engineering the organism by addition or deletion of one or more genes, it would be useful to be able to predict how these changes would affect cellular behavior.

However, it is currently difficult to make these sorts of predictions for S.
cerevisiae because of the complexity of the metabolic reaction network that is encoded by the S.
cerevisiae genome. Even relatively minor changes in media composition can affect hundreds of components of this network such that potentially hundreds of variables are worthy of consideration in making a prediction of fermentation behavior. Similarly, due to the complexity of interactions in the network, mutation of even a single gene can have effects on multiple components of the network Thus, there exists a need for a model that describes S.
cerevisiae reaction networks, such as its metabolic network, which can be used to simulate many different aspects of the cellular behavior of S. cerevisiae under different conditions.
The present invention satisfies this need, and provides related advantages as well.
RY OF TUE l 1VENTION
SUMMA
An object of the present invention is to provide compositions and methods for modeling Saccharomyces cerevisiae metabolism. In accordance with an aspect of the present invention, there is provided a computer readable medium or media, comprising:
(a) a data structure relating a plurality of Saccharomycess cerevisiae reactants to a plurality of Saccharomyces cerevisiae reactions, wherein each of said Saccharomyces cerevisiae reactions comprises a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating said substrate and said product, wherein at least one of said Saccharomyces cerevisiae reactions is annotated to indicate an associated gene;
(b) a gene database comprising information characterizing said associated gene;
(c) a constraint set for said plurality of Saccharomyces cerevisiae reactions, 2a and (d) commands for determining at least one flux distribution that minimizes or maximizes an objective function when said constraint set is applied to said data representation, wherein said at least one flux distribution is predictive of a Saccharomyces cerevisiae physiological function.
In accordance with another aspect of the invention, there is provided a method for predicting a Saccharomyces cerevisiae physiological function, comprising:

(a) providing a data structure relating a plurality of Saccharomyces cerevisiae reactants to a plurality of reactions, wherein each of said Saccharomyces cerevisiae reactions comprises a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating said substrate and said product, wherein at least one of said Saccharomyces cerevisiae reactions is annotated to indicate an associated gene;
(b) providing a constraint set for said plurality of Saccharomyces cerevisiae reactions;
(c) providing an objective function, and (d) determining at least one flux distribution that minimizes or maximizes said objective function when said constraint set is applied to said data structure, thereby predicting a Saccharomyces cerevisiae physiological function related to said gene.

In accordance with another aspect of the invention, there is provided a method for predicting Saccharomyces cerevisiae growth, comprising:
(a) providing a data structure relating a plurality of Saccharomyces cerevisiae reactants to a plurality of Saccharomyces cere*iae reactions, wherein each of said Saccharomyces cerevisiae reactions comprises a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating said substrate and said product;
(b) providing a constraint set for said plurality of Saccharomyces cerevisiae reactions;
(c) providing an objective function, and (d) determining at least one flux distribution that minimizes or maximizes said 2b objective function when said constraint set is applied to said data structure, thereby predicting Saccharomyces cerevisiae growth.

In accordance with another aspect of the invention, there is provided a method for mating a data structure relating a plurality of Saccharomyces cerevisiae reactants to a plurality of Saccharomyces cerevisiae reactions in a computer readable medium or media, comprising:
(a) identifying a plurality of Saccharomyces cerevisiae reactions and a plurality of Saccharomyces cerevisiae reactants that are substrates and products of said Saccharomyces cerevisiae reactions;
(b) relating said plurality of Saccharomyces cerevisiae reactants to said plurality of Saccharomyces cerevisiae reactions in a data structure, wherein each of said Saccharoinyces cerevisiae reactions comprises a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating said-substrate and said product; .
(c) determining a constraint set for said plurality of Saccharomyces cerevisiae reactions;
(d) providing an objective function;
(e) determining at least one flux distribution that minimizes or maximizes said objective function when said constraint set is applied to said data structure, and (f) if said at least one flux distribution is not predictive of a Saccharomyces cerevisiae physiological function, then adding a reaction to or deleting a reaction from said data structure and repeating step (e), if said at least one flux distribution is predictive of a Saccharomyces cerevisiae physiological function, then storing said data structure in a computer readable medium or media.
In accordance with another aspect of the invention, there is provided a data structure relating a plurality of Saccharomyces cerevisiae reactants to a plurality of Saccharomyces cerevisiae reactions, wherein said data structure is produced by a process comprising:
(a) identifying a plurality of Saccharomyces cerevisiae reactions and a plurality of Saccharomyces cerevisiae reactants that are substrates and products of said Saccharomyces cerevisiae reactions;
(b) relating said plurality of Saccharomyces cerevisiae reactants to said 2c plurality of Saccharomyces cerevisiae reactions in a data structure, wherein each of said Saccharomyces cerevisiae reactions comprises a reactant identified as. a substrate of the reactionõa reactant identified as a product of the reaction and 'a stoichiometric coefficient relating said substrate and said product;
(c) determining a constraint set for said plurality of Saccharonnyces cerevisiae reactions;
(d) providing an objective function;
(e) determining at least one flux distribution that minimizes or maximizes said objective function when said constraint set is applied to said data structure, and (f) if said at least one flux distribution is not predictive of Saccharomyces cerevisiae physiology, then adding a reaction to or deleting a reaction from said data structure and repeating step (e), if said at least one flux distribution is predictive of Saccharoinyces cerevisiae physiology, then storing said data structure in a computer readable medium or media.

The invention provides a computer readable medium or media, including: (a) a data structure relating a plurality of reactants in S. cerevisiae to a plurality of reactions in S.
cerevisiae, wherein each of the S. cerevisiae reactions includes a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating the substrate and the product, (b) a constraint set for the plurality of S.
cerevisiae reactions, and (c) commands for determining at least one flux distribution that minimizes or maximizes an objective function when the constraint set is applied to the data representation, wherein at least one flux distribution is predictive of a physiological function of S. cerevisiae. In one embodiment, at least one of the cellular reactions in the data structure is annotated to indicate an associated gene and the computer readable medium or media further includes a gene database including information characterizing the associated gene. In another embodiment, at least one of the cellular reactions in the data structure is annotated with an assignment of function within a subsystem or a compartment within the cell.
[0009] The invention also provides a method for predicting physiological function of S.
cerevisiae, including: (a) providing a data structure relating a plurality of S. cerevisiae to a plurality of S. cerevisiae reactions, wherein each of the S. cerevisiae reactions includes a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating the substrate and the product; (b) providing a constraint set for the plurality of S. cerevisiae reactions; (c) providing an objective function, and (d) determining at least one flux distribution that minimizes or maximizes the objective function when the constraint set is applied to the data structure, thereby predicting a S. cerevisiae physiological function. In one embodiment, at least one of the S. cerevisiae reactions in the data structure is annotated to indicate an associated gene and the method predicts a S. cerevisiae physiological function related to the gene.

[0010] Also provided by the invention is a method for making a data structure relating a plurality of S. cerevisiae reactants to a plurality of S. cerevisiae reactions in a computer readable medium or media, including: (a) identifying a plurality of S.
cerevisiae reactions and a plurality of reactants that are substrates and products of the reactions;
(b) relating the plurality of reactants to the plurality of reactions in a data structure, wherein each of the reactions includes a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating the substrate and the product;
(c) determining a constraint set for the plurality of S. cerevisiae reactions;
(d) providing an objective function; (e) determining at least one flux distribution that minimizes or maximizes the objective function when the constraint set is applied to the data structure, and (f) if at least one flux distribution is not predictive of a physiological function of S.
cerevisiae, then adding a reaction to or deleting a reaction from the data structure and repeating step (e), if at least one flux distribution is predictive of a physiological function of the eukaryotic cell, then storing the data structure in a computer readable medium or media. The invention further provides a data structure relating a plurality of S. cerevisiae reactants to a plurality of reactions, wherein the data structure is produced by the method.

BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 shows a schematic representation of a hypothetical metabolic network.
[0012] Figure 2 shows the stoichiometric matrix (S) for the hypothetical metabolic network shown in Figure 1.

[0013] Figure 3 shows mass balance constraints and flux constraints (reversibility constraints) that can be placed on the hypothetical metabolic network shown in Figure 1. (oo, infinity; Y1, uptake rate value) [0014] Figure 4 shows an exemplary metabolic reaction network in S.
cerevisiae.
[0015] Figure 5 shows a method for reconstruction of the metabolic network of S.
cerevisiae. Based on the available information from the genome annotation, biochemical pathway databases, biochemistry textbooks and recent publications, a genome-scale metabolic network for S. cerevisiae was designed. Additional physiological constraints were considered and modeled, such as growth, non-growth dependent ATP requirements and biomass composition.

[0016] Figure 6 shows a Phenotypic Phase Plane (PhPP) diagram for S.
cerevisiae revealing a finite number of qualitatively distinct patterns of metabolic pathway utilization divided into discrete phases. The characteristics of these distinct phases are interpreted using ratios of shadow prices in the form of isoclines. The isoclines can be used to classify these phases into futile, single and dual substrate limitation and to define the line of optimality.
The upper part of the figure shows a 3-dimensional S. cerevisiae Phase Plane diagram. The bottom part shows a 2-dimensional Phase Plane diagram with the line of optimality (LO) indicated.

[0017] Figure 7 shows the respiratory quotient (RQ) versus oxygen uptake rate (mmole/g-DW/hr) (upper left) on the line of optimality. The phenotypic phase plane (PhPP) illustrates that the predicted RQ is a constant of value 1.06 [0018] Figure 8 shows phases of metabolic phenotype associated with varying oxygen availability, from completely anaerobic fermentation to aerobic growth in S.
cerevisiae. The glucose uptake rate was fixed under all conditions, and the resulting optimal biomass yield, as well as respiratory quotient, RQ, are indicated along with the output fluxes associated with four metabolic by-products: acetate, succinate, pyruvate, and ethanol.

[0019] Figure 9 shows anaerobic glucose limited continuous culture of S.
cerevisiae.
Figure 9 shows the utilization of glucose at varying dilution rates in anaerobic chemostat culture. The data-point at the dilution rate of 0.0 is extrapolated from the experimental results. The shaded area or the infeasible region contains a set of stoichiometric constraints that cannot be balanced simultaneously with growth demands. The model produces the optimal glucose uptake rate for a given growth rate on the line of optimal solution (indicated by Model (optimal)). Imposition of additional constraints drives the solution towards a region where more glucose is needed (i.e. region of alternative sub-optimal solution). At the optimal solution, the in silica model does not secrete pyruvate and acetate.
The maximum difference between the model and the experimental points is 8% at the highest dilution rate.
When the model is forced to produce these by-products at the experimental level (Model (forced)), the glucose uptake rate is increased and becomes closer to the experimental values.
Figure 9B and 9C show the secretion rate of anaerobic by-products in chemostat culture. (q, secretion rate; D, dilution rate).

[0020] Figure 10 shows aerobic glucose-limited continuous culture of S.
cerevisiae in vivo and in silico. Figure 1OA shows biomass yield (Yx), and secretion rates of ethanol (Eth), and glycerol (Gly). Figure 1OB shows CO2 secretion rate (qco2) and respiratory quotient (RQ; i.e.
gco2/gO2) of the aerobic glucose-limited continuous culture of S. cerevisiae.
(exp, experimental).

DETAILED DESCRIPTION OF THE INVENTION

[0021] The present invention provides an in silico model of the baker's and brewer's yeast, S. cerevisiae, that describes the interconnections between the metabolic genes in the S.
cerevisiae genome and their associated reactions and reactants. The model can be used to simulate different aspects of the cellular behavior of S. cerevisiae under different environmental and genetic conditions, thereby providing valuable information for industrial and research applications. An advantage of the model of the invention is that it provides a holistic approach to simulating and predicting the metabolic activity of S.
cerevisiae.

[0022] As an example, the S. cerevisiae metabolic model can be used to determine the optimal conditions for fermentation performance, such as for maximizing the yield of a specific industrially important enzyme. The model can also be used to calculate the range of cellular behaviors that S. cerevisiae can display as a function of variations in the activity of one gene or multiple genes. Thus, the model can be used to guide the organismal genetic makeup for a desired application. This ability to make predictions regarding cellular behavior as a consequence of altering specific parameters will increase the speed and efficiency of industrial development of S. cerevisiae strains and conditions for their use.
[0023] The S. cerevisiae metabolic model can also be used to predict or validate the assignment of particular biochemical reactions to the enzyme-encoding genes found in the genome, and to identify the presence of reactions or pathways not indicated by current genomic data. Thus, the model can be used to guide the research and discovery process, potentially leading to the identification of new enzymes, medicines or metabolites of commercial importance.

[0024] The models of the invention are based on a data structure relating a plurality of S.
cerevisiae reactants to a plurality of S. cerevisiae reactions, wherein each of the S. cerevisiae reactions includes a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating the substrate and the product.
[0025] As used herein, the term "S. cerevisiae reaction" is intended to mean a conversion that consumes a substrate or forms a product that occurs in or by a viable strain of S.
cerevisiae. The term can include a conversion that occurs due to the activity of one or more enzymes that are genetically encoded by a S. cerevisiae genome. The term can also include a conversion that occurs spontaneously in a S. cerevisiae cell. Conversions included in the term include, for example, changes in chemical composition such as those due to nucleophilic or electrophilic addition, nucleophilic or electrophilic substitution, elimination, isomerization, deamination, phosphorylation, methylation, glycolysation, reduction, oxidation or changes in location such as those that occur due to a transport reaction that moves a reactant within the same compartment or from one cellular compartment to another. In the case of a transport reaction, the substrate and product of the reaction can be chemically the same and the substrate and product can be differentiated according to location in a particular cellular compartment. Thus, a reaction that transports a chemically unchanged reactant from a first compartment to a second compartment has as its substrate the reactant in the first compartment and as its product the reactant in the second compartment. It will be understood that when used in reference to an in silico model or data structure, a reaction is intended to be a representation of a chemical conversion that consumes a substrate or produces a product.
[00261 As used herein, the term "S. cerevisiae reactant" is intended to mean a chemical that is a substrate or a product of a reaction that occurs in or by a viable strain of S.
cerevisiae. The term can include substrates or products of reactions performed by one or more enzymes encoded by S. cerevisiae gene(s), reactions occurring in S.
cerevisiae that are performed by one or more non-genetically encoded macromolecule, protein or enzyme, or reactions that occur spontaneously in a S. cerevisiae cell. Metabolites are understood to be reactants within the meaning of the term. It will be understood that when used in reference to an in silico model or data structure, a reactant is intended to be a representation of a chemical that is a substrate or a product of a reaction that occurs in or by a viable strain of S.
cerevisiae.

[00271 As used herein the term "substrate" is intended to mean a reactant that can be converted to one or more products by a reaction. The term can include, for example, a reactant that is to be chemically changed due to nucleophilic or electrophilic addition, nucleophilic or electrophilic substitution, elimination, isomerization, deamination, phosphorylation, methylation, reduction, oxidation or that is to change location such as by being transported across a membrane or to a different compartment.

[00281 As used herein, the term "product" is intended to mean a reactant that results from a reaction with one or more substrates. The term can include, for example, a reactant that has been chemically changed due to nucleophilic or electrophilic addition, nucleophilic or electrophilic substitution, elimination, isomerization, deamination, phosphorylation, methylation, reduction or oxidation or that has changed location such as by being transported across a membrane or to a different compartment.

[00291 As used herein, the term "stoichiometric coefficient" is intended to mean a numerical constant correlating the number of one or more reactants and the number of one or more products in a chemical reaction. Typically, the numbers are integers as they denote the number of molecules of each reactant in an elementally balanced chemical equation that describes the corresponding conversion. However, in some cases the numbers can take on non-integer values, for example, when used in a lumped reaction or to reflect empirical data.
[00301 As used herein, the term "plurality," when used in reference to S.
cerevisiae reactions or reactants is intended to mean at least 2 reactions or reactants.
The term can include any number of S. cerevisiae reactions or reactants in the range from 2 to the number of naturally occurring reactants or reactions for a particular strain of S.
cerevisiae. Thus, the term can include, for example, at least 10, 20, 30, 50, 100, 150, 200, 300, 400, 500, 600 or more reactions or reactants. The number of reactions or reactants can be expressed as a portion of the total number of naturally occurring reactions for a particular strain of S.
cerevisiae such as at least 20%, 30%, 50%, 60%, 75%, 90%, 95% or 98% of the total number of naturally occurring reactions that occur in a particular strain of S.
cerevisiae.

[00311 As used herein, the term "data structure" is intended to mean a physical or logical relationship among data elements, designed to support specific data manipulation functions.
The term can include, for example, a list of data elements that can be added combined or otherwise manipulated such as a list of representations for reactions from which reactants can be related in a matrix or network. The term can also include a matrix that correlates data elements from two or more lists of information such as a matrix that correlates reactants to reactions. Information included in the term can represent, for example, a substrate or product of a chemical reaction, a chemical reaction relating one or more substrates to one or more products, a constraint placed on a reaction, or a stoichiometric coefficient.

[00321 As used herein, the term "constraint" is intended to mean an upper or lower boundary for a reaction. A boundary can specify a minimum or maximum flow of mass, electrons or energy through a reaction. A boundary can further specify directionality of a reaction. A boundary can be a constant value such as zero, infinity, or a numerical value such as an integer and non-integer.

[0033] As used herein, the term "activity," when used in reference to a reaction, is intended to mean the rate at which a product is produced or a substrate is consumed. The rate at which a product is produced or a substrate is consumed can also be referred to as the flux for the reaction.

[0034] As used herein, the term "activity," when used in reference to S.
cerevisiae is intended to mean the rate of a change from an initial state of S. cerevisiae to a final state of S.
cerevisiae. The term can include, the rate at which a chemical is consumed or produced by S.
cerevisiae, the rate of growth of S. cerevisiae or the rate at which energy or mass flow through a particular subset of reactions.

[0035] The invention provides a computer readable medium, having a data structure relating a plurality of S. cerevisiae reactants to a plurality of S.
cerevisiae reactions, wherein each of the S. cerevisiae reactions includes a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating the substrate and the product.

[0036] The plurality of S. cerevisiae reactions can include reactions of a peripheral metabolic pathway. As used herein, the term "peripheral," when used in reference to a metabolic pathway, is intended to mean a metabolic pathway that includes one or more reactions that are not a part of a central metabolic pathway. As used herein, the term "central," when used in reference to a metabolic pathway, is intended to mean a metabolic pathway selected from glycolysis, the pentose phosphate pathway (PPP), the tricarboxylic acid (TCA) cycle and the electron transfer system (ETS), associated anapleurotic reactions, and pyruvate metabolism.

[0037] A plurality of S. cerevisiae reactants can be related to a plurality of S. cerevisiae reactions in any data structure that represents, for each reactant, the reactions by which it is consumed or produced. Thus, the data structure, which is referred to herein as a "reaction network data structure," serves as a representation of a biological reaction network or system.
An example of a reaction network that can be represented in a reaction network data structure of the invention is the collection of reactions that constitute the metabolic reactions of S.
cerevisiae.

[0038] The methods and models of the invention can be applied to any strain of S.
cerevisiae including, for example, strain CEN.PK113.7D or any laboratory or production strain. A strain of S. cerevisiae can be identified according to classification criteria known in the art. Classification criteria include, for example, classical microbiological characteristics, such as those upon which taxonomic classification is traditionally based, or evolutionary distance as determined for example by comparing sequences from within the genomes of organisms, such as ribosome sequences.

[0039] The reactants to be used in a reaction network data structure of the invention can be obtained from or stored in a compound database. As used herein, the term "compound database" is intended to mean a computer readable medium or media containing a plurality of molecules that includes substrates and products of biological reactions. The plurality of molecules can include molecules found in multiple organisms, thereby constituting a universal compound database. Alternatively, the plurality of molecules can be limited to those that occur in a particular organism, thereby constituting an organism-specific compound database. Each reactant in a compound database can be identified according to the chemical species and the cellular compartment in which it is present. Thus, for example, a distinction can be made between glucose in the extracellular compartment versus glucose in the cytosol. Additionally each of the reactants can be specified as a metabolite of a primary or secondary metabolic pathway. Although identification of a reactant as a metabolite of a primary or secondary metabolic pathway does not indicate any chemical distinction between the reactants in a reaction, such a designation can assist in visual representations of large networks of reactions.

[0040] As used herein, the term "compartment" is intended to mean a subdivided region containing at least one reactant, such that the reactant is separated from at least one other reactant in a second region. A subdivided region included in the term can be correlated with a subdivided region of a cell. Thus, a subdivided region included in the term can be, for example, the intracellular space of a cell; the extracellular space around a cell; the periplasmic space; the interior space of an organelle such as a mitochondrium, endoplasmic reticulum, Golgi apparatus, vacuole or nucleus; or any subcellular space that is separated from another by a membrane or other physical barrier. Subdivided regions can also be made in order to create virtual boundaries in a reaction network that are not correlated with physical barriers. Virtual boundaries can be made for the purpose of segmenting the reactions in a network into different compartments or substructures.

[00411 As used herein, the term "substructure" is intended to mean a portion of the information in a data structure that is separated from other information in the data structure such that the portion of information can be separately manipulated or analyzed. The term can include portions subdivided according to a biological function including, for example, information relevant to a particular metabolic pathway such as an internal flux pathway, exchange flux pathway, central metabolic pathway, peripheral metabolic pathway, or secondary metabolic pathway. The term can include portions subdivided according to computational or mathematical principles that allow for a particular type of analysis or manipulation of the data structure.

[00421 The reactions included in a reaction network data structure can be obtained from a metabolic reaction database that includes the substrates, products, and stoichiometry of a plurality of metabolic reactions of S. cerevisiae. The reactants in a reaction network data structure can be designated as either substrates or products of a particular reaction, each with a stoichiometric coefficient assigned to it to describe the chemical conversion taking place in the reaction. Each reaction is also described as occurring in either a reversible or irreversible direction. Reversible reactions can either be represented as one reaction that operates in both the forward and reverse direction or be decomposed into two irreversible reactions, one corresponding to the forward reaction and the other corresponding to the backward reaction.
[00431 Reactions included in a reaction network data structure can include intra-system or exchange reactions. Intra-system reactions are the chemically and electrically balanced interconversions of chemical species and transport processes, which serve to replenish or drain the relative amounts of certain metabolites. These intra-system reactions can be classified as either being transformations or translocations. A transformation is a reaction that contains distinct sets of compounds as substrates and products, while a translocation contains reactants located in different compartments. Thus, a reaction that simply transports a metabolite from the extracellular environment to the cytosol, without changing its chemical composition is solely classified as a translocation, while a reaction such as the phosphotransferase system (PTS) which takes extracellular glucose and converts it into cytosolic glucose-6-phosphate is a translocation and a transformation.

[0044] Exchange reactions are those which constitute sources and sinks, allowing the passage of metabolites into and out of a compartment or across a hypothetical system boundary. These reactions are included in a model for simulation purposes and represent the metabolic demands placed on S. cerevisiae. While they may be chemically balanced in certain cases, they are typically not balanced and can often have only a single substrate or product. As a matter of convention the exchange reactions are further classified into demand exchange and input/output exchange reactions.

[0045] The metabolic demands placed on the S. cerevisiae metabolic reaction network can be readily determined from the dry weight composition of the cell which is available in the published literature or which can be determined experimentally. The uptake rates and maintenance requirements for S. cerevisiae can be determined by physiological experiments in which the uptake rate is determined by measuring the depletion of the substrate. The measurement of the biomass at each point can also be determined, in order to determine the uptake rate per unit biomass. The maintenance requirements can be determined from a chemostat experiment. The glucose uptake rate is plotted versus the growth rate, and the y-intercept is interpreted as the non-growth associated maintenance requirements. The growth associated maintenance requirements are determined by fitting the model results to the experimentally determined points in the growth rate versus glucose uptake rate plot.

[0046] Input/output exchange reactions are used to allow extracellular reactants to enter or exit the reaction network represented by a model of the invention. For each of the extracellular metabolites a corresponding input/output exchange reaction can be created.
These reactions can either be irreversible or reversible with the metabolite indicated as a substrate with a stoichiometric coefficient of one and no products produced by the reaction.
This particular convention is adopted to allow the reaction to take on a positive flux value (activity level) when the metabolite is being produced or removed from the reaction network and a negative flux value when the metabolite is being consumed or introduced into the reaction network. These reactions will be further constrained during the course of a simulation to specify exactly which metabolites are available to the cell and which can be excreted by the cell.

[00471 A demand exchange reaction is always specified as an irreversible reaction containing at least one substrate. These reactions are typically formulated to represent the production of an intracellular metabolite by the metabolic network or the aggregate production of many reactants in balanced ratios such as in the representation of a reaction that leads to biomass formation, also referred to as growth. As set forth in the Examples, the biomass components to be produced for growth include L-Alanine, L-Arginine, L-Asparagine, L-Aspartate, L-Cysteine, L-Glutamine, L-Glutamate, Glycine, L-Histidine, L-Isoleucine, L-Leucine, L-Lysine, L-Methionine, L-Phenylalanine, L-Proline, L-Serine, L-Threonine, L-Tryptophan, L-Tyrosine, L-Valine, AMP, GMP, CMP, UMP, dAMP, dCMP, dTMP, dGMP, Glycogen, alpha,alpha-Trehalose, Mannan, beta-D-Glucan, Triacylglycerol, Ergosterol, Zymosterol, Phosphatidate, Phosphatidylcholine, Phosphatidylethanolamine, Phosphatidyl-D-myo-inositol, Phosphatidylserine, ATP, Sulfate, ADP and Orthophosphate, with exemplary values shown in Table 1.

Table 1. Cellular components of S. cerevisiae (mmol/gDW).
ALA 0.459 CMP 0.05 ARG 0.161 dAMP 0.0036 ASN 0.102 dCMP 0-0024 ASP 0.297 dGMP 0.0024 CYS 0.007 DTMP 0.0036 GLU 0.302 TAGLY 0.007 GLN 0.105 ERGOST 0.0007 GLY 0.290 ZYMST 0.015 HIS 0.066 PA 0.0006 ILE 0.193 PINS 0.005 LEU 0.296 PS 0.002 LYS 0.286 PE 0.005 MET 0.051 PC 0.006 GLYCOGE
PHE 0.134 N 0.519 PRO 0.165 THE 0.023 SER 0.185 Mannan 0.809 THR 0.191 N 1.136 TRP 0.0 88 SLF 0.02 TYR 0.102 ATP 23.9166 VAL 0.265 ADP 23.9166 AMP 0.051 PI 23.9456 GMP 0.051 Biomass 1 UMP 0.067 [0048] A demand exchange reaction can be introduced for any metabolite in a model of the invention. Most commonly these reactions are introduced for metabolites that are required to be produced by the cell for the purposes of creating a new cell such as amino acids, nucleotides, phospholipids, and other biomass constituents, or metabolites that are to be produced for alternative purposes. Once these metabolites are identified, a demand exchange reaction that is irreversible and specifies the metabolite as a substrate with a stoichiometric coefficient of unity can be created. With these specifications, if the reaction is active it leads to the net production of the metabolite by the system meeting potential production demands. Examples of processes that can be represented as a demand exchange reaction in a reaction network data structure and analyzed by the methods of the invention include, for example, production or secretion of an individual protein;
production or secretion of an individual metabolite such as an amino acid, vitamin, nucleoside, antibiotic or surfactant; production of ATP for extraneous energy requiring processes such as locomotion;
or formation of biomass constituents.

[0049] In addition to these demand exchange reactions that are placed on individual metabolites, demand exchange reactions that utilize multiple metabolites in defined stoichiometric ratios can be introduced. These reactions are referred to as aggregate demand exchange reactions. An example of an aggregate demand reaction is a reaction used to simulate the concurrent growth demands or production requirements associated with cell growth that are placed on a cell, for example, by simulating the formation of multiple biomass constituents simultaneously at a particular cellular growth rate.

[0050] A hypothetical reaction network is provided in Figure 1 to exemplify the above-described reactions and their interactions. The reactions can be represented in the exemplary data structure shown in Figure 2 as set forth below. The reaction network, shown in Figure 1, includes intrasystem reactions that occur entirely within the compartment indicated by the shaded oval such as reversible reaction R2 which acts on reactants B and G and reaction R3 which converts one equivalent of B to two equivalents of F. The reaction network shown in Figure 1 also contains exchange reactions such as input/output exchange reactions AXt and E,,t, and the demand exchange reaction, Vw0Wth, which represents growth in response to the one equivalent of D and one equivalent of F. Other intrasystem reactions include Rl which is a translocation and transformation reaction that translocates reactant A into the compartment and transforms it to reactant G and reaction R6 which is a transport reaction that translocates reactant E out of the compartment.

[00511 A reaction network can be represented as a set of linear algebraic equations which can be presented as a stoichiometric matrix S, with S being an in x n matrix where in corresponds to the number of reactants or metabolites and n corresponds to the number of reactions taking place in the network. An example of a stoichiometric matrix representing the reaction network of Figure 1 is shown in Figure 2. As shown in Figure 2, each column in the matrix corresponds to a particular reaction n, each row corresponds to a particular reactant in, and each S,r,,, element corresponds to the stoichiometric coefficient of the reactant in in the reaction denoted n. The stoichiometric matrix includes intra-system reactions such as R2 and R3 which are related to reactants that participate in the respective reactions according to a stoichiometric coefficient having a sign indicative of whether the reactant is a substrate or product of the reaction and a value correlated with the number of equivalents of the reactant consumed or produced by the reaction. Exchange reactions such as -E,,t and -A,,t are similarly correlated with a stoichiometric coefficient. As exemplified by reactant E, the same compound can be treated separately as an internal reactant (E) and an external reactant (Eextemal) such that an exchange reaction (R6) exporting the compound is correlated by stoichiometric coefficients of -1 and 1, respectively. However, because the compound is treated as a separate reactant by virtue of its compartmental location, a reaction, such as R5, which produces the internal reactant (E) but does not act on the external reactant (Eexternal) is correlated by stoichiometric coefficients of 1 and 0, respectively. Demand reactions such as Vg o,th can also be included in the stoichiometric matrix being correlated with substrates by an appropriate stoichiometric coefficient.

[0052] As set forth in further detail below, a stoichiometric matrix provides a convenient format for representing and analyzing a reaction network because it can be readily manipulated and used to compute network properties, for example, by using linear programming or general convex analysis. A reaction network data structure can take on a variety of formats so long as it is capable of relating reactants and reactions in the manner exemplified above for a stoichiometric matrix and in a manner that can be manipulated to determine an activity of one or more reactions using methods such as those exemplified below. Other examples of reaction network data structures that are useful in the invention include a connected graph, list of chemical reactions or a table of reaction equations.

[0053] A reaction network data structure can be constructed to include all reactions that are involved in S. cerevisiae metabolism or any portion thereof. A portion of S. cerevisiae metabolic reactions that can be included in a reaction network data structure of the invention includes, for example, a central metabolic pathway such as glycolysis, the TCA
cycle, the PPP or ETS; or a peripheral metabolic pathway such as amino acid biosynthesis, amino acid degradation, purine biosynthesis, pyrimidine biosynthesis, lipid biosynthesis, fatty acid metabolism, vitamin or cofactor biosynthesis, transport processes and alternative carbon source catabolism. Examples of individual pathways within the peripheral pathways are set forth in Table 2, including, for example, the cofactor biosynthesis pathways for quinone biosynthesis, riboflavin biosynthesis, folate biosyntheis, coenzyme A
biosynthesis, NAD
biosynthesis, biotin biosynthesis and thiamin biosynthesis.

[0054] Depending upon a particular application, a reaction network data structure can include a plurality of S. cerevisiae reactions including any or all of the reactions listed in Table 2. Exemplary reactions that can be included are those that are identified as being required to achieve a desired S. cerevisiae specific growth rate or activity including, for example, reactions identified as ACO1, CDC19, CIT1, DAL7, ENO1, FBA1, FBP1, FUM1, GND1, GPM1, HXK1, ICL1, IDH1, IDH2, IDP1, IDP2, IDP3, KGD1, KGD2, LPD1, LSC1, LSC2, MDH1, MDH2, MDH3, MLS1, PDC1, PFK1, PFK2, PGI1, PGK1, PGM1, PGM2, PYC1, PYC2, PYK2, RKI1, RPE1, SOLI, TALI, TDH1, TDH2, TDH3, TKL1, TPI1, ZWF1 in Table 2. Other reactions that can be included are those that are not described in the literature or genome annotation but can be identified during the course of iteratively developing a S. cerevisiae model of the invention including, for example, reactions identified as MET6_2, MNADC, MNADDI, MNADE, MNADF_1, MNADPHPS, MNADG1, MNADG2, MNADH, MNPT1.

Table 2 Locus # E.C. # Gene Gene Description Reaction Ran Name Carbohydrate Metabolism Glycolysis/Gluconeogenesis YCL040W 2.7.1.2 GLKI Glucokinase GLC+ATP->G6P+ADP glkl_I
YCL040W 2.7.1.2 GLKI Glucokinase MAN+ATP-> MAN6P+ADP glkl 2 YCL040W 2.7.1.2 GLK1 Glucokinase bDGLC+ATP->bDG6P+ADP glkl 3 YFRO53C 2.7.1.1 HXK1 Hexokinase I (PI) (also called Hexokinase A) bDGLC + ATP -> G6P + ADP hxkl_1 YFR053C 2.7.1.1 HXK1 Hexokinase I (PI) (also called Hexokinase A) GLC + ATP ->
G61? + ADP hxkl_2 YFRO53C 2.7.1.1 HXKI Hexokinase I (PI) (also called Hexokinase A) MAN + ATP ->
MAN6P +ADP hxkl_3 YFRO53C 2.7.1.1 HXK1 Hexokinase I (PI) (also called Hexokinase A) ATP + FRU ->
ADP + F61? hxkl_4 YGL253W 2.7.1.1 HXK2 Hexokinase II (Pill (also called Hexokinase B) bDGLC +
ATP -> G61? + ADP hxk2_l YGL253W 2.7.1.1 HXK2 Hexokinase II (Pit) (also called Hexokinase B) GLC+ATP ->
G61? +ADP hxk2_2 YGL253W 2.7.1.1 HXK2 Hexokinase II (PII) (also called Hexokinase B) MAN + ATP -> MAN6P+ADP hxk2_3 YGL253W 2.7.1.1 HXK2 Hexokinase II (PII) (also called Hexokinase B) ATP + FRU -> ADP + F6P hxk2_4 YBR196C 5.3.1.9 PGII Glucose-6-phosphate isomerase G61? <-> F6P pgil_I
YBR196C 5.3.1.9 PGII Glucose-6-phosphate isomerase G61? <-> bDG6P pgil_2 YBR196C 5.3.1.9 PGIL Glucose-6-phosphate isomerase bDG6P <-> F6P pgil 3 YMR205C 2.7.1.11 PFK2 phosphofructokinase beta subunit F61? + ATP -> FDP + ADP
pfk2 YGR240C 2.7.1.11 PFKI phosphofnsctokinase alpha subunit F6P + ATP -> FDP + ADP
pfkl_1 YGR240C 2.7.1.11 PFK1 phosphofructokinase alpha subunit ATP+TAG6P->ADP+TAGI6P
pfkl_2 YGR240C 2.7.1.11 PFKI phosphofructokinase alpha subunit ATP + S7P -> ADP + S
17P pfkl_3 YKL060C 4.1.2.13 FBA1 fructose-bisphosphate aldolase FDP<->T3P2+T3P1 fbal_1 YDR050C 5.3.1.1 TPII triosephosphate isomerase T3P2 <-> T31? 1 tpil YJL052W 1.2.1.12 TDHI Glyceraldehyde-3-phosphate dehydrogenase 1 T3P1 + PI
+NAD <-> NADH + 13PDG tdhl YJR009C 1.2.1.12 TDH2 glyceraldehyde 3-phosphate dehydrogenase T3P 1 + PI +
NAD <-> NADH+13PDG tdh2 YGR192C 1.2.1.12 TDH3 Glyceraldehyde-3-phosphate dehydrogenase 3 T3P1 +PI+NAD
<-> NADH + 13PDG tdh3 YCRO12W 2.7.2.3 PGK1 phosphoglycerate kinase 13PDG + ADP <-> 3P0 + AT? pgkl YKL152C 5.4.2.1 GPM1 Phosphoglycerate mutase 13PDG <-> 23PDG gpml_I
YKL152C 5.4.2.1 GPMI Phosphoglycerate mutase 3PG <-> 2PG gpml_2 YDL021 W 5.4.2.1 GPM2 Similar to GPM1 (phosphoglycerate mutase) 3PG <-> 2PG
gpm2 YOL056W 5.4.2.1 GPM3 phosphoglycerate mutase 3PG <-> 2PG gpm3 YGR254W 4.2.1.11 EN01 enolase I 2PG <-> PEP cool YHR174W 4.2.1.11 EN02 enolase 2PG <-> PEP eno2 YMR323W 4.2.1.11 ERRI Protein with similarity to enolases 2PG <-> PEP eno3 YPL281C 4.2.1.11 ERR2 enolase related protein 2PG <-> PEP eno4 YOR393 W 4.2.1.11 ERRI enolase related protein 2PG <-> PEP eno5 YAL038W 2.7.1.40 CDC19 Pyruvate kinase PEP + ADP -> PYR + ATP cdc19 YOR347C 2.7.1.40 PYK2 Pyruvate kinase, glucose-repressed isoform PEP + AD? ->
PYR+ATP pyk2 YER178w 1.2.4.1 PDAI pyruvate dehydrogenase (lipoamide) alpha chain PYRm+COAm+NADm->NADHm+C02m+ pdal precursor, El component, alpha unit ACCOAm YBR221c 1.2.4.1 PDB I pyruvate dehydrogenase (lipoamide) beta chain precursor, El component, beta unit YNL071w 2.3.1.12 LAT1 dihydrolipoamide S-acetyltransferase, E2 component Citrate cycle (TCA cycle) YNROOIC 4.1.3.7 CITI Citrate synthase, Nuclear encoded mitochondrial ACCOAm +
OAm -> COAm + CITm citl protein.
YCRO05C 4.1.3.7 CIT2 Citrate synthase, non-mitochondrial citrate synthase ACCOA + OA -> COA + CIT cit2 YPROO1 W 4.1.3.7 cit3 Citrate synthase, Mitochondrial isoform of citrate ACCOAm + OAm -> COAm + CITm cit3 synthase YLR304C 4.2.1.3 acol Aconitase, mitochondrial CITm <-> ICITm acol YJL200C 4.2.1.3 YJL200C aconitate hydratase homolog CITm <-> ICITm aco2 YNL037C 1.1.1.41 IDH I Isocitrate dehydrogenase (NAD+) mito, subuintl ICITm +NADm -> C02m +NADHm+ AKGm idhl YOR136W 1.1.1.41 IDH2 Isocitrate dehydrogenase (NAD+) mito, subunit2 YDL066W 1.1.1.42 IDPI Isocitrate dehydrogenase (NADP+) ICITm+NADPm->NADPHm+OSUCm idpl_1 YLR174W 1.1.1.42 IDP2 Isocitrate dehydrogenase (NADP+) ICIT + NADP -> NADPH +
OSUC idp2_1 YNLO09W 1.1.1.42 IDP3 Isocitrate dehydrogenase (NADP+) ICIT + NADP -> NADPH +
OSUC idp3_1 YDL066W 1.1.1.42 IDP 1 Isocitrate dehydrogenase (NADP+) OSUCm -> C02m + AKGm idpl_2 YLR174W 1.1.1.42 IDP2 Isocitrate dehydrogenase (NADP+) OSUC -> C02 + AKG idp2 YNLO09W 1.1.1.42 IDP3 Isocitrate dehydrogenase (NADP+) OSUC -> C02 + AKG idp3 YIL125W 1.2.4.2 kgdl alpha-ketoglutaratedehydrogenasecomplex, El AKGm + NADm +
COAm -> C02m + NADHm + kgdla component SUCCOAm YDR148C 2.3.1.61 KGD2 Dihydrolipoamide S-succinyltransferase, E2 component YGR244C 6.2.1.4/6. LSC2 Succinate--CoA ligase (GDP-forming) ATPm+SUCCm+COAm<->ADPm+Plat + lsc2 2.1.5 SUCCOAm YOR142W 6.2.1.4/6. LSCI succinate-CoA ligase alpha subunit ATPm + ITCm + COAm <-> ADPm + PIm + Ise) 2.1.5 ITCCOAm Electron Transport System, Complex II
YKL141w 1.3.5.1 SDH3 succinate dehydrogenase cytochrome b SUCCm + FADm <->
FUMm + FADH2m sdh3 YKL148c 1.3.5.1 SDH1 succinate dehydrogenase cytochrome b YLL041c 1.3.5.1 SDH2 Succinate dehydrogenase (ubiquinone) iron-sulfur protein subunit YDR178w 1.3.5.1 SDH4 succinate dehydrogenase membrane anchor subunit YLR164w 1.3.5.1 YLR164 strong similarity to SDH4P
w YMRI 18c 1.3.5.1 YMR118 strong similarity to succinate dehydrogenase c YJL045w 1.3.5.1 YJL045w strong similarity to succinate dehydrogenase flavoprotein YEL047c 1.3.99.1 YEL047c soluble fumarate reductase, cytoplasmic FADH2m + FUM -> SUCC + FADm frdsl YJR051 W 1.3.99.1 osml Mitochondria) soluble fumarate reductase involved in FADH2m + FUMm -> SUCCm + FADm osml osmotic regulation YPL262W 4.2.1.2 FUM1 Fumaratase FUMm <-> MALm fuml_1 YPL262W 4.2.1.2 FUMI Fumaratase FUM <-> MAL fuml_2 YKL085W 1.1.1.37 MDHI mitochondrial malate dehydrogenase MALm+NADm<->NADHm+OAm mdhl YDL078C 1.1.1.37 MDH3 MALATE DEHYDROGENASE, PEROXISOMAL MAL+NAD<-> NADH+OA
mdh3 YOL126C 1.1.1.37 MDH2 malate dehydrogenase, cytoplasmic MAL + NAD <-> NADH +
OA mdh2 Anaplerotic Reactions YER065C 4.1.3.1 ICLI isocitrate lyase ICIT -> GLX + SUCC icl l YPRO06C 4.1.3.1 ICL2 Isocitrate lyase, may be nonfunctional ICIT -> GLX + SUCC
icl2 YIR031 C 4.1.3.2 dal? Malate synthase ACCOA + GLX -> COA + MAL dal) YNL117W 4.1.3.2 MLS1 Malate synthase ACCOA + GLX -> COA + MAL misl YKR097W 4.1.1.49 pckl phosphoenolpyruvate carboxylkinase OA + ATP -> PEP + C02 + ADP pckl YLR377C 3.1.3.11 FBP1 fructose-1,6-bisphosphatase FDP->F6P+PI fbpl YGL062W 6.4.1.1 PYC1 pyruvate carboxylase PYR + ATP + C02 -> ADP + OA + PI
pycl YBR218C 6.4.1.1 PYC2 pyruvate carboxylase PYR+ATP + C02 -> ADP + OA + PI pyc2 YKL029C 1.1.1.38 MAE1 mitochondrial malic enzyme MALm + NADPm -> C02m + NADPHm + PYRm mael Pentose phosphate cycle YNL24IC 1.1.1.49 zwfl Glucose-6-phosphate-l-dehydrogenase G6P + NADP <-> D6PGL
+ NADPH zwfl YNR034W 3.1.1.31 SOLI Possible 6-phosphogluconolactonase D6PGL -> D6PGC soil YCR073W- 3.1.1.31 SOL2 Possible 6-phosphogluconolactonase D6PGL -> D6PGC sol2 A
YHR163W 3.1.1.31 SOL3 Possible 6-phosphogluconolactonase D6PGL->D6PGC sol3 YGR248W 3.1.1.31 SOL4 Possible 6-phosphogluconolactonase D6PGL -> D6PGC sol4 YGR256W 1.1.1.44 GND2 6-phophogluconate dehydrogenase D6PGC+NADP->NADPH+C02+RL5P gnd2 YHR183W 1.1.1.44 GND1 6-phophogluconate dehydrogenase D6PGC+NADP->NADPH+C02+RL5P gndl YJL121C 5.1.3.1 RPE1 ribulose-5-P 3-epimerase RL5P <-> X5P rpel YOR095C 5.3.1.6 RKII ribose-5-P isomerase RLSP <-> R5P rkil YBR117C 2.2.1.1 TKL2 transketolase R5P + X5P <-> T3PI + S71? tkl2_t YBR117C 2.2.1.1 TKL2 transketolase X5P+E4P<->F6P+T3PI tkl2_2 YPR074C 2.2.1.1 TKLI transketolase R5P + X5P <-> T3P1 + S7P tkll_I
YPR074C 2.2.1.1 TKLI transketolase X5P+E4P<->F6P+T3P1 tkll_2 YLR354C 2.2.1.2 TALI transaldolase T3PI + S7P <-> E4P + F6P tall_I
YGR043C 2.2.1.2 YGR043 transaldolase T3P1 + S7P <-> E4P + F6P tall-2 C
YCR036W 2.7.1.15 RBKI Ribokinase RIB + ATP -> R5P + ADP rbkl_1 YCR036W 2.7.1.15 RBK1 Ribokinase DRIB + ATP -> DR5P + ADP rbkl_2 YKL127W 5.4.2.2 pgml phosphoglucomutase RIP <-> R5P pgml_l YKL127W 5.4.2.2 pgml phosphoglucomutase1 G1P<->G6P pgml_2 YMR105C 5.4.2.2 pgm2 phosphoglucomutase RIP <->R5P pgm2_1 YMR105C 5.4.2.2 pgm2 Phosphoglucomutase GIP <->G6P pgm2_2 Mannose YER003C 5.3.1.8 PMI40 mannose-6-phosphate isomerase MAN61? <-> F6P pmi4o YFL045C 5.4.2.8 SEC53 phosphomannomutase MAN6P <-> MANIP sec53 YDL055C 2.7.7.13 PSAI mannose-1-phosphate guanyltransferase,GDP-mannose GTP+MAN1P->PPI+GDPMAN psal pyrophosphorylase Fructose YILI07C 2.7.1.105 PFK26 6-Phosphoructose-2-kinase ATP + F6P -> ADP + F26P
pfk26 YOL136C 2.7.1.105 pfk27 6-phosphofructo-2-kinase ATP + F6P -> ADP + F26P pfk27 YJL155C 3.1.3.46 FBP26 Fructose-2,6-biphosphatase F26P->F6P+PI fbp26 2.7.1.56 - I-Phosphofructokinase (Fructose 1-phosphate kinase) FIP+ATP->FDP+ADP frc3 Sorbose S.c. does not metabolize sorbitol, erythritol, mannitol, xylitol, ribitol, arabinitol, galactinol YJR159W 1.1.1.14 SOR1 sorbitol dehydrogenase (L-iditol 2-dehydrogenase) SOT +
NAD -> FRU + NADH sorl Galactose metabolism YBR020W 2.7.1.6 gall galactokinase GLAC+ATP->GALIP+ADP gall YBROI8C 2.7.7.10 gal7 galactose-l -phosphateuridyltransferase UTP+GALIP<-> PPI
+ UDPGAL gall YBRO19C 5.1.3.2 gallO UDP-glucose 4-epimerase UDPGAL <-> UDPG gall0 YHLO12W 2.7.7.9 YHLO12 UTP-GlucoseI -Phosphate Uridylyltransferase GIP + UTP <-> UDPG + PPI ugpl_2 W
YKL035W 2.7.7.9 UGPI Uridinephosphoglucose pyrophosphorylase GIP+UTP<->UDPG+PPI ugpt_1 YBRI84W 3.2.1.22 YBRI84 Alpha-galactosidase(melibiase) MELI->GLC+GLAC mell 1 W
YBRI84W 3.2.1.22 YBRl84 Alpha-galactosidase (melibiase) DFUC->GLC+GLAC mell 2 W
YBR184W 3.2.1.22 YBRl84 Alpha-galactosidase (melibiase) RAF -> GLAC + SUC mell W
YBRI84W 3.2.1.22 YBRI84 Alpha-galactosidase (melibiase) GLACL <-> MYOI + GLAC
mell 4 W
YBRI84W 3.2.1.22 YBR184 Alpha-galactosidase (melibiase) EPM <-> MAN + GLAC
mell_5 W
YBR184W 3.2.1.22 YBR184 Alpha-galactosidase (melibiase) GGL <-> GL + GLAC
mell_6 W
YBR184W 3.2.1.22 YBRl84 Alpha-galactosidase (melibiase) MELT <-> SOT + GLAC
mell 7 W
YBR299W 3.2.1.20 MAL32 Maltase MLT -> 2 GLC mal32a YGR287C 3.2.1.20 YGR287 putative alpha glucosidase MLT -> 2 GLC mal32b C
YGR292W 3.2.1.20 MAL12 Maltase MLT -> 2 GLC mall2a YIL172C 3.2.1.20 YILI72C putative alpha glucosidase MLT -> 2 GLC mall2b YJL216C 3.2.1.20 YJL216C probable alpha-glucosidase (MALTase) MLT -> 2 GLC
mall2c YJL221C 3.2.1.20 FSP2 homology to maltase(alpha-D-glucosidase) MLT -> 2 GLC
fsp2a YJL221C 3.2.1.20 FSP2 homology to maltase(alpha-D-glucosidase) 6DGLC -> GLAC +
GLC fsp2b YBROI8C 2.7.7.12 GAL7 UDPglucose-hexose-l-phosphate uridylyltransferase UDPG+GALIP<-> G1P+UDPGAL unkrxl0 Trehalose YBR126C 2.4.1.15 TPSI trehalose-6-P synthetase, 56 kD synthase subunit of UDPG+G6P -> UDP+TRE6P tpsl trehalose-6-phosphate synthaseVphosphatase complex YMLIOOW 2.4.1.15 tsl I trehalose-6-P synthetase, 123 kD regulatory subunit of UDPG + G6P -> UDP + TRE6P tall trehalose-6-phosphate synthaseVphosphatase complex\;
homologous to TPS3 gene product YMR261C 2.4.1.15 TPS3 trehalose-6-P synthetase, 115 kD regulatory subunit of UDPG+G6P->UDP+TRE6P tps3 trehalose-6-phosphate synthaseVphosphatase complex YDR074 W 3.1.3.12 TPS2 Trehalose-6-phosphate phosphatase TRE6P -> THE +PI tps2 YPR026W 3.2.1.28 ATH1 Acid trehalase THE -> 2 GLC affil YBROOIC 3.2.1.28 NTH2 Neutral trehalase, highly homologous to Nthlp THE -> 2 GLC nth2 YDR001C 3.2.1.28 NTHI neutral trehalase THE -> 2 GLC nthl Glycogen Metabolism (sucorose and sugar metabolism) YEL011W 2.4.1.18 gle3 Branching enzyme, l,4-glucan-6-(1,4-glucano)- GLYCOGEN 4-P1-> 01? glc3 transferase YPR160W 2.4.1.1 GPH1 Glycogen phosphorylase GLYCOGEN+PI->G1P gphl YFRO 15C 2.4.1.11 GSY1 Glycogen synthase (UDP-gluocse-starch UDPG -> UDP +
GLYCOGEN gsyl glucosyltransferase) YLR258W 2.4.1.11 GSY2 Glycogen synthase (UDP-gluocse-starch UDPG -> UDP +
GLYCOGEN gsy2 glucosyltransferase) Pyruvate Metabolism YAL054C 6.2.1.1 acsI acetyl-coenzyme A synthetase ATPm+ACm+COAm->AMPm+PPIm+
acsl ACCOAm YLR153C 6.2.1.1 ACS2 acetyl-coenzyme A synthetase ATP + AC + COA -> AMP + PPI
+ ACCOA acs2 YDL168W 1.2.1.1 SFA1 Formaldehyde dehydrogenase/long-chain alcohol FALD + RGT
+ NAD <-> FGT + NADH sfal_I
dehydrogenase YJL068C 3.1.2.12 - S-Formylglutathionehydrolase FGT <-> ROT + FOR unkrxl1 YGR087C 4.1.1.1 PDC6 pyruvate decarboxylase PYR -> CO2 + ACAL pdc6 YLR134W 4.1.1.1 PDC5 pyruvate decarboxylase PYR -> C02 + ACAL pdc5 YLR044C 4.1.1.1 pdcl pyruvate decarboxylase PYR -> C02 + ACAL pdcl YBLO15W 3.1.2.1 ACHI acetyl CoA hydrolase COA + AC -> ACCOA achl_l YBLO15W 3.1.2.1 ACH1 acetyl CoA hydrolase COAm+ACm->ACCOAm achl_2 YDL131 W 4.1.3.21 LYS21 probable homocitrate synthase, mitochondrial isozyme ACCOA + AKG -> HCIT + COA lys21 precursor YDL182W 4.1.3.21 LYS20 homocitrate synthase, cytosolic isozyme ACCOA + AKG ->
HCIT + COA lys20 YDLI82W 4.1.3.21 LYS20 Homocitrate synthase ACCOAm + AKGm -> HCITm + COAm lys2Oa YGL256W 1.1.1.1 adh4 alcohol dehydrogenase isoenzyme IV ETH+NAD <-> ACAL +
NADH adh4 YMR083W 1.1.1.1 adh3 alcohol dehydrogenase isoenzyme III ETHm+NADm <-> ACALm +NADHm adh3 YMR303C 1.1.1.1 adh2 alcohol dehydrogenase 11 ETH + NAD <-> ACAL + NADH adh2 YBR145W 1.1.1.1 ADH5 alcohol dehydrogenase isoenzyme V ETH+NAD <-> ACAL+NADH
adh5 YOL086C 1.1.1.1 adhl Alcohol dehydrogenase I ETH + NAD <-> ACAL + NADH adhl YDL168W 1.1.1.1 SFAI Alcohol dehydrogenase I ETH + NAD <-> ACAL + NADH sfal_2 Glyoxylate and dicarboxylate metabolism Glyoxal Pathway YML004C 4.4.1.5 GLOI Lactoylglutathione lyase, glyoxalase I RGT + MTHGXL <->
LOT glol YDR272W 3.1.2.6 GL02 Hydroxyacylglutathionehydrolase LGT -> ROT + LAC glo2 YOR04OW 3.1.2.6 GL04 glyoxalase II (hydroxyacylglutathione hydrolase) LGTm ->
RGTm + LACm glo4 Energy Metabolism Oxidative Phosphorylation YBRO11C 3.6.1.1 ippl Inorganic pyrophosphatase PPI->2PI ippl YMR267W 3.6.1.1 ppa2 mitochondrial inorganic pyrophosphatase PPIm -> 2 Pita ppa2 1.2.2.1 FDNG Formate dehydrogenase FOR + Qm -> QH2m + C02 +2 NEXT fdng YML120C 1.6.5.3 NDII NADH dehydrogenase (ubiquinone) NADHm+Qm-> QH2m+NADm ndil YDLO85W 1.6.5.3 NDH2 Mitochondrial NADH dehydrogenase that catalyzes the NADH
+ Qm -> QH2m +NAD ndh2 oxidation of cytosolic NADH

YMR145C 1.6.5.3 NDH1 Mitochondrial NADH dehydrogenase that catalyzes the NADH
+ Qm -> QH2m +NAD ndhl oxidation of cytosolicNADH
YHR042W 1.6.2.4 NCP I NADPH--ferrihemoprotein reductase NADPH + 2 FERIm ->
NADP + 2 FEROm ncpl YKLI41w 1.3.5.1 SDH3 succinate dehydrogenase cytochrome b FADH2m + Quo <->
FADm + QH2m fad YKL148c 1.3.5.1 SDHI succinate dehydrogenase cytochrome b YLL041c 1.3.5.1 SDH2 succinate dehydrogenase cytochrome b YDR178w 1.3.5.1 SDH4 succinate dehydrogenase cytochrome b Electron Transport System, Complex III
YEL024W 1.10.2.2 RIP 1 ubiquinol-cytochrome c reductase iron-sulfur subunit 02m + 4 FEROm + 6 Hm -> 4 FERIm cyto Q0105 1.10.2.2 CYTB ubiquinol-cytochrome c reductase cytochrome b subunit YOR065 W 1.10.2.2 CYTI ubiquinol-cytochrome c reductase cytochrome cl subunit YBL045C 1.10.2.2 COR1 ubiquinol-cytochrome c reductase core subunit 1 YPR191 W 1.10.2.2 QCRI ubiquinol-cytochrome c reductase core subunit 2 YPR191W 1.10.2.2 QCR2 ubiquinol-cytochrome c reductase YFR033C 1.10.2.2 QCR6 ubiquinol-cytochrome c reductase subunit 6 YDR529C 1.10.2.2 QCR7 ubiquinol-cytochrome c reductase subunit 7 YJL166W 1.10.2.2 QCR8 ubiquinol-cytochrome c reductase subunit 8 YGR183C 1.10.2.2 QCR9 ubiquinol-cytochrome c reductase subunit 9 YHROO1 W- 1.10.2.2 QCR10 ubiquinol-cytochrome c reductase subunit 10 A
Electron Transport System, Complex IV
Q0045 1.9.3.1 COXI cytochrome c oxidase subunit I QH2m+ 2 FERIm + 1.5 Hm -> Qm + 2 FEROm cytr Q0250 1.9.3.1 COX2 cytochrome c oxidase subunit I
Q0275 1.9.3.1 COX3 cytochrome c oxidase subunit I
YDL067C 1.9.3.1 COX9 cytochrome c oxidase subunit I
YGL187C 1.9.3.1 COX4 cytochrome c oxidase subunit I
YGL191W 1.9.3.1 COX13 cytochrome c oxidase subunit I
YHR051 W 1.9.3.1 COX6 cytochrome c oxidase subunit I
Y1LI 1 I W 1.9.3.1 COXSB cytochrome c oxidase subunit I
YLR038C 1.9.3.1 COX12 cytochrome c oxidase subunit I
YLR395C 1.9.3.1 COX8 cytochrome c oxidase subunit I
YMR256C 1.9.3.1 COX7 cytochrome c oxidase subunit I
YNL052W 1.9.3.1 COXSA cytochrome c oxidase subunit I
ATP Synthase YBL099W 3.6.1.34 ATP1 F1FO-ATPase complex, 171 alpha subunit ADPm+Plm->ATPm+3Hm atpl YPL271W 3.6.1.34 ATP15 F1FO-ATPase complex, F1 epsilon subunit YDLO04W 3.6.1.34 ATP 16 F-type H+-transporting ATPase delta chain Q0085 3.6.1.34 ATP6 FIFO-ATPase complex, FO A subunit YBR039W 3.6.1.34 ATP3 FIFO-ATPase complex, F1 gamma subunit YBR127C 3.6.1.34 VMA2 H+-ATPase V 1 domain 60 KD subunit, vacuolar YPL078C 3.6.1.34 ATP4 FIFO-ATPase complex, Fl delta subunit YDR298C 3.6.1.34 ATPS FIFO-ATPase complex, OSCP subunit YDR377W 3.6.1.34 ATP 17 ATP synthase complex, subunit f YJR121 W 3.6.1.34 ATP2 FIFO-ATPase complex, Fl beta subunit YKLO16C 3.6.1.34 ATP7 F1FO-ATPase complex, FO D subunit YLR295C 3.6.1.34 ATP 14 ATP synthase subunit h Q0080 3.6.1.34 ATP8 F-type H+-transporting ATPase subunit 8 Q0130 3.6.1.34 ATP9 F-type H+-transporting ATPase subunit c YOL077W- 3.6.1.34 ATP19 ATP synthase k chain, mitochondrial A
YPR020W 3.6.1.34 ATP20 subunit G of the dimeric form of mitochondria) FIFO-ATP synthase YLR447C 3.6.1.34 VMA6 V-type H+-transporting ATPase subunit AC39 YGR020C 3.6.1.34 VMA7 V-type H+-transporting ATPase subunit F
YKL080W 3.6.1.34 VMAS V-type H+-transporting ATPase subunit C
YDL185W 3.6.1.34 TFPI V-type H+-transporting ATPase subunit A
YBR127C 3.6.1.34 VMA2 V-type H+-transporting ATPase subunit B
YOR332W 3.6.1.34 VMA4 V-type H+-transporting ATPase subunit E
YEL027W 3.6.1.34 CUPS V-type H+-transporting ATPase proteolipid subunit YHR026W 3.6.1.34 PPAI V-type H+-transporting ATPase proteolipid subunit YPL234C 3.6.1.34 TFP3 V-type H+-transporting ATPase proteolipid subunit YMR054W 3.6.1.34 STV I V-type H+-transporting ATPase subunit I
YOR270C 3.6.1.34 VPH1 V-type H+-transporting ATPase subunit I
YEL051W 3.6.1.34 VMA8 V-type H+-transporting ATPase subunit D
YHR039C-A 3.6.1.34 VMAIO vacuolar ATP synthase subunit G
YPR036W 3.6.1.34 VMA13 V-type H+-transporting ATPase 54 kD subunit Electron Transport System, Complex IV
Q0045 1.9.3.1 COX1 cytochrome-c oxidase subunit I 4 FEROm + 02m + 6 Hm -> 4 FERIm cox) Q0275 1.9.3.1 COX3 Cytochrome-c oxidase subunit III, mitochondrially-coded Q0250 1.9.3.1 COX2 cytochrome-c oxidase subunit II
YDL067C 1.9.3.1 COX9 Cytochrome-c oxidase YGL187C 1.9.3.1 COX4 cytochrome-c oxidase chain IV
YOL191W 1.9.3.1 COX13 cytochrome-c oxidase chain Vla YHRO51 W 1.9.3.1 COX6 cytochrome-c oxidase subunit VI

YLR395C 1.9.3.1 COX8 cytochrome-c oxidase chain VIII
YMR256C 1.9.3.1 COX7 cytochrome-c oxidase, subunit VII
YNLO52W 1.9.3.1 COX5A cytochrome-c oxidase chain V.A precursor YML054C 1.1.2.3 cyb2 Lactic acid dehydrogenase 2 FERIm + LLACm -> PYRm + 2 FEROm cyb2 YDL174C 1.1.2.4 DLDI mitochondrial enzyme D-Lactate ferricytochrome c 2 FERIna + LACm -> PYRm + 2 FEROm dldl oxidoreductase Methane metabolism YPL275W 1.2.1.2 YPL275 putative formate dehydrogenase/putative pseudogene FOR+NAD->C02+NADH tfola W
YPL276W 1.2.1.2 YPL276 putative formate dehydrogenase/putative pseudogene FOR+NAD->C02+NADH tfolb W
YOR388C 1.2.1.2 FDH1 Protein with similarity to formate dehydrogenases FOR +
NAD -> C02 + NADH fdhl Nitrogen metabolism YBR208C 6.3.4.6 DURI urea amidolyase containing urea carboxylase / ATP + UREA
+ C02 <-> ADP + PI+UREAC durl allophanate hydrolase YBR208C 3.5.1.54 DUR! Allophanate hydrolase UREAC -> 2 NH3 + 2 C02 dur2 YJL126W 3.5.5.1 NIT2 nitrilase ACNL->INAC+NH3 nit2 Sulfur metabolism (Cystein biosynthesis maybe) YJR137C 1.8.7.1 ECM17 Sulfite reductase H2SO3 + 3 NADPH <-> H2S + 3 NADP ecml7 Lipid Metabolism Fatty acid biosynthesis YER015W 6.2.1.3 FAA2 Long-chain-fatty-acid--CoA ligase,Acyl-CoA ATP + LCCA +
COA <-> AMP + PPI + ACOA faa2 synthetase YIL009W 6.2.1.3 FAA3 Long-chain-fatty-acid--CoA ligase,Acyl-CoA ATP + LCCA +
COA <-> AMP + PPI + ACOA faa3 synthetase YOR317 W 6.2.1.3 FAA1 Long-chain-fatty-acid--CoA ligase, Acyl-CoA ATP + LCCA +
COA <-> AMP + PPI + ACOA faal synthetase YMR246W 6.2.1.3 FAA4 Acyl-CoA synthase (long-chain fatty acid CoA ligase); ATP
+ LCCA + COA <-> AMP + PPI+ ACOA faa4 contributes to activation of imported myristate YKRO09C 1.1.1: FOX2 3-Hydroxyacyl-CoA dehydrogenase HACOA + NAD <-> OACOA +
NADH fox2b YIL160C 2.3.1.16 pot! 3-Ketoacyl-CoAthiolase OACOA + COA -> ACOA + ACCOA
potl_l YPL028W 2.3.1.9 erg10 Acetyl-CoA C-acetyltransferase, ACETOACETYL- 2A000A<->COA+AACCOA ergl0_1 COA THIOLASE
YPL028W 2.3.1.9 erg 10 Acetyl-CoA C-acetyltransferase, ACETOACETYL- 2 ACCOAm <-> COAm+AACCOAm erg 10_2 COA THIOLASE (mitoch) Fatty Acids Metabolism Mitochondrial type II fatty acid synthase YKL192C 1.6.5.3 ACP! Acyl carrier protein, component of mitochondrial type II
NADHm + Qm -> NADm + QH2m ACPI
fatty acid synthase YER061C - CEM1 Beta-ketoacyl-ACP synthase, mitochondria! (3-oxoacyl-[Acyl-carrier-protein] synthase) YOR221 C - MCTI Malonyl CoA:acyl carrier protein transferase YKL055C - OAR1 3-Oxoacyl-[acyl-carrier-protein] reductase YKL192C/Y 1.6.5.3/- ACPI/CE Type II fatty acid synthase ACACPm + 4 MALACPm + 8 NADPHm -> 8 TypeIi_1 ER061C/YO /-/- MI/MCT NADPm+CI00ACPm+4C02m+4ACPm YKL192C/Y 1.6.5.3/- ACP 1/CE Type II fatty acid synthase ACACPm + 5 MALACPm +
10 NADPHm -> 10 TypeIl_2 ER061C/YO /-I- MI/MCT NADPm + C120ACPm + 5 C02m + 5 ACPm R221C/YKL 1/OAR!

YKL192C/Y 1.6.5.3/- ACPI/CE Type II fatty acid synthase ACACPm + 6 MALACPm +
12 NADPHm -> 12 TypeIl_3 ER061C/YO /-/- MI/MCT NADPm + C140ACPm + 6 C02m + 6 ACPm YKLI92C/Y 1.6.5.3/- ACPI/CE Type II fatty acid synthase ACACPm + 6 MALACPm+ 11 NADPHm -> I 1 TypeII_4 ER061CIYO /-/- M1/MCT NADPm + C141ACPm+ 6 C02m + 6 ACPm R221ClYKL 1/OARI

YKL192C/Y 1.6.5.3/- ACPI/CE Type II fatty acid synthase ACACPm + 7 MALACPm +
14 NADPHm -> 14 Type!! 5 ERO61ClYO /-/- Ml/MCT NADPm + C 1 60ACPm + 7 C02m + 7 ACPm R221C/YKL 1/OAR!

YKL192C/Y 1.6.5.3/- ACP I/CE Type II fatty acid synthase ACACPm + 7 MALACPm +
13 NADPHm -> 13 TypelI 6 ERO61ClYO /-/- Ml/MCT NADPm+C161ACPm+7CO2m +7ACPm YKL192C/Y 1.6.5.3/- ACPI/CE Type II fatty acid synthase ACACPm + 8 MALACPm +
16 NADPHm -> 16 TypeIl_7 ERO61ClYO /-/- Ml/MCT NADPm+Cl80ACPm+8C02m+8ACPm YKL192CfY 1.6.5.3/- ACP 1/CE Type II fatty acid synthase ACACPm + 8 MALACPm +
15 NADPHm -> 15 Typell8 ER061C/YO /-/- Ml/MCT NADPm+C18IACPm+8CO2m+8ACPm R221ClYKL 1/OARI

YKL192C/Y 1.6.5.3/- ACP 1/CE Type II fatty acid synthase ACACPm + 8 MALACPm +
14 NADPHm -> 14 Typell_9 ER061CIYO /-/- Ml/MCT NADPm+C182ACPm+8C02m+8ACPm R221C/YKL 1/OAR!

Cytosolic fatty acid synthesis YNRO16C 6.4.1.2 ACC I acetyl-CoA carboxylase (ACC) / biotin carboxylase ACCOA
+ ATP + C02 <-> MALCOA + ADP + PI accl 6.3.4.14 YKL182w 4.2.1.61; fast fatty-acyl-CoA synthase, beta chain MALCOA + ACP <->
MALACP + COA fasl_1 1.3.1.9;2.
3.1.38;2.
3.1.39;3.
1.2.14;2.
3.1.86 YPL231w 2.3.1.85; FAS2 fatty-acyl-CoA synthase, alpha chain 1.1.1.100 ;2.3.1.41 YKL182w 4.2.1.61; fall fatty-acyl-CoA synthase, beta chain ACCOA+ ACP <->
ACACP + COA fasl_2 1.3.1.9;2.
3.1.38;2.
3.1.39;3.
1.2.14;2.
3.1.86 YER06IC 2.3.1.41 CEMI 3-Oxoacyl-[acyl-carrier-protein] synthase MALACPm +
ACACPm -> ACPm + C02m + ceml 30ACPm YGR037C/Y 6.4.1.2; ACB 1/A b-Ketoacyl-ACP synthase (C10,0), fatty acyl CoA
ACACP + 4 MALACP + 8 NADPH -> 8 NADP + ciOOsn NRO16C/YK 6.3.4.1;4 CCI/fall/ synthase C100ACP + 4 C02 + 4 ACP
L182W/YPL 2.3.1.85; FAS2/
231w 1.1.1.100 ;2.3.1.41;
4.2.1.61 YGR037C/Y 6.4.1.2; ACB1/A b-Ketoacyl-ACP synthase (C12,O), fatty acyl CoA
ACACP + 5 MALACP + 10 NADPH -> 10 NADP + c120sn NRO16C/YK 6.3.4.1;4 CC1/fall/ synthase C120ACP + 5 C02 + 5 ACP
L182W/YPL 2.3.1.85; FAS2/
231w 1.1.1.100 ;2.3.1.41;
4.2.1.61 YGRO37CfY 6.4.1.2; ACB I/A b-Ketoacyl-ACP synthase (C14,0) ACACP + 6 MALACP +
12 NADPH -> 12 NADP + cl40sn NRO16C/YK 6.3.4.1;4 CCI/fasl/ C140ACP + 6 C02 + 6 ACP
L182W/YPL 2.3.1.85; FAS2/
231w 1.1.1.100 ;2.3.1.41;
4.2.1.61 YGR037C/Y 6.4.1.2; ACB 1/A b-Ketoacyl-ACP synthase I (C 14,1) ACACP + 6 MALACP
+ I I NADPH ->11 NADP + c l41 sy NROI6C/YK 6.3.4.1;4 CCl/fall/ C141ACP + 6 C02 + 6 ACP
L182W/YPL 2.3.1.85; FAS2/
231w 1.1.1.100 ;2.3.1.41;
4.2.1.61 YGR037C/Y 6.4.1.2; ACB 1/A b-Ketoacyl-ACP synthase I (C16,0) ACACP + 7 MALACP
+ 14 NADPH -> 14 NADP + cl60sn NRO16C/YK 6.3.4.1;4 CCI/fasl/ C160ACP + 7 C02 + 7 ACP
L182W/YPL 2.3.1.85; FAS2/
231w 1.1.1.100 ;2.3.1.41;
4.2.1.61 YGR037C/Y 6.4.1.2; ACB 1/A b-Ketoacyl-ACP synthase I (C16,1) ACACP + 7 MALACP
+ 13 NADPH -> 13 NADP + cl6lsy NRO16C/YK 6.3.4.1;4 CCl/fall/ C161ACP+7C02 +7ACP
L182W/YPL 2.3.1.85; FAS2/
231w 1.1.1.100 ;2.3.1.41;
4.2.1.61 YGR037C/Y 6.4.1.2; ACB 1/A b-Ketoacyl-ACP synthase I (C 18,0) ACACP + 8 MALACP
+ 16 NADPH -> 16 NADP + c 180sy NRO16C/YK 6.3.4.1;4 CC1/fasl/ C 180ACP + 8 C02 + 8 ACP
L182W/YPL 2.3.1.85; FAS2/
231w 1.1.1.100 ;2.3.1.41;
4.2.1.61 YGR037C/Y 6.4.1.2; ACB 1/A b-Ketoacyl-ACP synthase I (C18,1) ACACP + 8 MALACP
+ 15 NADPH ->15 NADP + cl81 sy NRO16C/YK 6.3.4.1;4 CCl/fasl/ C181ACP+8 C02+8ACP
L182W/YPL 2.3.1.85; FAS2/
231w 1.1.1.100 ;2.3.1.41;
4.2.1.61 YGR037C/Y 6.4.1.2; ACB 1/A b-Ketoacyl-ACP synthase I (C18,2) ACACP + 8 MALACP
+ 14 NADPH -> 14 NADP + cl82sy NRO16C/YK 6.3.4.1;4 CCI/fasl/ C 182ACP + 8 C02 + 8 ACP
L182W/YPL 2.3.1.85; FAS2/
231w 1.1.1.100 ;2.3.1.41;
4.2.1.61 YKLI82W 4.2.1.61 fall 3-hydroxypalmitoyl-[acyl-carrier protein] dehydratase 3HPACP <-> 2HDACP fasl3 YKL182W 1.3.1.9 fasl Enoyl-ACP reductase AACP+NAD <-> 23DAACP+NADH fasl_4 Fatty acid degradation YGL205W/Y 1.3.3.6/2. POXI/FO Fatty acid degradation C140+ATP+7COA+7FADm+7NAD->AMP cl40dg KRO09C/YIL 3.1.18 X2/POT3 + PPI + 7 FADH2m + 7 NADH + 7 ACCOA

YGL205 W/Y 1.3.3.6/2. POXI/FO Fatty acid degradation C160 + ATP + 8 COA + 8 FADm + 8 NAD -> AMP cl6Odg KRO09C/YIL 3.1.18 X2/POT3 + PPI+ 8 FADH2m + 8 NADH + 8 ACCOA

YGL205W/Y 1.3.3.6/2. POXI/FO Fatty acid degradation C180+ATP+9COA +9FADm+9NAD->AMP cl80dg KRO09C/YIL 3.1.18 X2/POT3 +PPI+9FADH2m+9NADH+9A000A

Phosphollpid Biosyntheses Glycerol-3-phosphate acyltransferase GL3P + 0.0 17 C100ACP + 0.062 C120ACP +
0.1 Gatl_1 C140ACP + 0.27 C160ACP + 0.169 C161ACP +
0.055 C180ACP + 0.235 C181ACP+0.093 C 182ACP -> AGL3P +ACP
Glycerol-3-phosphate acyltransferase GL3P + 0.0 17 C100ACP + 0.062 C120ACP +
0.1 Gat2_1 C140ACP + 0.27 C160ACP + 0.169 C16IACP +
0.055 CISOACP + 0.235 C181ACP + 0.093 C 182ACP -> AGL3P + ACP
Glycerol-3-phosphate acyltransferase T3P2 + 0.017 C100ACP + 0.062 C120ACP+0.1 Gatl_2 C 140ACP + 0.27 C160ACP + 0.169 C161ACP +
0.055 C180ACP + 0.235 C181ACP + 0.093 C182ACP -> AT3P2 + ACP
Glycerol-3-phosphate acyltransferase T3P2 + 0.017 C100ACP + 0.062 C120ACP +
0.1 Gat2_2 C 140ACP + 0.27 C 160ACP + 0.169 C161ACP +
0.055 CISOACP + 0.235 C181ACP+0.093 C 182ACP -> AT3P2 + ACP
Acyldihydroxyacetonephosphate reductase AT3P2 + NADPH -> AGL3P + NADP ADHAPR
YDLO52C 2.3.1.51 SLC1 1-Acylglycerol-3-phosphate acyltransferase AGL3P + 0.017 C100ACP + 0.062 C120ACP + 0.100 slcl C 140ACP + 0.270 C160ACP + 0.169 C161ACP +
0.055 C180ACP+0.235 C181ACP + 0.093 C182ACP -> PA+ACP
2,3.1.51 - 1-Acylglycerol-3-phosphate acyltransferase AGL3P + 0.017 C100ACP +
0.062 C120ACP + 0.100 AGAT
C140ACP + 0.270 C160ACP + 0.169 C161ACP +
0.055 C180ACP + 0.235 C I81ACP + 0.093 C 182ACP -> PA + ACP
YBR029C 2.7.7.41 CDS1 CDP-Diacylglycerol synthetase PAm+CTPm<->CDPDGm+PPIm cdsla YBR029C 2.7.7.41 CDS1 CDP-Diacylglycerol synthetase PA + CTP <-> CDPDG + PPI
cdslb YER026C 2.7.8.8 chol phosphatidylserine synthase CDPDG + SER <-> CMP + PS
chola YER026C 2.7.8.8 chol Phosphatidylserine synthase CDPDGm + SERm <-> CMPm + PSm cholb YGR170W 4.1.1.65 PSD2 phosphatidylserine decarboxylase located in vacuole or PS -> PE+ C02 psd2 Golgi YNL169C 4.1.1.65 PSDI PhosphatidylserineDecarboxylase1 PSm->PEm+C02m psdl YGRI57W 2.1.1.17 CH02 Phosphatidylethanolamine N-methyltransferase SAM + PE ->
SAH + PMME cho2 YJR073C 2.1.1.16 OPI3 Methylene-fatty-acyl-phospholipid synthase. SAM + PMME -> SAH + PDME opi3 1 YJR073C 2.1.1.16 OPI3 Phosphatidyl-N-methylethanolamine N- PDME + SAM -> PC +
SAH opi3_2 methyltransferase YLR133W 2.7.1.32 CKII Cholinekinase ATP + CHO -> ADP + PCHO ckil YGR202C 2.7.7.15 PCT1 Cholinephosphate cytidylyltransferase PCHO + CTP ->
CDPCHO + PPI pctl YNL130C 2.7.8.2 CPTI Diacylglycerolcholinephosphotransferase CDPCHO + DAGLY ->
PC + CMP cptl YDR147W 2.7.1.82 EKII Ethanolamine kinase ATP + ETHM -> ADP + PETHM ekil YGRO07W 2.7.7.14 MUQI Phosphoethanolaminecytidylyltransferase PETHM + CTP ->
CDPETN + PPI ectl YHR123 W 2.7.8.1 EPTI Ethanolaminephosphotransferase. CDPETN + DAGLY <-> CMP +
PE eptl YJL153C 5.5.1.4 tool myo-Inositol-l-phosphate synthase G6P->MI1P inol YHR046C 3.1.3.25 INMl myo-Inositol-l(or 4)-monophosphatase MI!? -> MYOI + PT
impal YPRI 13W 2.7.8.11 PIS1 phosphatidylinositol synthase CDPDG + MYOI -> CMP +
PINS pisl YJR066W 2.7.1.137 tort I-Phosphatidylinositol 3-kinase ATP + PINS -> ADP +
PINSP torl YKL203C 2.7.1.137 tort 1-Phosphatidylinositol 3-kinase ATP + PINS -> ADP +
PINSP tort YLR240W 2.7.1.137 vps34 1-Phosphatidylinositol 3-kinase ATP + PINS -> ADP +
PINSP vps34 YNL267W 2.7.1.67 PIK1 Phosphatidylinositol 4-kinase (PI 4-kinase), generates ATP + PINS -> ADP + PINS4P pikl Ptdlns 4-P
YLR305C 2.7.1.67 STT4 Phosphatidylinositol 4-kinase ATP + PINS -> ADP + PINS4P
sst4 YFRO19W 2.7.1.68 FAB1 PROBABLE PHOSPHATIDYLINOSITOL-4- PINS4P+ATP->D45PI+ADP
fabI
PHOSPHATE 5-KINASE, I-phosphatidylinositol-4-phosphate kinase YDR208 W 2.7.1.68 MSS4 Phosphatidylinositol-4-phosphate 5-kinase; required for PINS4P + ATP -> D45PI + ADP mss4 proper organization of the actin cytoskeleton YPL268W 3.1.4.11 plcl 1-phosphatidylinositol-4,5-bisphosphate D45PI -> TPI +
DAGLY plcl phosphodiesterase YCLO04W 2.7.8.8 PGS1 CDP-diacylglycerol-serine0-phosphatidyltransferase CDPDGm+GL3Pm<_>CMPm+PGPm pgsl 3.1.3.27 Phosphatidylglycerol phosphate phosphatase A PGPm -> Plan + PGm pgpa YDL142C 2.7.8.5 CRD1 Cardiolipin synthase CDPDGm + PGm -> CMPm + CLm crdI
YDR284C DPP1 diacylglycerol pyrophosphate phosphatase PA -> DAGLY + PI dppl YDR503C LPP 1 lipid phosphate phosphatase DGPP -> PA + PI Ippi Sphingoglycolipid Metabolism YDR062W 2.3.1.50 LCB2 Serine C-palmitoyltransferase PALCOA + SER -> COA +
DHSPH + C02 lcb2 YMR296C 2.3.1.50 LCB1 Serine C-palmitoyltransferase PALCOA + SER -> COA +
DHSPH + C02 lcbl YBR265w 1.1.1.102 TSC 10 3-Dehydrosphinganine reductase DHSPH + NADPH -> SPH +
NADP tsclo YDR297W SUR2 SYRINGOMYCIN RESPONSE PROTEIN 2 SPH + 02 + NADPH -> PSPH + NADP
sur2 Ceramide synthase PSPH + C26000A -> CER2 + COA esyna Ceramide synthase PSPH + C24000A -> CER2 + COA esynb YMR272C SCS7 Ceramide hydroxylase that hydroxylates the C-26 fatty- CER2 +
NADPH + 02 -> CER3 +NADP scs7 acyl moiety of Inositol-phosphorylceramide YKLO04W AURI IPS synthase, AUREOBASIDIN A RESISTANCE CER3 + PINS -> IPC curl PROTEIN
YBR036C CSG2 Protein required for synthesis of the mannosylated IPC + GDPMAN -> MIPC csg2 sphingolipids YPLO57C SURI Protein required for synthesis of the mannosylated IPC + GDPMAN -> MIPC surl sphingolipids YDR072C 2.-.-.- IPTI MIP2C synthase, MANNOSYL MIPC + PINS -> MIP2C iptl DIPHOSPHORYLINOSITOL CERAMIDE
SYNTHASE
YOR171C LCB4 Long chain base kinase, involved in sphingolipid SPH + ATP ->
DHSP + ADP lcb4_1 metabolism YLR260W LCB5 Long chain base kinase, involved in sphingolipid SPH +ATP -> DHSP
+ ADP lcb5_l metabolism YOR171C LCB4 Long chain base kinase, involved in sphingolipid PSPH +ATP ->
PHSP + ADP lcb4 2 metabolism YLR260W LCB5 Long chain base kinase, involved in sphingolipid PSPH + ATP ->
PHSP + ADP lcb5_2 metabolism YJL134W LCB3 Sphingoid base-phosphate phosphatase, putative DHSP -> SPH+PI
lcb3 regulator of sphingolipid metabolism and stress response YKRO53C YSR3 Sphingoid base-phosphate phosphatase, putative DHSP -> SPH+PI
ysr3 regulator of sphingolipid metabolism and stress response YDR294C DPL1 Dihydrosphingosine- I -phosphate lyase DHSP -> PETHM + C16A dpll Sterol biosynthesis YMLI26C 4.1.3.5 HMGS 3-hydroxy-3-methylglutaryl coenzyme A synthase H3MCOA +
COA <-> ACCOA + AACCOA brags YLR45OW 1.1.1.34 hmg2 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) MVL +
COA + 2 NADP <-> H3MCOA + 2 NADPH hmg2 reductase isozyme YML075C 1.1.1.34 hmgl 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) MVL+COA+2NADP<->H3MCOA+2NADPH hmg!
reductase isozyme YMR208W 2.7.1.36 ergl2 mevalonatekinase ATP+MVL->ADP+PMVL ergl2_1 YMR208W 2.7.1.36 ergl2 mevalonate kinase CTP + MVL -> CDP + PMVL ergl2_2 YMR208W 2.7.1.36 ergl2 mevalonate kinase GTP+MVL->GDP+PMVL erg 123 YMR208W 2.7.1.36 ergl2 mevalonate kinase UTP + MVL -> UDP + PMVL ergl2_4 YMR220W 2.7.4.2 ERG8 48kDaPhosphomevalonatekinase ATP + PMVL -> ADP + PPMVL
erg8 YNR043W 4.1.1.33 MVD1 Diphosphomevalonatedecarboxylase ATP + PPMVL -> ADP + PI
+ IPPP + C02 mvdl YPL117C 5.3.3.2 idil Isopentenyl diphosphate:dimethylallyl diphosphate IPPP <-> DMPP idil isomerase (IPP isomerase) YJL167W 2.5.1.1 ERG20 prenyltransferase DMPP+IPPP->GPP+PPI erg20_1 YJL167W 2.5.1.10 ERG20 Famesyl diphosphate synthetase (FPP synthetase) GPP+IPPP -> FPP + PPI erg20_2 YHR190W 2.5.1.21 ERG9 Squalene synthase. 2 FPP + NADPH -> NADP + SQL erg9 YGR175C 1.14.99.7 ERG1 Squalene monooxygenase SQL + 02 + NADP -> S23E + NADPH
ergl YHR072W 5.4.99.7 ERG7 2,3-oxidosqualene-lanosterolcyclase S23E->LNST erg7 YHR007c 1.14.14.1 ergi1 cytochromeP450lanosterol14a-demethylase LNST + RFP +
02 -> IGST + OFP ergll_l YNL280c ERG24 C-14 sterol reductase IGST + NADPH -> DMZYMST + NADP erg24 YGR060w ERG25 C-4 sterol methyl oxidase 3 02 + DMZYMST -> IMZYMST erg25_1 YGLO01c 5.3.3.1 ERG26 C-3 sterol dehydrogenase (C-4 decarboxylase) IMZYMST->IIMZYMST+C02 erg26_1 YLR100C YLR100 C-3 sterol keto reductase IIMZYMST + NADPH -> MZYMST + NADP
erg! l_2 C
YGR060w ERG25 C-4 sterol methyl oxidase 3 02 + MZYMST -> IZYMST erg25_2 YGLOOIc 5.3.3.1 ERG26 C-3 sterol dehydrogenase (C-4 decarboxylase) IZYMST ->
IIZYMST+ C02 erg26_2 YLRIOOC YLR100 C-3 sterol keto reductase IIZYMST + NADPH -> ZYMST + NADP
ergl1_3 C
YML008c 2.1.1.41 erg6 S-adenosyl-methionine delta-24-sterol-c- ZYMST+ SAM ->
FEST + SAH erg6 methyltransferase YMR202W ERG2 C-8 sterol isomerase FEST -> EPST erg2 YLR056w ERG3 C-5 sterol desaturase EPST+ 02 +NADPH -> NADP + ERTROL erg3 YMR015c 1.14.14: ERGS C-22 sterol desaturase ERTROL + 02 + NADPH -> NADP +
ERTEOL ergs YGLO12w ERG4 sterol C-24 reductase ERTEOL+NADPH->ERGOST+NADP erg4 LNST + 3 02 + 4 NADPH + NAD -> MZYMST + unkrxn3 C02 + 4 NADP +NADH
MZYMST+302+4 NADPH + NAD -> ZYMST + unkrxn4 C02 + 4 NADP + NADH

5.3.3.5 Cholestenol delta-isomerase ZYMST + SAM -> ERGOST + SAH cdisoa Nucleotide Metabolism Histidine Biosynthesis YOL061 W 2.7.6.1 PRS5 ribose-phosphate pyrophosphokinase R5P + ATP <-> PRPP +
AMP prs5 YBL068W 2.7.6.1 PRS4 ribose-phosphate pyrophosphokinase 4 R5P + ATP <-> PRPP +
AMP prs4 YER099C 2.7.6.1 PRS2 ribose-phosphate pyrophosphokinase 2 R51? + ATP <-> PRPP
+AMP prs2 YHLOI IC 2.7.6.1 PRS3 ribose-phosphate pyrophosphokinase 3 R5P + ATP <-> PRPP
+ AMP prs3 YKLI81W 2.7.6.1 PRS1 ribose-phosphate pyrophosphokinase R5P + ATP <-> PRPP +
AMP prsl YIR027C 3.5.2.5 dall allantoinase ATN <-> ATT dall YIR029W 3.5.3.4 dal2 allantoicase ATT<->UGC+UREA dal2 YIR032C 3.5.3.19 dal3 ureidoglycolate hydrolase UGC <-> GLX + 2 NH3 + C02 dal3 Purine metabolism YJL005W 4.6.1.1 CYRI adenylate cyclase ATP -> cAMP + PPI cyrl YDR454C 2.7.4.8 GUK1 guanylate kinase GMP + ATP <-> GDP + ADP gukl_1 YDR454C 2.7.4.8 GUK1 guanylate kinase DGMP+ATP<->DGDP+ADP gukl_2 YDR454C 2.7.4.8 GUKI guanylate kinase GMP + DATP <-> GDP + DADP gukl_3 YMR300C 2.4.2.14 ade4 phosphoribosylpyrophosphate amidotransferase PRPP + GLN -> PPI+ GLU + PRAM ade4 YGL234W 6.3.4.13 ade5,7 glycinamide ribotide synthetase and aminoimidazole PRAM + ATP + GLY <-> ADP + PI+ GAR ade5 ribotide synthetase YDR408C 2.1.2.2 ade8 glycinamide ribotide transformylase OAR+FTHF->THF+FGAR
ade8 YGR061 C 6.3.5.3 ade6 5'-phosphoribosylformyl glycinamidine synthetase FGAR +
ATP + GLN -> GLU + ADP + PI+ FOAM ade6 YGL234W 6.3.3.1 ade5,7 Phosphoribosylfonnylglycinamidecyclo-ligase FGAM + ATP -> ADP + PI + AIR ade7 YOR128C 4.1.1.21 ade2 phosphoribosylamino-imidazole-carboxylase CAIR <-> AIR +
C02 ade2 YAR015 W 6.3.2.6 adeI phosphoribosyl amino imidazolesuccinocarbozamide CAIR +
ATP + ASP <-> ADP + PI + SAICAR adel synthetase YLR359W 4.3.2.2 ADE13 5'-Phosphoribosyl-4-(N-succinocarboxamide)-5- SAICAR<->FUM+AICAR ade13-1 aminoimidazole lyase YLR028C 2.1.2.3 ADE16 5-aminoimidazole-4-carboxamideribonucleotide AICAR+FTHF<->THF+PRFICA adel6 I
(AICAR) transformylaseViMP cyclohydrolase YMR120C 2.1.2.3 ADE17 5-aminoimidazole-4-carboxamide ribonucleotide AICAR +
FTHF <-> THF + PRFICA adel7_l (AICAR) transformylaseVlMP cyclohydrolase YLRO28C 3.5.4.10 ADE16 5-aminoimidazole-4-carboxamide ribonucleotide PRFICA <-> IMP adel6 2 (AICAR) transfonnylaseVIMP cyclohydrolase YMR120C 2.1.2.3 ADE17 IMP cyclohydrolase PRFICA <-> IMP ade17 2 YNL220W 6.3.4.4 ade12 adenylosuccinate synthetase IMP + GTP + ASP -> GDP + PI
+ ASUC ade12 YLR359W 4.3.2.2 ADE13 AdenylosuccinateLyase ASUC <-> FUM + AMP ade13_2 YAR073W 1.1.1.205 fun63 putative inosine-5'-monophosphatedehydrogenase IMP +
NAD -> NADH + XMP fun63 YHR216W 1.1.1.205 pur5 purine excretion IMP+NAD->NADH+XMP pur5 YML056C 1.1.1.205 IMD4 probable inosine-5'-monophosphate dehydrogenase IMP +
NAD -> NADH + XMP prm5 (IMP
YLR432W 1.1.1.205 IMD3 probable inosine-5'-monophosphate dehydrogenase IMP +
NAD -> NADH + XMP prm4 (IMP
YAR075 W 1.1.1.205 YAR075 Protein with strong similarity to inosine-5'- IMP +
NAD -> NADH + XMP prm6 W monophosphate dehydrogenase, frameshifted from YAR073W, possible pseudogene YMR217W 6.3.5.2, GUAI GMP synthase XMP+ATP+GLN->GLU+AMP+PPI+GMP gual 6.3.4.1 YML035C 3.5.4.6 amdl AMP deaminase AMP->IMP+NH3 amdl YGL248W 3.1.4.17 PDEI 3',5'-Cyclic-nucleotide phosplodiesterase, low affinity cAMP -> AMP pdel YOR360C 3.1.4.17 pde2 3',5'-Cyclic-nucleotide phosphodiesterase, high affinity cAMP -> AMP pde2_1 YOR360C 3.1.4.17 pde2 cdAMP -> DAMP pde2_2 YOR360C 3.1.4.17 pde2 GIMP -> IMP pde2_3 YOR360C 3.1.4.17 pde2 cGMP -> GMP pde2_4 YOR360C 3.1.4.17 pde2 cCMP -> CMP pde2_5 YDR530C 2.7.7.53 APA2 5',5"'-P-1,P-4-tetraphosphate phosphorylase II ADP + ATP
-> PI+ ATRP apa2 YCL050C 2.7.7.53 apal 5',5"'-P-1,P-4-tetraphosphate phosphorylase II ADP + GTP
-> PI + ATRP apal_1 YCL050C 2.7.7.53 apal 5',5"'-P-1,P-4-tetraphosphate phosphorylase II GDP + GTP
-> PI+ GTRP apal_3 Pyrimidine metabolism YJL130C 2.1.3.2 ura2 Aspartate-carbamoyltransferase CAP + ASP -> CAASP + PI
ura2_l YLR420W 3.5.2.3 ura4 dihydrooratase CAASP <-> DOROA um4 YKL216W 1.3.3.1 ural dihydroorotatedehydrogenase DOROA+02<-> H202 + OROA
ural_1 YKL216W 1.3.3.1 PYRD Dihydroorotate dehydrogenase DOROA + Qm <-> QH2m + OROA
ural_2 YML106W 2.4.2.10 URA5 Orotate phosphoribosyltransferase l OROA + PRPP <-> PPI+
OMP um5 YMR271C 2.4.2.10 URA10 Orotate phosphoribosyltransferase 2 OROA + PRPP <-> PPI
+ OMP uralO
YEL021 W 4.1.1.23 ura3 orotidine-5'-phosphate decarboxylase OMP -> C02 +UMP
ura3 YKL024C 2.7.4.14 URA6 Nucleoside-phosphate kinase ATP + UMP <-> ADP + UDP npk YHR128W 2.4.2.9 furl UPRTase, Uracil phosphoribosyltransferase URA + PRPP ->
UMP + PPI furl YPR062W 3.5.4.1 FCYI cytosine deaminase CYTS->URA+NH3 fcyl 2.7.1.21 Thymidine (deoxyuridine) kinase DU + ATP -> DUMP + ADP tdkl 2.7.1.21 Thymidine (deoxyuridine) kinase DT + ATP -> ADP + DTMP tdk2 YNRO12W 2.7.1.48 URKI Uridinekinase URI + GTP -> UMP + GDP urkl_1 YNRO12W 2.7.1.48 URKI Cytodine kinase CYTD + GTP -> GDP+CMP urkl_2 YNRO12W 2.7.1.48 URK1 Uridine kinase, converts ATP and uridine to ADP and URI
+ ATP -> ADP + UMP urkl-3 UMP
YLR209C 2.4.2.4 PNPI Protein with similarity to human purine nucleoside DU +
PI <-> URA+ DRIP deoal phosphorylase, Thymidine (deoxyuridine) phosphorylase, Purine nucleotide phosphorylase YLR209C 2.4.2.4 PNPI Protein with similarity to human purine nucleoside DT+PI<->THY+DRIP deoa2 phosphorylase, Thymidine (deoxyuridine) phosphorylase YLR245C 3.5.4.5 CDD1 Cytidine deaminase CYTD -> URI + NH3 cddl_l YLR245C 3.5.4.5 CDDI Cytidine deaminase DC->NH3+DU cddl_2 YJR057 W 2.7.4.9 cdc8 dTMP kinase DTMP +ATP <-> ADP + DTDP cdc8 YDR353W 1.6.4.5 TRRI Thioredoxin reductase OTHIO + NADPH -> NADP + RTHIO trrl YHR106W 1.6.4.5 TRR2 mitochondrial thioredoxin reductase OTHIOm + NADPHm ->
NADPm + RTHIOm trr2 YBR252W 3.6.1.23 DUTI dUTP pyrophosphatase (dUTPase) DUTP -> PPI+ DUMP dull YOR074C 2.1.1.45 cdc2l Thymidylate synthase DUMP + METTHF -> DHF + DTMP cdc2l 2.7.4.14 Cytidylate kinase DCMP +ATP <-> ADP + DCDP cmkal 2.7.4.14 Cytidylate kinase CMP +ATP <-> ADP + CDP cmka2 YHRI44C 3.5.4.12 DCDI dCMP deaminase DCMP<->DUMP +NH3 dcdl YBL039C 6.3.4.2 URA7 CTP synthase, highly homologus to URA8 CTP UTP + GLN +
ATP -> GLU + CTP + ADP + PI um7-I
synthase YJR103W 6.3.4.2 URA8 CTP synthase UTP + GLN + ATP -> GLU + CTP + ADP + PI ura8 I
YBL039C 6.3.4.2 URA7 CTP synthase, highly homologus to URAS CTP ATP + UTP +
NH3 -> ADP + PI+ CTP um7 2 synthase YJR103W 6.3.4.2 URA8 CTP synthase ATP+UTP+NH3->ADP+PI+CTP um8_2 YNL292W 4.2.1.70 PUS4 Pseudouridine synthase URA + R5P <-> PURI5P pus4 YPL212C 4.2.1.70 PUS 1 intranuclear protein which exhibits a nucleotide-specific URA + R5P <-> PURI5P pusl intron-dependent tRNA pseudouridine synthase activity YGL063W 4.2.1.70 PUS2 pseudouridine synthase 2 URA + R5P <-> PURI5P pus2 YFL001 W 4.2.1.70 degl Similar to rRNA methyltransferase (Caenorhabditis URA +
R5P <-> PURISP degl elegans) and hypothetical 28K protein (alkaline endoglucanase gene 5' region) from Bacillus sp.
Salvage Pathways YML022W 2.4.2.7 APTI Adenine phosphoribosyltransferase AD + PRPP -> PPI + AMP
aptl YDR441C 2.4.2.7 APT2 similar to adenine phosphoribosyltransferase AD + PRPP ->
PPI + AMP apt2 YNL141 W 3.5.4.4 AAH1 adenine aminohydrolase (adenine deaminase) ADN -> INS +
NH3 aahla YNL141 W 3.5.4.4 AAH1 adenine aminohydrolase (adenine deaminase) DA -> DIN +
NH3 aahlb YLR209C 2.4.2.1 PNPI Purine nucleotide phosphorylase, Xanthosine DIN + PI <->
HYXN + DRIP xapal phosphorylase YLR209C 2.4.2.1 PNPI Xanthosine phosphorylase, Purine nucleotide DA + PI <->
AD +DRIP xapa2 phosphorylase YLR209C 2.4.2.1 PNPI Xanthosine phosphorylase DG + PI <-> GN + DRIP xapa3 YLR209C 2.4.2.1 PNPI Xanthosine phosphorylase, Purine nucleotide HYXN + R1P <-> INS + PI xapa4 phosphorylase YLR209C 2.4.2.1 PNPI Xanthosine phosphorylase, Purine nucleotide AD + RIP <->
PI+ADN xapa5 phosphorylase YLR209C 2.4.2.1 PNPI Xanthosine phosphorylase, Purine nucleotide GN + RIP <->
PI + GSN xapa6 phosphorylase YLR209C 2.4.2.1 PNP1 Xanthosine phosphorylase, Purine nucleotide XAN + RIP <->
PI + XTSINE xapa7 phosphorylase YJR133W 2.4.2.22 XPT1 Xanthine-guanine phosphoribosyltransferase XAN + PRPP ->
XMP + PPI gptl YDR400 W 3.2.2.1 urhl Purine nucleosidase GSN -> GN + RIB pur2l YDR400W 3.2.2.1 urhl Purine nucleosidase ADN -> AD + RIB purll YJR105W 2.7.1.20 YJR105 Adenosine kinase ADN + ATP -> AMP + ADP prm2 W
YDR226W 2.7.4.3 adkl cytosolic adenylate kinase AT? + AMP <-> 2 ADP adkl_1 YDR226W 2.7.4.3 adkl cytosolic adenylate kinase GTP + AMP <-> ADP + GDP adkl_2 YDR226W 2.7.4.3 adkl cytosolic adenylate kinase ITP + AMP <-> ADP + IDP adkl_3 YER170W 2.7.4.3 ADK2 Adenylate kinase (mitochondrial GTP:AMP ATPm+AMPm <-> 2 ADPm adk2_1 phosphotransferase) YER170W 2.7.4.3 adk2 Adenylate kinase (mitochondrialGTP:AMP GTPm+AMPm<->ADPm+GDPm adk2_2 phosphotransferase) YER170W 2.7.4.3 adk2 Adenylate kinase (mitochondrial GTP:AMP ITPm + AMPm <->
ADPm+ IDPm adk2_3 phosphotransferase) YGR180C 1.17.4.1 RNR4 ribonucleotide reductase, small subunit (alt), beta chain YIL066C 1.17.4.1 RNR3 Ribonucleotide reductase (ribonucleoside-diphosphate ADP
+ RTHIO -> DADP + OTHIO rnr3 reductase) large subunit, alpha chain YJL026W 1.17.4.1 rnr2 small subunit of ribonucleotide reductase, beta chain YKL067W 2.7.4.6 YNKI Nucleoside-diphosphatekinase UDP + ATP <-> UTP + ADP
ynkl_1 YKL067W 2.7.4.6 YNK1 Nucleoside-diphosphate kinase CDP + ATP <-> CTP + ADP
ynkl_2 YKL067W 2.7.4.6 YNKI Nucleoside-diphosphate kinase DGDP + ATP <-> DGTP + ADP
ynkl_3 YKL067W 2.7.4.6 YNKI Nucleoside-diphosphate kinase DUDP + ATP <-> DUTP + ADP
ynkl_4 YKL067W 2.7.4.6 YNKI Nucleoside-diphosphatekinase DCDP+ATP<->DCTP+ADP ynkl_5 YKL067W 2.7.4.6 YNKI Nucleoside-diphosphate kinase DTDP + ATP <-> DTTP + ADP
ynkl_6 YKL067W 2.7.4.6 YNK1 Nucleoside-diphosphate kinase DADP + ATP <-> DATP + ADP
ynkl_7 YKL067W 2.7.4.6 YNKI Nucleoside diphosphate kinase GDP + ATP <-> GTP + ADP
ynkl_8 YKL067W 2.7.4.6 YNK1 Nucleoside diphosphate kinase IDP + ATP <-> ITP + IDP
ynkl_9 2.7.4.11 Adenylate kinase, dAMP kinase DAMP + ATP <-> DADP + ADP dampk YNL141 W 3.5.4.2 AAH 1 Adenine deaminase AD -> NH3 + HYXN yicp 2.7.1.73 Inosine kinase INS + ATP -> IMP + ADP gsk l 2.7.1.73 Guanosine kinase GSN + ATP -> GMP + ADP gsk2 YDR399W 2.4.2.8 HPT1 Hypoxanthinephosphoribosyltransferase HYXN + PRPP -> PPI
+ IMP hptl_l YDR399W 2.4.2.8 HPT1 Hypoxanthine phosphoribosyltransferase GN + PRPP -> PPI +
GMP hptl 2 2.4.2.3 Uridine phosphorylase URI + PI <-> URA + RIP udp YKL024C 2.1.4.- URA6 Uridylate kinase UMP + ATP <-> UDP + ADP pyrhl YKL024C 2.1.4: URA6 Uridylate kinase DUMP + ATP <-> DUDP + ADP pyrh2 3.2.2.10 CMP glycosylase CMP -> CYTS + R5P cmpg YHR144C 3.5.4.13 DCD1 dCTPdeaminase DCTP -> DUTP+NH3 dcd 3.1.3.5 5-Nucleotidase DUMP -> DU + PI ushal 3.1.3.5 5'-Nucleotidase DTMP -> DT + PI usha2 3.1.3.5 5'-Nucleotidase DAMP ->DA+PI usha3 3.1.3.5 5'-Nucleotidase DGMP -> DG + PI usha4 3.1.3.5 5'-Nucleotidase DCMP -> DC+PI usha5 3.1.3.5 5'-Nucleotidase CMP -> CYTD + PI usha6 3.1.3.5 5'-Nucleotidase AMP->PI+ADN usha7 3.1.3.5 5'-Nucleotidase GM? -> PT + GSN usha8 3.1.3.5 5'-Nucleotidase IMP -> PI+ INS usha9 3.1.3.5 5'-Nucleotidase XMP -> PI + XTSINE ushal2 3.1.3.5 5'-Nucleotidase UM? -> P1 + URI ushall YER070W 1.17.4.1 RNR1 Ribonucleotide-diphosphatereductase ADP + RTHIO -> DADP
+ OTHIO mrl_1 YER070W 1.17.4.1 RNRI Ribonueleoside-diphosphate reductase GDP + RTHIO -> DGDP
+ OTHIO mrl_2 YER070W 1.17.4.1 RNR1 Ribonueleoside-diphosphate reductase CDP + RTHIO -> DCDP
+ OTHIO mrl_3 YER070W 1.17.4.1 RNR1 Ribonueleoside-diphosphate reductase UDP + RTHIO ->
OTHIO + DUDP mrl4 1.17.4,2 Ribonucleoside-triphosphate reductase ATP + RTHIO ->DATP + OTHIO
nrdd_l 1.17.4.2 Ribonueleoside-triphosphate reductase GTP + RTHIO -> DGTP + OTHIO
nrdd2 1.17.4.2 Ribonucleoside-triphosphate reductase CTP + RTHIO -> DCTP + OTHIO
nrdd3 1.17.4.2 Ribonucleoside-triphosphate reductase UTP + RTHIO -> OTHIO + DUTP
nrdd4 3.6.1: Nucleoside triphosphatase GTP -> GSN + 3 PI muttl 3.6.1: Nucleoside triphosphatase DGTP -> DG + 3 PI mutt2 YML035C 3.2.2.4 AMDL AMP deaminase AMP->AD+R5P amn YBR284W 3.2.2.4 YBR284 Protein with similarity to AMP deaminase AMP -> AD +
R5P amnl W
YJL070C 3.2.2.4 YJL070C Protein with similarity to AMP deaminase AMP -> AD +
RSP amn2 Amino Acid Metabolism Glutamate Metabolism (Aminosugars met) YMR25OW 4.1.1.15 GADI Glutamate decarboxylase B GLU -> GABA + C02 btn2 YGRO19W 2.6.1.19 ugal Aminobutyrate aminotransaminase 2 GABA + AKG -> SUCCSAL
+ GLU ugal YBRO06w 1.2.1.16 YBR006 Succinate semialdehyde dehydrogenase -NADP SUCCSAL +
NADP -> SUCC + NADPH gabda w YKLI04C 2.6.1.16 GFA1 Glutaminefructose-6-phosphate amidotmnsferase F6P + GLN -> GLU + GA6P gfal (glucoseamine-6-phosphate synthase) YFLO17C 2.3.1.4 GNA1 Glucosamine-phosphate N-acetyltransferase ACCOA+GA6P<->COA+NAGA6P gnal YEL058W 5.4.2.3 PCMI Phosphoacetylglucosamine Mutase NAGAIP <->NAGA6P pcmla YDLI03C 2.7.7.23 QRII N-Acetylglucosamine-l-phosphate-uridyltransferase UTP+NAGAIP<->UDPNAG+PPI qril YBR023C 2.4.1.16 chs3 chitin synthase 3 UDPNAG -> CHIT + UDP chs3 YBR038W 2.4.1.16 CHS2 chitin synthase 2 UDPNAG -> CHIT + UDP cbs2 YNL192W 2.4.1.16 CHS1 chitin synthase 2 UDPNAG -> CHIT + UDP chsl YHR037W 1.5.1.12 putt delta-l-pyrroline-5-carboxylatedehydrogenase GLUGSALm+NADPm->NADPHm+GLUm put2_1 PSCm + NADm -> NADHm + GLUm putt YDL171C 1.4.1.14 GLTI Glutamate synthase (NADH) AKG + GLN + NADH -> NAD + 2 GLU Oil YDL215C 1.4.1.4 GDH2 glutamate dehydrogenase GLU + NAD -> AKG + NH3 + NADH
gdh2 YAL062W 1.4.1.4 GDH3 NADP-linked glutamate dehydrogenase AKG + NH3 +NADPH <->
GLU+NADP gdh3 YOR375C 1.4.1.4 GDHI NADP-specific glutamate dehydragenase AKG + NH3 +NADPH <-> GLU +NADP gdhl YPR035W 6.3.1.2 glnl glutamine synthetase GLU + NH3 + ATP -> GLN + ADP + PI
glnl YEL058W 5.4.2.3 PCMI Phosphoglucosamine mutase GA6P <-> GAIP pcmlb 3.5.1.2 GlutaminaseA GLN -> GLU+NH3 glnasea 3.5.1.2 Glutaminase B GLN -> GLU + NH3 glnaseb Glucosamine 5.3.1.10 Glucosamine-6-phosphate deaminase GA6P -> F6P + NH3 nagb Arabinose YBR149W 1.1.1.117 ARAI D-arabinosel-dehydrogenase(NAD(P)+), ARAB +NAD->
ARABLAC+NADH aral_I
YBR149W 1.1.1.117 ARA! D-arabinosel-dehydrogenase(NAD(P)+), ARAB + NADP ->
ARABLAC + NADPH aral_2 Xylose YGR194C 2.7.1.17 XKS1 Xylulokinase XUL+ATP->X5P+ADP xksl Mannitol 1.1.1.17 Mannitol-l-phosphate 5-dehydrogenase MNT6P + NAD <-> F6P + NADH mild Alanine and Aspartate Metabolism YKLI06W 2.6.1.1 AAT1 Asparatetransaminase OAm+GLUm<->ASPm+AKGm aatl_1 YLR027C 2.6.1.1 AAT2 Asparate transaminase OA + GLU <-> ASP +AKG aat2_1 YAR035 W 2.3.1.7 YAT 1 Camitine 0-acetyltransferase COAm + ACARm -> ACCOAm +
CARm yatl YML042W 2.3.1.7 CAT2 Camitine0-acetyltransferase ACCOA + CAR -> COA + ACAR
cat2 YDRI11C 2.6.1.2 YDRIlI putative alanine transaminase PYR + GLU <-> AKG+ALA
alab C
YLR089C 2.6.1.2 YLRO89 alanineaminotransferase,mitochondria)precursor PYRm +
GLUm <-> AKGm + ALAm cfx2 C (glutamic--YPR145W 6.3.5.4 ASN1 asparagine synthetase ASP+ATP+GLN->GLU+ASN+AMP+PPI asnl YGR124W 6.3.5.4 ASN2 asparagine synthetase ASP + ATP + GLN -> GLU + ASN + AMP
+ PPI asn2 YLL062C 2.1.1.10 MHT1 Putative cobalamin-dependent homocysteine S- SAM + HCYS -> SAH + MET mhtl methyltransferase, Homocysteine S-methyltransferase YPL273W 2.1.1.10 SAM4 Putative cobalamin-dependent homocysteine 5- SAM + HCYS -> SAH + MET sam4 methyltransferase Asparagine YCR024c 6.1.1.22 YCR024c asn-tRNA synthetase, mitochondrial ATPm+ASPm+TRNAm->AMPm+PPIm+ mas ASPTRNAm YHRO19C 6.1.1.23 DED8I asn-tRNA synthetase ATP + ASP + TRNA -> AMP + PPI +
ASPTRNA ded81 YLR155C 3.5.1.1 ASP3-1 Asparaginase, extracellular ASN -> ASP + NH3 asp3_1 YLR157C 3.5.1.1 ASP3-2 Asparaginase, extracellular ASN -> ASP +NH3 asp3_2 YLR158C 3.5.1.1 ASP3-3 Asparaginase, extracellular ASN -> ASP+NH3 asp3_3 YLR160C 3.5.1.1 ASP3-4 Asparaginase, extracellular ASN -> ASP + NH3 asp3_4 YDR321W 3.5.1.1 asp! Asparaginase ASN -> ASP + NH3 asp I
Glycine, serine and threonine metabolism YER081W 1.1.1.95 ser3 Phosphoglycerate dehydrogenase 3PG+NAD->NADH+PHP ser3 YIL074C 1.1.1.95 ser33 Phosphoglycerate dehydrogenase 3PG+NAD->NADH+PHP ser33 YOR184W 2.6.1.52 seri phosphoserine transaminase PHP + GLU -> AKG + 3PSER serl YGR208W 3.1.3.3 ser2 phosphoserine phosphatase 3PSER -> PI + SER ser2 YBR263W 2.1.2.1 SHMI Glycine hydroxymethyltransferase THFm + SERm <-> GLYm +
METTHFm shml YLR058C 2.1.2.1 SHM2 Glycine hydroxymethyltransferase THE + SER <-> GLY +
METTHF shm2 YFL030W 2.6.1.44 YFL030 Putative alanine glyoxylate aminotransferase (serine ALA+ GLX <-> PYR + GLY agt W pyruvate aminotransferase) YDR019C 2.1.2.10 GCV1 glycine cleavage T protein (T subunit of glycine GLYm +
THFm +NADm -> METTHFm + NADHm gcvl_1 decarboxylase complex + C02 +NH3 YDRO 19C 2.1.2.10 GCV 1 glycine cleavage T protein (T subunit of glycine GLY +
THF+ NAD -> METTHF + NADH + C02 + gcvl_2 decarboxylase complex NH3 YER052C 2.7.2.4 hom3 Aspartate kinase, Aspartate kinase 1, II, III ASP + ATP -> ADP + GASP hom3 YDR158W 1.2.1.11 hom2 aspartic beta semi-aldehyde dehydrogenase,Aspartate BASP
+ NADPH -> NADP + PI + ASPSA hom2 semialdehyde dehydrogenase YJR139C 1.1.1.3 hom6 Homoserine dehydrogenase I ASPSA+NADH->NAD+HSER hom6_1 YJR139C 1.1.1.3 hom6 Homoserine dehydrogenase I ASPSA + NADPH -> NADP+HSER
hom6_2 YHR025W 2.7.1.39 thrl homoserine kinase HSER + ATP -> ADP + PHSER thrl YCRO53W 4.2.99.2 thr4 threonine synthase PHSER -> PI+THR thr4 1 YGR155W 4.2.1.22 CYS4 Cystathionine beta-synthase SER + HCYS -> LLCT cys4 YEL046C 4.1.2.5 GLY1 Threonine Aldolase GLY + ACAL -> THR glyl YMR189W 1.4.4.2 GCV2 Glycine decarboxylase complex (P-subunit), glycine GLYm +
LIPOm <-> SAPm + C02m gcv2 synthase (P-subunit), Glycine cleavage system (P-subunit) YCL064C 4.2.1.16 chal threonine deaminase THR -> NH3 + OBUT chal_1 YER086W 4.2.1.16 ilvl L-Serine dehydratase THRm->NH3m+OBUTm ilvl YCL064C 4.2.1.13 chal catabolic serine (threonine) dehydratase SER-> PYR + NH3 chal2 YIL167W 4.2.1.13 YIL167 catabolic serine (threonine) dehydratase SER> PYR +
NH3 sdll W
1.1.1.103 Threonine dehydrogenase THR + NAD -> GLY + AC + NADH tdhlc Methionine metabolism YFR055W 4.4.1.8 YFR055 Cystathionine-b-lyase LLCT->HCYS+PYR+NH3 mete W
YER043C 3.3.1.1 SAHI putative S-adenosyl-L-homocysteine hydrolase SAH -> HCYS
+ ADN sahl YER091C 2.1.1.14 met6 vitamin B 12-(cobalamin)-independent isozyme of HCYS +
MTHPTGLU -> THPTGLU + MET met6 methionine synthase (also called N5-methyltetrahydrofolate homocysteine methyltransferase or 5-methyltetrahydropteroyl triglutamate homocysteine methyltransferase) 2.1.1.13 Methionine synthase HCYS+MTHF>THF+MET met6_2 YAL012W 4.4.1.1 cys3 cystathionine gamma-lyase LLCT>CYS+NH3+OBUT cys3 YNL277W 2.3.1.31 met2 homoserine 0-trans-acetylase ACCOA+HSER<->COA+0AHSER
met2 YLR303W 4.2.99.10 MET17 O-Acetylhomoserine(thiol)-lyase OAHSER + METH -> MET +
AC met17_1 YLR303W 4.2.99.8 MET17 O-Acetylhomoserine(thiol)-lyase OAHSER+H2S->AC+HCYS
metl7_2 YLR303 W 4.2.99.8, met17 0-acetylhomoserine sulfhydrylase (OAH SHLase); OAHSER
+ H2S -> AC + HCYS metl7_3 4.2.99.10 converts O-acetylhomoserine into homocysteine YML082W 4.2.99.9 YML082 putative cystathionine gamma-synthase OSLHSER <-> SUCC
+ OBUT + NH4 metl7h W
YDR502C 2.5.1.6 sam2 S-adenosylmethionine synthetase MET + ATP -> PPI + PI +
SAM sam2 YLRI80W 2.5.1.6 saml S-adenosylmethionine synthetase MET + ATP -> PPI + PI +
SAM saml YLR172C 2.1.1.98 DPH5 Diphthine synthase SAM + CALH -> SAH + DPTH dph5 Cysteine Biosynthesis YJR01OW 2.7.7.4 met3 ATP sulfurylase SLIT +ATP->PPI+APS met3 YKLOOIC 2.7.1.25 met14 adenylylsulfatekinase APS+ATP->ADP+PAPS met14 YFR030W 1.8.1.2 metlO sulfite reductase H2S03 +3 NADPH <> H2S+3 NADP metlO
2.3.1.30 Serine transacetylase SER + ACCOA -> COA + ASER cysl YGRO12W 4.2.99.8 YGRO12 putative cysteine synthase (0-acetylserine ASER+ H2S -> AC + CYS sell 1 W sulthydrylase) (0-YOL064C 3.1.3.7 MET22 3'- 5' Bisphosphate nucleotidase PAP -> AMP + PI met22 YPR167C 1.8,99.4 MET16 PAPS Reductase PAPS +RTHIO->OTHIO+H2S03+PAP met16 YCLO50C 2.7.7.5 apal diadenosine 5',5"'-P 1,P4-tetraphosphate phosphorylase I
ADP + SLF <-> PI+ APS apal_2 Branched Chain Amino Acid Metabolism (Valise, Leucine and Isoleucine) YHR208W 2.6.1.42 BATI Branched chain amino acid aminotransferase OICAPm+GLUm<->AKGm+LEUm batl_1 YHR208W 2.6.1.42 BATI Branched chain amino acid aminotransferase OMVALm+GLUm<>AKGm+ILEm batl_2 YJR148W 2.6.1.42 BAT2 branched-chain amino acid transaminase, highly similar OMVAL +GLU <-> AKG + ILE bat2_1 to mammalian ECA39, which is regulated by the oncogene myc YJR148W 2.6.1.42 BAT2 Branched chain amino acid aminotransferase OIVAL + GLU <-> AKG + VAL bat2_2 YJR148W 2.6.1.42 BAT2 branched-chain amino acid transaminase, highly similar OICAP + GLU <-> AKG + LEU bat2_3 to mammalian ECA39, which is regulated by the oncogene myc YMR108W 4.1.3.18 ilv2 Acetolactate synthase, large subunit OBUTm + PYRm ->
ABUTm + C02m ilv2_1 YCLO09C 4.1.3.18 ILV6 Acetolactate synthase, small subunit YMR108W 4.1.3.18 ilv2 Acetolactate synthase, large subunit 2 PYRm ->
C02m+ACLACm ilv2_2 YCLO09C 4.1.3.18 ILV6 Acetolactate synthase, small subunit YLR355C 1.1.1.86 ilv5 Keto-acid reductoisomerase ACLACm+NADPHm->NADPm+DHVALm ilv5_I
YLR355C 1.1.1.86 ilv5 Keto-acid reductoisomerase ABUTm+NADPHm->NADPm+DHMVAm ilv5 2 YJR016C 4.2.1.9 ilv3 Dihydroxy acid dehydratase DHVALm -> OIVALm ilv3 1 YJR016C 4.2.1.9 ilv3 Dihydroxy acid dehydratase DHMVAm -> OMVALm ilv32 YNLI04C 4.1.3.12 LEU4 alpha-isopropylmalate synthase (2-Isopropylmalate ACCOAm + OIVALm -> COAm + IPPMALm leu4 Synthase) YGLO09C 4.2.1.33 leul Isopropylmalate isomerase CBHCAP <-> IPPMAL leul_1 YGLO09C 4.2.1.33 leul isopropylmalate isomerase PPMAL <-> IPPMAL leul_2 YCLO18W 1.1.1.85 leu2 beta-IPM (isopropylmalate) dehydrogenase IPPMAL + NAD ->
NADH + OICAP + C02 leu2 Lysine biosyntlresisldegradation 4.2.1.79 2-Methylcitrate dehydratase HCITm <-> HACNm 1ys3 YDR234W 4.2.1.36 lys4 Homoaconitate hydratase HICITm <-> HACNm lys4 YIL094C 1.1.1.155 LYS 12 Homoisocitrate dehydrogenase (Strathem:1.1.1.87) HICITm +NADm <-> OXAm + C02m+NADHm lysl2 non-enzymatic OXAm <-> C02m + AKAm lysl2b 2.6.1.39 2-Aminoadipate transaminase AKA+GLU<_>AMA+AKG amit YBRII5C 1.2.1.31 lys2 L-Aminoadipate-semialdehyde dehydrogenase, large AMA +
NADPH + ATP -> AMASA + NADP + AMP lys2_1 subunit + PPI
YGL154C 1.2.1.31 lys5 L-Aminoadipate-semialdehyde dehydrogenase, small subunit YBRII5C 1.2.1.31 lys2 L-Aminoadipate-semialdehyde dehydrogenase, large AMA +
NADH + ATP -> AMASA + NAD + AMP + lys2_2 subunit PPI
YGL154C 1.2.1.31 lys5 L-Aminoadipate-semialdehyde dehydrogenase, small subunit YNRO50C 1.5.1.10 lys9 Saccharopine dehydrogenase (NADP+, L-glutamate GLU +
AMASA + NADPH <-> SACP+NADP lys9 forming) YIR034C 1.5.1.7 lysl Saccharopine dehydrogenase (NAD+, L-lysine forming) SACP+NAD <-> LYS + AKG + NADH lysla YDR037W 6.1.1.6 krsl lysyl-tRNA synthetase, cytosolic ATP + LYS + LTRNA -> AMP
+ PPI + LLTRNA krsl YNL073W 6.1.1.6 mskl lysyl-tRNA synthetase, mitochondrial ATPm+LYSm+LTRNAm->AMPm+PPIm+ mskl LLTRNAm YDR368W 1.1.1: YPRI similar to aldo-keto reductase Arginine metabolism YMR062C 2.3.1.1 ECM40 Amino-acid N-acetyltransferase GLUm+ACCOAm->COAm+NAGLUm ecm40_1 YER069W 2.7.2.8 arg5 Acetylglutamate kinase NAGLUm + ATPm -> ADPm +NAGLUPm arg6 YER069W 1.2.1.38 arg5 N-acetyl-gamma-glutamyl-phosphate reductase and NAGLUPm+NADPHm->NADPm+PIm+ arg5 acetylglutamate kinase NAGLUSm YOLI4OW 2.6.1.11 arg8 Acetylornithineaminotransferase NAGLUSm+GLUm->AKGm+NAORNm arg8 YMR062C 2.3.1.35 ECM40 Glutamate N-acetyltransferase NAORNm + GLUm -> ORNm +NAGLUm ccm40_2 YJL130C 6.3.5.5 ura2 carbamoyl-phophate synthetase, aspartate GLN + 2 ATP +
C02 -> GLU + CAP + 2 ADP + PI ura2_2 transcarbamylase, and glutamine amidotransferase YJR109C 6.3.5.5 CPA2 carbamyl phosphate synthetase, large chain GLN + 2 ATP +
C02 -> GLU + CAP + 2 ADP + PI cpa2 YOR303 W 6.3.5.5 opal Carbamoyl phosphate synthetase, samll chain, arginine specific YJL088W 2.1.3.3 arg3 Omithine carbamoyltransferase ORN + CAP -> CITR + PI arg3 YLR438W 2.6.1.13 cart Omithine transaminase ORN + AKG -> GLUGSAL+ GLU cart YOL058W 6.3.4.5 arg1 arginosuccinatesynthetase CITR + ASP + ATP <-> AMP + PPI
+ ARGSUCC argl YHRO18C 4.3.2.1 arg4 argininosuccinate lyase ARGSUCC <-> FUM + ARG arg4 YKL184W 4.1.1.17 spel Ornithine decarboxylase ORN -> PTRSC + C02 spel YOL052C 4.1.1.50 spe2 S-adenosylmethionine decarboxylase SAM <-> DSAM + C02 spe2 YPR069C 2.5.1.16 SPE3 putrescine aminopropyltransferase (spermidine PTRSC +
SAM -> SPRMD + 5MTA spe3 synthase) YLRI46C 2.5.1.22 SPE4 Spermine synthase DSAM + SPRMD -> 5MTA + SPRM spe4 YDR242W 3.5.1.4 AMD2 Amidase GBAD -> GBAT + NH3 amd2_1 YMR293C 3.5.1.4 YMR293 Probable Amidase GBAD -> GBAT+NH3 amd C
YPL1I1W 3.5.3.1 carl arginase ARG->ORN+UREA carl YDR341 C 6.1.1.19 YDR341 arginyl-tRNA synthetase ATP + ARG + ATRNA -> AMP +
PPI + ALTRNA atrna C
YHR091C 6.1.1.19 MSR1 arginyl-tRNAsynthetase ATP + ARG + ATRNA -> AMP + PPI +
ALTRNA msrl YHR068W 1.5.99.6 DYS1 deoxyhypusine synthase SPRMD + Qm -> DAPRP + QH2m dysi Histidine metabolism YER055C 2.4.2.17 hisl ATP phosphoribosyltransferase PRPP + ATP -> PPI + PRBATP
his!
YCL030C 3.6.1.31 his4 phosphoribosyl-AMP cyclohydrolase / phosphoribosyl-PRBATP -> PPI + PRBAMP his4_I
ATP pyrophosphohydrolase / histidinol dehydrogenase YCL030C 3.5.4.19 his4 histidinol dehydrogenase PRBATP -> PRFP his4_2 YIL020C 5.3.1.16 his6 phosphoribosyl-5-amino-l-phosphoribosyl-4- PRFP -> PRLP
his6 imidazolecarboxiamide isomerase YOR202W 4.2.1.19 his3 imidazoleglycerol-phosphate dehydratase DIMGP -> IMACP
his3 YIL116W 2.6.1.9 his5 histidinol-phosphate aminotransferase IMACP + GLU -> AKG
+ HISOLP hiss YFR025C 3.1.3.15 his2 Histidinolphosphatase HISOLP ->PI+HISOL his2 YCL030C 1.1.1.23 his4 phosphoribosyl-AMP cyclohydrolase / phosphoribosyl-HISOL + 2 NAD -> HIS + 2 NADH his4_3 ATP pyrophosphohydrolase / histidinol dehydrogenase YBR248C 2.4.2.- his7 glutamine amidotransferase:cyclase PRLP + GLN -> GLU +
AICAR + DIMGP his7 YPR033C 6.1.1.21 htsI histidyl-tRNA synthetase ATP + HIS + HTRNA -> AMP + PPI
+ HHTRNA htsl YBR034C 2.1.1.- hmtl hnRNP arginine N-methyltransferase SAM + HIS -> SAH +
MHIS hmtl YCLO54W 2.1.1: spbl putative RNA methyltransferase YMLI 1OC 2.1.1.- cogs ubiquinone biosynthesis methlytransferase COQS
YOR201C 2.1.1: pet56 rRNA (guanosine-2'-O-)-methyltransferase YPL266W 2.1.1: diml dimethyladenosine transferase Phenylalanine, tyrosine and tryptophan biosynthesis (Aromatic Amino Acids) YBR249C 4.1.2.15 AR04 3-deoxy-D-arabino-heptulosonate 7-phosphate (DAHP) E4P +
PEP -> PI + 3DDAH7P aro4 synthase isoenzyme YDR035W 4.1.2.15 AR03 DAHP synthase\; a.k.a. phospho-2-dehydro-3- E41? +PEP->PI+3DDAH7P aro3 deoxyheptonate aldolase, phenylalanine-inhibited\;
phospho-2-keto-3-deoxyheptonate aldolase\; 2-dehydro-3-deoxyphosphoheptonate aldolase\; 3-deoxy-D-arabine-heptulosonate-7-phosphate synthase YDR127W 4.6.1.3 aro l pentafunetional arom polypeptide (contains: 3- 3DDAH7P -> DQT+PI arol_1 dehydroquinate synthase, 3-dehydroquinate dehydratase (3-dehydroquinase), shikimate 5-dehydrogenase, shikimate kinase, and epsp synthase) YDR127W 4.2.1.10 aro l 3-Dehydroquinate dehydratase DQT->DHSK arol 2 YDR127W 1.1.1.25 aro 1 Shikimate dehydrogenase DHSK + NADPH -> SME + NADP arol YDRI27W 2.7.1.71 arol Shikimatekinase1,II SME+ATP->ADP+SMESP arot_4 YDR127W 2.5.1.19 arol 3-Phosphoshikimate-l-carboxyvinyltransferase SMESP+PEP->3PSME+PI arol 5 YGL148W 4.6.1.4 aro2 Chorismate synthase 3PSME->PI+CHOR aro2 YPR060C 5.4.99.5 aro7 Chorismate mutase CHOR -> PHEN aro7 YNL316C 4.2.1.51 pha2 prephenate dehydratase PHEN -> C02 + PHPYR pha2 YHR137W 2.6.1: ARO9 putative aromatic amino acid aminotransferase II PHPYR +
GLU <-> AKG + PHE aro9_l YBR166C 1.3.1.13 tyrl Prephenate dehydrogenase (NADP+) PHEN + NADP -> 4HPP +
C02 + NADPH tyrI
YGL202W 2.6.1: AR08 aromatic amino acid aminotransferase I 4HPP + GLU -> AKG +
TYR aro8 YHR137 W 2.6.1.- AR09 aromatic amino acid aminotransferase II 4HPP + GLU ->
AKG + TYR aro9_2 1.3.1.12 Prephanate dehydrogenase PHEN + NAD -> 4HPP + C02 +NADH tyra2 YER09OW 4.1.3.27 trp2 Anthranilate synthase CHOR+GLN->GLU+PYR+AN trp2_1 YKL211C 4.1.3.27 trp3 Anthranilate synthase CHOR+ GLN -> GLU + PYR+AN trp3_1 YDR354W 2.4.2.18 trp4 anthranilate phosphoribosyl transferase AN + PRPP -> PPI
+ NPRAN trp4 YDR007W 5.3.1.24 trill n-(5'-phosphoribosyl)-anthranilate isomerase NPRAN ->
CPADSP trpI
YKL21IC 4.1.1.48 trp3 Indoleglycerol phosphate synthase CPAD5P -> C02 + IGP
trp3 2 YGL026C 4.2.1.20 trp5 tryptophan synthetase IGP + SER -> T3PI + TRP trp5 YDR256C 1.11.1.6 CTAI catalase A 2 H202 -> 02 ctal YGR088W 1.11.1.6 CTTI cytoplasmic catalase T 2 H202 -> 02 cttl YKLI06W 2.6.1.1 AAT1 Asparate aminotransferase 4HPP + GLU <_> AKG + TYR aatl_2 YLR027C 2.6.1.1 AAT2 Asparate aminotransferase 4HPP + GLU <-> AKG + TYR aat2_2 YMR170C 1.2.1.5 ALD2 Cytosolic aldeyhde dehydrogenase ACAL+ NAD -> NADH + AC
ald2 YMR169C 1.2.1.5 ALD3 strong similarity to aldehyde dehydrogenase ACAL +NAD ->
NADH + AC ald3 YOR374W 1.2.1.3 ALD4 mitochondria) aldehyde dehydrogenase ACALm + NADm ->
NADHm + ACm ald4_1 YOR374W 1.2.1.3 ALD4 mitochondrial aldehyde dehydrogenase ACALm + NADPm ->
NADPHm + ACm ald4_2 YER073W 1.2.1.3 ALD5 mitochondrial Aldehyde Dehydrogenase ACALm+NADPm->NADPHm+ACm ald5_I
YPL06IW 1.2.1.3 ALD6 Cytosolic Aldehyde Dehydrogenase ACAL + NADP -> NADPH +
AC ald6 YJR078 W 1.13.11.1 YJR078 Protein with similarity to indoleamine 2,3- TRP + 02 -> FKYN tdo2 1 W dioxygenases, which catalyze conversion of tryptophan and other indole derivatives into kynurenines, Tryptophan 2,3-dioxygenase 3.5.1.9 Kynurenine formamidase FKYN -> FOR + KYN kfor YLR231C 3.7.1.3 YLR231 probable kynureninase (L-kynurenine hydrolase) KYN ->
ALA + AN kynu_1 C
YBL098W 1.14.13.9 YBL098 Kynurenine 3-hydroxylase, NADPH-dependent flavin KYN
+ NADPH + 02 -> HKYN + NADP kmo W monooxygenase that catalyzes the hydroxylation of kynurenine to 3-hydroxykynurenine in tryptophan degradation and nicotinic acid synthesis, Kynurenine 3-monooxygenase YLR231C 3.7.1.3 YLR231 probable kynureninase (L-kynurenine hydrolase) HKYN ->
HAN + ALA kynu ?
C
YJR025C 1.13.11.6 BNAI 3-hydroxyanthranilate 3,4-dioxygenase (3-HAO) (3- HAN +
02 -> CMUSA bnal hydroxyanthranilic acid dioxygenase) (3-hydroxyanthranilatehydroxyanthranilic acid dioxygenase) (3-hydroxyanthranilate oxygenase) 4.1.1.45 Picolinic acid decarboxylase CMUSA ->C02+AM6SA aaaa 1.2.1.32 AM6SA + NAD -> AMUCO + NADH aaab 1.5.1: AMUCO + NADPH -> AKA + NADP + NH4 aaac 1.3.11.27 4-Hydroxyphenylpyruvate dioxygenase 4HPP + 02 -> HOMOGEN + C02 tyrdega 1.13.11.5 Homogentisate 1,2-dioxygenase HOMOGEN + 02 -> MACAC tyrdegb 5.2.1.2 Maleyl-acetoacetate isomerase MACAC -> FUACAC tyrdegc 3.7.1.2 Fumarylacetoacetase FUACAC -> FUM + ACTAC trydegd YDR268w 6.1.1.2 MSWI tryptophanyl-tRNA synthetase, mitochondrial ATPm+TRPm+TRNAm->AMPm+PPIm+ mswl TRPTRNAm YDR242W 3.5.1.4 AMD2 putative amidase PAD-> PAC + NH3 amd2_2 YDR242W 3.5.1.4 AMD2 putative amidase IAD -> IAC +NH3 amd2 3 2.6.1.29 Diamine transaminase SPRMD + ACCOA -> ASPERMD + COA spry 1.5.3.11 Polyamine oxidase ASPERMD + 02 -> APRUT + APROA + H2O2 sprb 1.5.3.11 Polyamine oxidase APRUT+02->GABAL+APROA+H2O2 sprc 2.6.1.29 Diamine transaminase SPRM + ACCOA -> ASPRM + COA sprd 1.5.3.11 Polyamine oxidase ASPRM + 02 -> ASPERMD + APROA + H202 spre Proline biosynthesis YDR300C 2.7.2.11 pro) gamma-glutamylkinase, glutamate kinase GLU + ATP -> ADP
+ GLUP pro) YOR323C 1.2.1.41 PR02 gamma-glutamyl phosphate reductase GLUP + NADH -> NAD +
PI + GLUGSAL pro2_I
YOR323C 1.2.1.41 pro2 gamma-glutamyl phosphate reductase GLUP + NADPH -> NADP
+ PI + GLUGSAL pro2_2 spontaneous conversion (Strathem) GLUGSAL <-> P5C gpsl spontaneous conversion (Strathem) GLUGSALm <-> P5Cm gps2 YER023W 1.5.1.2 pro3 Pyrroline-5-carboxylate reductase P5C + NADPH -> PRO +
NADP pro3 1 YER023W 1.5.1.2 pro3 Pyrroline-5-carboxylate reductase PHC + NADPH -> HPRO +
NADP pro3_3 YER023W 1.5.1.2 pro3 Pyrroline-5-carboxylatereductase PHC + NADH -> HPRO + NAD
pro3 4 YLR142W 1.5.3.- PUT1 Proline oxidase PROm+NADm->P5Cm+NADHm pro3_5 Metabolism of Other Amino Acids beta-Alanine metabolism 1.2.1.3 aldehyde dehydrogenase, mitochondrial I GABALm + NADm -> GABAm + NADHm aldl YER073W 1.2.1.3 ALD5 mitochondrial Aldehyde Dehydrogenase LACALm + NADm <->
LLACm + NADHm ald5 2 Cyanoamino acid metabolism YJL126W 3.5.5.1 NIT2 NITRILASE APROP -> ALA + NH3 nit2_1 YJL126W 3.5.5.1 NIT2 NITRILASE ACYBUT -> GLU + NH3 nit2 2 Proteins, Peptides and Aminoacids Metabolism YLR195C 2.3.1.97 nmtl GlycyipeptideN-tetradecanoyltransferase TCOA+GLP->COA+TGLP nmtl YDL040C 2.3.1.88 natl Peptide alpha-N-acetyltransferase ACCOA + PEPD -> COA +
APEP natl YGR147C 2.3.1.88 NAT2 Peptide alpha-N-acetyltransferase ACCOA + PEPD -> COA +
APEP nat2 Glutathione Biosynthesis YJL101C 6.3.2.2 GSHI gamma-glutamylcysteinesynthetase CYS + GLU + ATP -> GC +
PI + ADP gshl YOL049W 6.3.2.3 GSH2 Glutathione Synthetase GLY + GC + ATP -> RGT + PI+ ADP
gsh2 YBR244W 1.11.1.9 GPX2 Glutathione peroxidase 2 RGT+H202 <-> OGT gpx2 YIR037W 1.11.1.9 HYRI Glutathione peroxidase 2 RGT + H202 <-> OGT hyrl YKL026C 1.11.1.9 GPXI Glutathione peroxidase 2 RGT + H202 <-> OGT gpxl YPL091 W 1.6.4.2 GLRI Glutathione oxidoreductase NADPH + OGT -> NADP + RGT
girl YLR299W 2.3.2.2 ECM38 gamma-glutamyltranspeptidase RGT+ALA-> CGLY + ALAGLY
ecm38 Metabolism of Complex Carbohydrates Starch and sucrose metabolism YGR032W 2.4.1.34 GSC2 1,3-beta-Glucan synthase UDPG-> 13GLUCAN+UDP gsc2 YLR342W 2.4.1.34 FKSI 1,3-beta-Glucansynthase UDPG -> 13GLUCAN + UDP fksl YGR306W 2.4.1.34 FKS3 Protein with similarity to Fkslp and Gsc2p UDPG ->
13GLUCAN + UDP fks3 YDR261C 3.2.1.58 exg2 Exo-1,3-b-glucanase 13GLUCAN -> GLC exg2 YGR282C 3.2.1.58 BGL2 Cell wall endo-beta-1,3-glucanase 13GLUCAN -> GLC bgl2 YLR300W 3.2.1.58 exgl Exo-1,3-beta-glucanase 13GLUCAN -> GLC exgl YOR19OW 3.2.1.58 sprl sporulation-specific exo-1,3-beta-glucanase 13GLUCAN ->
GLC sprl Glycoprotein Biosynthesis / Degradation YMR013C 2.7.1.108 sec59 Dolicholkinase CTP + DOL -> CDP + DOLP sec59 YPRi83W 2.4.1.83 DPM1 Dolichyl-phosphate beta-D-mannosyltransferase GDPMAN +
DOLP -> GDP + DOLMANP dpml YAL023C 2.4.1.109 PMT2 Dolichyl-phosphate-mannose--protein DOLMANP -> DOLP +
MANNAN pmt2 mannosyltransferase YDL093W 2.4.1.109 PMT5 Dolichyl-phosphate-mannose--protein DOLMANP -> DOLP +
MANNAN pmt5 mannosyltransferase YDL095W 2.4.1.109 PMT) Dolichyl-phosphate-mannose--protein DOLMANP -> DOLP +
MANNAN pmt) mannosyltransferase YGR199W 2.4.1.109 PMT6 Dolichyl-phosphate-mannose--protein DOLMANP -> DOLP +
MANNAN pmt6 mannosyltransferase YJR143C 2.4.1.109 PMT4 Dolichyl-phosphate-mannose--protein DOLMANP -> DOLP +
MANNAN pmt4 mannosyltransferase YOR321W 2.4.1.109 PMT3 Dolichyl-phosphate-mannose--protein DOLMANP -> DOLP +
MANNAN pmt3 mannosyltransferase YBR199W 2.4.1.131 KTR4 Glycolipid2-alpha-mannosyltransferase MAN2PD + 2 GDPMAN
-> 2 GDP + 2MANPD ktr4 YBR205W 2.4.1.131 KTR3 Glycolipid2-alpha-mannosyltransferase MAN2PD + 2 GDPMAN
-> 2 GDP + 2MANPD ktr3 YDR483W 2.4.1.131 kre2 Glycolipid2-alpha-mannosyltransferase MAN2PD + 2 GDPMAN
-> 2 GDP + 2MANPD kre2 YJL139C 2.4.1.131 yurl Glycolipid2-alpha-mannosyltransferase MAN2PD + 2 GDPMAN
-> 2 GDP + 2MANPD yurl YKR061 W 2.4.1.131 KTR2 Glycolipid 2-alpha-mannosyltransferase MAN2PD + 2 GDPMAN -> 2 GDP + 2MANPD ktt2 YOR099W 2.4.1.131 KTRI Glycolipid2-alpha-mannosyltransferase MAN2PD + 2 GDPMAN
-> 2 GDP + 2MANPD ktrl YPL053C 2.4.1.131 KTR6 Glycolipid 2-alpha-mannosyltransferase MAN2PD + 2 GDPMAN -> 2 GDP + 2MANPD ktr6 Aminosugars metabolism YER062C 3.1.3.21 HOR2 DL-glycerol-3-phosphatase GL3P->GL+PI hor2 YIL053W 3.1.3.21 RHR2 DL-glycerol-3-phosphatase GL3P->GL+PI rhr2 YLR307W 3.5.1.41 CDAI Chitin Deacetylase CHIT -> CHITO + AC cdal YLR308W 3.5.1.41 CDA2 Chitin Deacetylase CHIT -> CHITO + AC cda2 Metabolism of Complex Lipids Glycerol (Glycerolipid metabolism) YFLO53W 2.7.1.29 DAK2 dihydroxyacetonekinase GLYN + ATP -> T3P2 + ADP dak2 YML070W 2.7.1.29 DAKI putative dihydroxyacetonekinase GLYN + ATP -> T3P2 + ADP
daki YDL022W 1.1.1.8 GPDI glycerol-3-phosphate dehydrogenase (NAD) T3P2+NADH->GL3P+NAD gpdl YOL059W 1.1.1.8 GPD2 glycerol-3-phosphate dehydrogenase (NAD) T3P2 + NADH ->
GL3P + NAD gpd2 YHL032C 2.7.1.30 GUT) glycerol kinase GL + ATP -> GL3P + ADP gut) YIL155C 1.1.99.5 GUT2 glycerol-3-phosphate dehydrogenase GL3P + FADm -> T3P2 +
FADH2m gut2 DAGLY + 0.017 C100ACP + 0.062 C120ACP + daga 0.100 C 140ACP + 0.270 C 160ACP + 0.169 C 161 ACP + 0.055 C I80ACP + 0.235 C 181ACP +
0.093 C 182ACP -> TAGLY + ACP
Metabolism of Cofactors, Vitamins, and Other Substances Thiamine (Vitamin BI) metabolism YOR143C 2.7.6.2 THI80 Thiamin pyrophosphokinase ATP + THIAMIN -> AM? + TP?
thi80 1 YOR143C 2.7.6.2 THI80 Thiamin pyrophosphokinase ATP + TPP -> AMP + TPPP thi802 thiC protein AIR -> AHM thic YOL055C 2.7.1.49 THI20 Bipartite protein consisting of N-terminal AHM + ATP ->
AHMP + ADP thi20 hydroxymethylpyrimidine phosphate (HMP-P) kinase domain, needed for thiamine biosynthesis, fused to C-terminal Petl8p-like domain of indeterminant function YPL258C 2.7.1.49 THI21 Bipartite protein consisting of N-terminal AHM + ATP ->
AHMP + ADP thi21 hydroxymethylpyrimidine phosphate (HMP-P) kinase domain, needed for thiamine biosynthesis, fused to C-terminal Petl8p-like domain of indeterminant function YPRI21 W 2.7.1.49 THI22 Bipartite protein consisting of N-terminal AHM + ATP -> AHMP + ADP thi22 hydroxymethylpyrimidine phosphate (HMP-P) kinase domain, needed for thiamine biosynthesis, fused to C-terminal Petl8p-like domain of indeterminant function YOL055C 2.7.4.7 THI20 HMP-phosphate kinase AHMP + ATP -> AHMPP + ADP thid Hypothetical T3PI + PYR -> DTP unkrxnl thiG protein DTP+TYR+CYS->THZ+HBA+C02 thig thiE protein DTP+TYR+CYS->THZ+HBA+C02 thie thiF protein DTP+TYR+CYS->THZ+HBA+C02 thif thiH protein DTP+TYR+CYS->THZ+HBA+C02 thih YPL214C 2.7.1.50 THI6 Hydroxyethylthiazole kinase THZ + ATP -> THZP + ADP thim YPL214C 2.5.1.3 THI6 TMP pyrophosphorylase, hydroxyethylthiazole kinase THZP +
AHMPP -> THMP + PPI thi6 2.7.4.16 Thiamin phosphate kinase THMP + ATP <-> TPP + ADP thil 3.1.3.- (DL)-glycerol-3-phosphatase 2 THMP -> THIAMIN + PI unkrxn8 Riboflavin metabolism YBL033C 3.5.4.25 ribl GTP cyclohydrolase II GTP -> D6RP5P + FOR + PPI ribi YBR153W 3.5.4.26 RIB7 HTP reductase, second step in the riboflavin D6RP5P ->
A6RP5P +NH3 ribdl biosynthesis pathway YBR153 W 1.1.1.193 rib? Pyrimidine reductase A6RP5P + NADPH -> A6RP5P2 +NADP
rib?
Pyrimidine phosphatase A6RP5P2 -> A6RP+PI prm 3,4 Dihydroxy-2-butanone-4-phosphate synthase RL5P -> DB4P + FOR ribb YBR256C 2.5.1.9 RIBS Riboflavin biosynthesis pathway enzyme, 6,7-dimethyl-DB4P + A6RP -> D8RL + PI rib5 8-ribityllumazine synthase, apha chain YOL143C 2.5.1.9 RIB4 Riboflavin biosynthesis pathway enzyme, 6,7-dimethyl-8-ribityllumazine synthase, beta chain YAR071 W 3.1.3.2 phol l Acid phosphatase FMN -> RIBFLAV + PI phol l YDR236C 2.7.1.26 FMN 1 Riboflavin kinase RIBFLAV + ATP -> FMN +ADP fmnl 1 YDR236C 2.7.1.26 FMN 1 Riboflavin kinase RIBFLAVm + ATPm -> FMNm + ADPm fmnl 2 YDL045C 2.7.7.2 FAD 1 FAD synthetase FMN + ATP -> FAD + PPI fad 1 2.7.7.2 FAD synthetase FMNm+ATPm-> FADm+PPIm fad lb Vitamin B6 (Pyridoxine) Biosynthesis metabolism 2.7.1.35 Pyridoxine kinase PYRDX + ATP -> P51? + ADP pdxka 2.7.1.35 Pyridoxine kinase PDLA+ ATP -> PDLA5P + ADP pdxkb 2.7.1.35 Pyridoxine kinase PL + ATP -> PLSP +ADP pdxkc YBR035C 1.4.3.5 PDX3 Pyridoxine 5'-phosphateoxidase PDLA5P+02->PLSP+H202+NH3 pdx3_1 YBR035C 1.4.3.5 PDX3 Pyridoxine 5'-phosphate oxidase P5P+02<-> PLSP+H202 pdx3_2 YBR035C 1.4.3.5 PDX3 Pyridoxine 5'-phosphate oxidase PYRDX + 02 <-> PL+H202 pdx3-3 YBR035C 1.4.3.5 PDX3 Pyridoxine 5'-phosphate oxidase PL+02+NH3<->PDLA+H202 pdx3 4 YBR035C 1.4.3.5 PDX3 Pyridoxine 5'-phosphate oxidase PDLA5P+02->PL5P+H2O2+NH3 pdx3-5 YOR184W 2.6.1.52 serl Hypothetical transaminase/phosphoserinetransaminase OHB+GLU<->PHT+AKG serl-2 YCR053W 4.2.99.2 thr4 Threoninesynthase PHT->4HLT+PI thr4 2 3.1.3.- Hypothetical Enzyme PDLA5P -> PDLA + PI hor2b Pantothenate and CoA biosynthesis 3 MALCOA -> CHCOA + 2 COA + 2 C02 biol 2.3.1.47 8-Amino-7-oxononanoate synthase ALA+ CHCOA <-> C02 + COA + AONA biof YNRO58W 2.6.1.62 BI03 7,8-diamino-pelargonic acid aminotransferase (DAPA) SAM
+ AONA <-> SAMOB + DANNA bio3 aminotransferase YNR057C 6.3.3.3 BI04 dethiobiotin synthetase C02+DANNA+ATP<_>DTB+PI+ADP bio4 YGR286C 2.8.1.6 B102 Biotin synthase DTB + CYS <-> BT bio2 Folate biosynthesis YGR267C 3.5.4.16 fol2 GTP cyclohydrolase I GTP -> FOR + AHTD fol2 3.6.1.- Dihydroneopterintriphosphatepyrophosphorylase AHTD -> PPI + DHPP ntpa YDR481C 3.1.3.1 pho8 Glycerophosphatase, Alkaline phosphatase; Nucleoside AHTD
-> DHP + 3 PI pho8 triphosphatase YDLi000 3.6.1.- YDLl00 Dihydroneopterin monophosphate dephosphorylase DHPP ->DHP+PI dhdnpa C
YNL256W 4.1.2.25 foil Dihydroneopterin aldolase DHP -> AHHMP + GLAL fo1l_1 YNL256W 2.7.6.3 fall 6-Hydroxymethyl-7,8 dihydropterin pyrophosphokinase AHHMP
+ATP -> AMP + AHHMD foll-2 YNR033W 4.1.3.- ABZI Aminodeoxychorismate synthase CHOR + GLN -> ADCHOR + GLU
abzl 4.-.-.- Aminodeoxychorismate lyase ADCHOR -> PYR + PABA pabc YNL256W 2.5.1.15 foil Dihydropteroate synthase PABA+AHHMD->PPI+DHPT foil _3 YNL256W 2.5.1.15 fall Dihydropteroate synthase PABA+AHHMP -> DHPT foil-4 6.3.2.12 Dihydrofolate synthase DHPT + ATP + GLU -> ADP + PI + DHF folc YOR236W 1.5.1.3 dfrl Dihydrofolatereductase DHFm+NADPHm->NADPm+THFm dfrl_1 YOR236W 1.5.1.3 dfrl Dihydrofolate reductase DHF + NADPH -> NADP + THF dfrl-2 6.3.3.2 5-Formyltetrahydrofolatecyclo-ligase ATPm+FTHFm->ADPm+Phn+MTHFm ftfa 6.3.3.2 5-Formyltetrahydrofolatecyclo-ligase ATP + FTHF -> ADP + PI + MTHF fib YKLI32C 6.3.2.17 RMAI Protein with similarity to folylpolyglutamate synthase;
THF + ATP + GLU <-> ADP + PI + THFG mtal converts tetrahydrofolyl-[Glu(n)] + glutamate to tetrahydrofolyl-[Glu(n+l)]
YMRI13W 6.3.2.17 FOL3 Dihydrofolate synthetase THF + ATP + GLU <-> ADP + PI +
THFG fol3 YOR241W 6.3.2.17 MET7 Folylpolyglutamate synthetase, involved in methionine THF + ATP + GLU <-> ADP + PI + THFG met7 biosynthesis and maintenance of mitochondrial genome One carbon pool by foiate IMAP:006701 YPL023C 1.5.1.20 MET12 Methylene tetrahydrofolate reductase METTHFm+NADPHm->NADPm+MTHFm met12 YGL125W 1.5.1.20 met13 Methylene tetrahydrofolatereductase METTHFm+NADPHm->NADPm+MTHFm met13 YBR084W 1.5.1.5 mist the mitochondrial trifunctional enzyme Cl- METTHFm +
NADPm <-> METHFm+NADPHm misl_1 tetrahydroflate synthase YGR204W 1.5.1.5 ade3 the cytoplasmic trifunctional enzyme Cl- METTHF + NADP <-> METHF+NADPH ade3_1 tetrahydrofolate synthase YBRO84W 6.3.4.3 mist the mitochondrial trifunctional enzyme Cl- THFm+FORm+ATPm->ADPm+Plan +FTHFm misl-2 tetrahydroflate synthase YGR204W 6.3.4.3 ade3 the cytoplasmic trifunctional enzyme Cl- THE+FOR+ATP ->
ADP+PI+FTHF ade3 2 tetrahydrofolate synthase YBRO84W 3.5.4.9 mist the mitochondrial trifunctional enzyme Cl- METHFm <->
FTHFm mist-3 tetrahydroflate synthase YGR204W 3.5.4.9 ade3 the cytoplasmic trifunctional enzyme C 1- METHF <-> FTHF
ade3-3 tetrahydrofolate synthase YKRO80W 1.5.1.15 MTD1 NAD-dependent 5,l0-methylenetetrahydrafolate METTHF +
NAD -> METHF + NADH mtdl dehydrogenase YBLO13W 2.1.2.9 fmtl Methionyl-tRNATransformylase FTHFm+MTRNAm->THFm+FMRNAm fmtl Coenzyme A Biosynthesis YBR176W 2.1.2.11 ECM31 Ketopentoate hydroxymethyl transferase OIVAL + METTHF -> AKP + THF ecm31 YHR063C 1.1.1.169 PANS Putative ketopantoate reductase (2-dehydropantoate 2-AKP +NADPH -> NADP + PANT pane reductase) involved in coenzyme A synthesis, has similarity to Cbs2p, Ketopantoate reductase YLR355C 1.1.1.86 ilvS Ketol-acid reductoisomerase AKPm+NADPHm->NADPm+PANTm ilv5 3 YIL145C 6.3.2.1 YIL145C Pantoate-b-alanine ligase PANT + bALA + ATP -> AMP +
PPI+ PNTO panca YDR531 W 2.7.1.33 YDR531 Putative pantothenate kinase involved in coenzyme A
PNTO + ATP -> ADP + 4PPNTO coca W biosynthesis, Pantothenate kinase 6.3.2.5 Phosphopantothenate-cysteine ligase 4PPNTO + CTP + CYS -> CMP +
PPI+4PPNCYS pclig 4.1.1.36 Phosphopantothenate-cysteine decarboxylase 4PPNCYS -> C02 + 4PPNTE
pcdcl 2.7.7.3 Phospho-pantethiene adenylyltransferase 4PPNTE + ATP -> PPI+ DPCOA
patrana 2.7.7.3 Phospho-pantethiene adenylyltransferase 4PPNTEm + ATPm -> PPIm +
DPCOAm patranb 2.7.1.24 DephosphoCoA kinase DPCOA + ATP -> ADP + COA dphcoaka 2.7.1.24 DephosphoCoA kinase DPCOAm + ATPm -> ADPm + COAm dphcoakb 4.1.1.11 ASPARTATE ALPHA-DECARBOXYLASE ASP -> C02 + bALA pancb YPL148C 2.7.8.7 PPT2 Acyl carrier-protein synthase, phosphopantetheine COA ->
PAP + ACP asps protein transferase for Acplp NAD Biosynthesis YGL037C 3.5.1.19 PNC1 Nicotinamidase NAM<->NAC+NH3 nadh YOR209C 2.4.2.11 NPT1 NAPRTase NAC +PRPP -> NAMN + PPI nptl 1.4.3.- Aspartate oxidase ASP+FADm->FADH2m+ISUCC nadb 1.4.3.16 Quinolate synthase ISUCC + T3P2 -> PI + QA nada YFR047C 2.4.2.19 QPT1 Quinolate phosphoribosyl transferase QA+PRPP->
NAMN+C02+PPI nadc YLR328W 2.7.7.18 YLR328 Nicotinamide mononucleotide (NMN) NAMN +ATP ->
PPI+NAAD nadd l W adenylyltransferase YHR074W 6.3.5.1 QNSI Deamido-NAD ammonia ligase NAAD+ATP+NH3-> NAD+AMP+PPI
nade YJR049c 2.7.1.23 utrl NAD kinase, POLYPHOSPHATE KINASE (EC NAD + ATP -> NADP +
ADP nadf 1 2.7.4.1) / NAD+ KINASE (EC 2.7.1.23) YEL041w 2.7.1.23 YEL041 NAD kinase, POLYPHOSPHATE KINASE (EC NAD + ATP -> NADP
+ ADP nadf 2 w 2.7.4.1) / NAD+ KINASE (EC 2.7.1.23) YPL188w 2.7.1.23 POSS NAD kinase, POLYPHOSPHATE KINASE (EC NAD + ATP -> NADP +
ADP nadf 5 2.7.4.1) / NAD+ KINASE (EC 2.7.1.23) 3.1.2.- NADP phosphatase NADP -> NAD + PI nadphps 3.2.2.5 NAD -> NAM + ADPRIB nadi 2.4.2.1 strong similarity to purine-nucleoside phosphorylases ADN + PI <-> AD
+ RIP nadgl 2.4.2.1 strong similarity to purine-nucleoside phosphorylases GSN + PI <-> GN
+ RIP nadg2 Nicotinic Acid synthesis from TRP
YFR047C 2.4.2.19 QPTI Quinolate phosphoribosyl transferase QAm + PRPPm ->
NAMNm + C02m + PPIm mnadc YLR328W 2.7.7.18 YLR328 NAMNadenyiyltransferase NAMNm + ATPm -> PPIm + NAADm mnaddl w YLR328W 2.7.7.18 YLR328 NAMN adenylyl transferase NMNm + ATPm -> NADm + PPIm mnadc2 W
YHR074W 6.3.5.1 QNSI Deamido-NAD ammonia ligase NAADm+ATPm+NH3m->NADm+AMPm+
mnade PPIm YJR049c 2.7.1.23 utrl NAD kinase, POLYPHOSPHATE KINASE (EC NADm+ATPm->
NADPm+ADPm mnadf 1 2.7.4.1) / NAD+ KINASE (EC 2.7.1.23) YPL188w 2.7.1.23 POSS NAD kinase, POLYPHOSPHATE KINASE (EC NADm + ATPm ->
NADPm + ADPm mnadf 2 2.7.4.1) / NAD+ KINASE (EC 2.7.1.23) YEL04Iw 2.7.1.23 YEL041 NAD kinase, POLYPHOSPHATE KINASE (EC NADm+ ATPm ->
NADPm + ADPm mnadf 5 w 2.7.4. 1) / NAD+ KINASE (EC 2.7.1.23) 3.1.2.- NADP phosphatase NADPm -> NADm + PIm mnadphps YLR209C 2.4.2.1 PNPI strong similarity to purine-nucleoside phosphorylases ADNm + Plan <-> ADm + RIPm mnadgl YLR209C 2.4.2.1 PNP1 strong similarity to purine-nucleoside phosphorylases GSNm+Plan <->GNm+RIPm mnadg2 YGL037C 3.5.1.19 PNC1 Nicotinamidase NAMm<->NACm+NH3m mnadh YOR209C 2.4.2.11 NPTI NAPRTase NACm + PRPPm -> NAMNm + PPIm mnptl 3.2.2.5 NADm -> NAMm + ADPRIBm mnadi Uptake Pathways Porphyrin and Chlorophyll Metabolism YDR232W 2.3.1.37 heml 5-Aminolevulinate synthase SUCCOAm + GLYm -> ALAVm +
COAm + C02m heml YGL040C 4.2.1.24 HEM2 Aminolevulinate dehydratase 2 ALAV -> PEG hem2 YDL205C 4.3.1.8 HEM3 Hydroxymethylbilane synthase 4 PBG -> HMB + 4 NH3 hem3 YOR278W 4.2.1.75 HEM4 Uroporphyrinogen-III synthase HMB -> UPRG hem4 YDR047W 4.1.1.37 HEM12 Uroporphyrinogendecarboxylase UPRG -> 4C02+CPP hem12 YDR044W 1.3.3.3 HEM13 Coproporphyrinogen oxidase, aerobic 02 + CPP -> 2 C02 +
PPHG hem 13 YER014W 1.3.3.4 HEM14 Protoporphyrinogen oxidase 02+PPHGm->PPIXm hem14 YOR176W 4.99.1.1 HEM15 Ferrochelatase PPIXm ->PTHm hem15 YGL245W 6.1.1.17 YGL245 glutamyl-tRNA synthetase, cytoplasmic GLU + ATP ->
GTRNA + AMP + PPI unrxnl0 W
YOL033W 6.1.1.17 MSE1 GLUm+ATPm->GTRNAm+AMPm+PPIm msel YKR069W 2.1.1.107 met) uroporphyrin-III C-methyltransferase SAM + UPRG -> SAH
+ PC2 met) Quinone Biosynthesis YKL211C 4.1.3.27 trp3 anthranilate synthase Component II and indole-3- CHOR ->
4HBZ + PYR trp3_3 phosphate (multifunctional enzyme) YER090W 4.1.3.27 trp2 anthranilate synthase Component I CHOR -> 4HBZ + PYR
trp2_2 YPR176C 2.5.1: BET2 geranylgeranyltransferase type II beta subunit 4HBZ +NPP -> N4HBZ + PPI bet2 YJL031C 2.5.1.- BET4 geranylgeranyltransferase type II alpha subunit YGL155W 2.5.1: cdc43 geranylgeranyltransferase type I beta subunit YBRO03W 2.5.1: COQ1 Hexaprenyl pyrophosphate synthetase, catalyzes the 4HBZ+NPP ->N4HBZ+PPI coq) first step in coenzyme Q (ubiquinone) biosynthesis pathway YNR041C 2.5.1: COQ2 pars-hydroxybenzoate--polyprenyltransferase 4HBZ + NPP ->
N4HBZ + PPI coq2 YPL172C 2.5.1: COX10 protohemeIXfarnesyltransferase,mitochondrial 4HBZ+NPP->N4HBZ+PPI cox10 precursor YDL090C 2.5.1: ram) protein famesyltransferase beta subunit 4HBZ +NPP -> N4HBZ
+ PPI ram) YKLO19W 2.5.1: RAM2 protein famesyltransferase alpha subunit YBRO02C 2.5.1: RER2 putative dehydrodolichyl diphospate synthetase 4HBZ+NPP ->N4HBZ+PPI rer2 YMR101C 2.5.1: SRTI putative dehydrodolichyl diphospate synthetase 4HBZ + NPP -> N4HBZ + PPI srtl YDR538W 4.1.1: PADI Octaprenyt-hydroxybenzoatedecarboxylase N4HBZ -> C02 +
2NPPP padl_2 1.13.14.- 2-Octaprenylphenol hydroxylase 2NPPP + 02 -> 2N6H ubib YPL266W 2.1.1: DIMl 2N6H+SAM->2NPMP+SAH dim) 1.14.13: 2-Octaprenyl-6-methoxyphenol hydroxylase 2NPMPm + 02m -> 2NPMBm ubih YML110C 2.1.1: COQ5 2-Octaprenyl-6-methoxy-1,4-benzoquinone methylase 2NPMBm+
SAMm -> 2NPMMBm + SAHm coq5 YGR255C 1.14.13: COQ6 COQ6 monooxygenase 2NPMMBm + 02m -> 2NMHMBm coq6b YOL096C 2.1.1.64 COQ3 3-Dimethylubiquinone 3-methyltransferase 2NMHMBm + SAMm -> QH2m + SAHm ubig Memberane Transport Mltochondiral Membrane Transport The followings diffuse through the inner milochondiral membrane in a non-carrier-mediated manner:
02 <-> 02m mot C02 <-> C02m mco2 ETH <-> ETHm meth NH3 <-> NH3 in mnh3 MTHN <-> MTHNm mmthn THFm <->THE mthf METTHFm <-> METTHF mmthf SERm <-> SER mser GLYm <-> GLY mgly CBHCAPm <-> CBHCAP mcbh OICAPm <-> OICAP moicap PROm <-> PRO mpro CMPm <-> CMP mcmp ACm <-> AC mac ACAR -> ACARm macar_ CARm -> CAR mcar ACLAC <-> ACLACm maclac ACTAC <-> ACTACm mactc SLF -> SLFm + Hm mslf THRm <-> THR mthr AKAm->AKA maka YMR056c AAC1 ADP/ATP carrier protein (MCF) ADP+ATPm+PI->Hm+ADPm+ATP+Plan aacl YBL030C pet9 ADP/ATP carrier protein (MCF) ADP+ATPm+PI->Hirt +ADPm+ATP+PIm pet9 YBRO85w AAC3 ADP/ATP carrier protein (MCF) ADP + ATPm + PI -> Hm + ADPm + ATP
+ Phn aac3 YJR077C MIRI phosphate carrier PI<->Hm+Plan mirl a YER053C YER053 similarity to C.elegans mitochondria) phosphate carrier PI+ OHm <-> PIm mirld C
YLR348C DICI dicarboxylate carrier MAL + SUCCm <-> MALm + SUCC diel_1 YLR348C DICI dicarboxylate carrier MAL+PIm<-> MALm+PI dicl_2 YLR348C DICI dicarboxylate carrier SUCC+PIm->SUCCm+PI dicl_3 MALT + PIm <-> MALTm + PI mmlt YKL120W OACI Mitochondria! oxaloacetate carrier OA <-> OAm + Hin mesh YBR291C CTP1 citrate transport protein CIT+MALm<->CITm+MAL ctpl_1 YBR29IC CTP1 citrate transport protein CIT+PEPm<-> CITm+PEP ctpl_2 YBR291C CTPI citrate transport protein CIT+ICITm<->CITm+ICIT ctpl_3 !PPMAL <-> IPPMALm mpmalR
LAC <-> LACm + Hm mlac pyruvate carrier PYR <-> PYRm+Hm pyrca glutamate carrier GLU <-> GLUm+Hm gca GLU + OHm -> GLUm gcb YOR130C ORTI ornithine carrier ORN + Hm <-> ORNm ortl YOR100C CRCI camitine carrier CARm+ACAR->CAR+ACARm crcl OIVAL <-> OIVALm moival OMVAL <-> OMVALm momval YIL134W FLXI Protein involved in transport of FAD from cytosol into FAD + FMNm -> FADm + FMN mfad the mitochondria) matrix RIBFLAV <-> RIBFLAVm mribo DTB <-> DTBm mdtb H3MCOA <-> H3MCOAm mmcoa MVL <-> MVLm mmvl PA <-> PAm mpa 4PPNTE <-> 4PPNTEm mppnt AD <-> ADm mad PRPP <-> PRPPm mprpp DHF <-> DHFm mdhf QA <-> QAm mqa OPP <-> OPPm mopp SAM <-> SAMm msam SAH <-> SAHm msah YJR095 W SFC 1 Mitochondrial membrane succinate-fumarate SUCC + FUMm -> SUCCm + FUM sfcl transporter, member of the mitochondria) carrier family (MCF) of membrane transporters YPL134C ODC1 2-oxodicarboylate transporter AKGm+OXA<->AKG+OXAm odcl YOR222W ODC2 2-oxodicarboylate transporter AKGm + OXA <-> AKG + OXAm odc2 Malate Aspartate Shuttle Included elsewhere Glycerol phosphate shuttle T3P2m -> T3P2 mt3p GL3P -> GL3Pm mgl3p Plasma Membrane Transport Carbohydrates YHR092c HXT4 moderate- to low-affinity glucose transporter GLCxt -> GLC hxt4 YLRO81w GAL2 galactose (and glucose) permease GLCxt -> GLC gal2_3 YOLI56w HXTI I low affinity glucose transport protein GLCxt -> GLC hxtl l YDR536W stll Protein member of the hexose transporter family GLCxt -> GLC
stll_1 YHR094c hxtl High-affinity hexose (glucose) transporter GLCxt -> GLC hxtl_l YOL156w HXTII Glucose permease GLCxt -> GLC hxtll_1 YEL069c HXT13 high-affinity hexose transporter GLCxt -> GLC hxtl3_I
YDL245c HXT15 Hexose transporter GLCxt -> GLC hxtl5_1 YJR158w HXT16 hexose permease GLCxt -> GLC hxtl6_1 YFLO11w HXTIO high-affinity hexose transporter GLCxt-> GLC hxtlO_I
YNR072w HXT17 Putative hexose transporter GLCxt -> GLC hxtl7_1 YMR011w HXT2 high affinity hexose transporter-2 GLCxt -> GLC hxt2_I
YHR092c hxt4 High-affinity glucose transporter GLCxt -> GLC hxt4_1 YDR345c hxt3 Low-affinity glucose transporter GLCxt -> GLC hxt3_1 YHR096c HXT5 hexose transporter GLCxt -> GLC hxt5_I
YDR343c HXT6 Hexose transporter GLCxt -> GLC hxt6_1 YDR342c HXT7 Hexose transporter GLCxt -> GLC hxt7_1 YJL214w HXT8 hexose permease GLCxt -> GLC hxt8_4 YJL219w HXT9 hexose permease GLCxt -> GLC hxt9_1 YLR081w gall galactose permease GLACxt + HEXT -> GLAC gal2_1 YFLOI lw HXT10 high-affinity hexose transporter GLACxt+ HEXT -> GLAC hxtlO_4 YOL156w HXTI 1 Glucose permease GLACxt + HEXT -> GLAC hxtl 14 YNL318c HXT14 Member of the hexose transporter family GLACxt + HEXT -> GLAC
hxtl4 YJL219w HXT9 hexose permease GLACxt+ HEXT -> GLAC hxt9_4 YDR536W stl l Protein member of the hexose transporter family GLACxt+ HEXT ->
GLAC stll_4 YFL055w AGP3 Amino acid permease for serine, aspartate, and GLUxt + HEXT <->
GLU agp3 3 glutamate YDR536W stl l Protein member of the hexose transporter family GLUxt + HEXT <->
GLU stll_2 YKR039W gapI General amino acid permease GLUxt + HEXT <-> GLU gapS
YCL025C AGPI Amino acid permease for most neutral amino acids GLUxt + HEXT <->
GLU gap24 YPL265W DIPS Dicarboxylic amino acid permease GLUxt + HEXT <-> GLU diplO
YDR536W stll Protein member of the hexose transporter family GLUxt + HEXT <->
GLU stll_3 YHR094c hxtl High-affinity hexose (glucose) transporter FRUxt + HEXT -> FRU
hxtl_2 YFLO11w HXT10 high-affinity hexose transporter FRUxt + HEXT -> FRU hxt10_2 YOL156w HXT11 Glucose permease FRUxt + HEXT -> FRU hxtll 2 YEL069c HXT13 high-affinity hexose transporter FRUxt+HEXT-> FRU hxtl3__2 YDL245c HXT15 Hexose transporter FRUxt+ HEXT -> FRU hxtl5_2 YJRI58w HXT16 hexose permease FRUxt + HEXT -> FRU hxtl6_2 YNR072w HXTI7 Putative hexose transporter FRUxt + HEXT -> FRU hxtl7_2 YMROI1w HXT2 high affinity hexose transporter-2 FRUxt + HEXT -> FRU hxt2_2 YDR345c hxt3 Low-affinity glucose transporter FRUxt + HEXT -> FRU hxt3_2 YHR092c hxt4 High-affinity glucose transporter FRUxt + HEXT -> FRU hxt4_2 YHR096c HXT5 hexose transporter FRUxt + HEXT -> FRU hxt5_2 YDR343c HXT6 Hexose transporter FRUxt + HEXT -> FRU hxt6_2 YDR342c HXT7 Hexose transporter FRUxt + HEXT -> FRU hxt7_2 YJL2I4w HXT8 hexose permease FRUxt + HEXT -> FRU hxt8_5 YJL219w HXT9 hexosepermease FRUxt + HEXT -> FRU hxt9_2 YHR094c hxtl High-affinity hexose (glucose) transporter MANxt+HEXT-> MAN
hxtl_5 YFLOI1w HXTIO high-affinity hexose transporter MANxt + HEXT -> MAN hxtlO_3 YOL156w HXTI1 Glucosepermease MANxt+HEXT-> MAN hxtll 3 YEL069c HXT13 high-affinity hexose transporter MANxt + HEXT -> MAN hxt13 3 YDL245c HXT15 Hexose transporter MANxt+HEXT->MAN hxtl5_3 YJR158w HXTI6 hexose permease MANxt+HEXT-> MAN hxtl6_3 YNR072w HXT17 Putative hexose transporter MANxt+HEXT->MAN hxtl7_3 YMR011w HXT2 high affinity hexose transporter-2 MANxt+HEXT->MAN hxt2 3 YDR345c hxt3 Low-affinity glucose transporter MANxt+HEXT->MAN hxt3_3 YHR092c hxt4 High-affinity glucose transporter MANxt+HEXT->MAN hxt4 3 YHR096c HXT5 hexose transporter MANxt+HEXT->MAN hxt5 3 YDR343c HXT6 Hexose transporter MANxt+HEXT->MAN hxt6 3 YDR342c HXT7 Hexose transporter MANxt + HEXT -> MAN hxt7_3 YJL214w HXT8 hexose permease MANxt+HEXT->MAN hxt8_6 YJL219w HXT9 hexose permease MANxt+HEXT->MAN hxt9_3 YDR497c ITRI myo-inositol transporter MIxt+HEXT->MI itri YOL103w ITR2 myo-inositol transporter M!xt + NEXT -> MI itr2 Maltase permease MLTxt + HEXT -> MLT mltup YIL162W 3.2.1.26 SUC2 invertase (sucrose hydrolyzing enzyme) SUCxt -> GLCxt+
FRUxt suc2 sucrose SUCxt + HEXT -> SUC sucup YBR298c MAL31 Dicarboxylates MALxt+ HEXT <-> MAL mal3l a-Ketoglutarate/malate translocator MALxt + AKG <-> MAL + AKGxt akmup a-methylglucoside AMGxt <-> AMG amgup Sorbose SORxt <-> SOR sorup Arabinose (low affinity) ARABxt <-> ARAB arbup 1 Fucose FUCxt + HEXT <-> FUC fucup GLTLxt + HEXT -> GLTL gltlupb Glucitol GLTxt + HEXT -> GLT gltup Glucosamine GLAMxt + HEXT <-> GLAM gaup YLL043W FPSI Glycerol GLxt<->GL glup YKL217W JEN1 Lactate transport LACxt + HEXT <-> LAC lacup 1 Mannitol MNTxt+HEXT -> MNT mntup Melibiose MELIxt+ HEXT -> MELI melup_l N-Acetylglucosamine NAGxt + HEXT -> NAG nagup Rhamnose RMNxt + HEXT -> RMN mrnup Ribose RIBxt + HEXT -> RIB ribup Trehalose TRExt + HEXT -> THE treup_1 TRExt -> AATRE6P treup_2 XYLxt <-> XYL xylup Amino Acids YKR039W gapI General amino acid permease ALAxt + HEXT <-> ALA gapl_1 YPL265W DIPS Dicarboxylic amino acid permease ALAxt+HEXT<->ALA dip5 YCL025C AGPI Amino acid permease for most neutral amino acids ALAxt + HEXT <->
ALA gap25 YOL02OW TAT2 Tryptophan permease ALAxt + HEXT <-> ALA tat5 YOR348C PUT4 Proline permease ALAxt+ HEXT <-> ALA put4 YKR039W gapI General amino acid permease ARGxt +HEXT <-> ARG gap2 YEL063C canl Permease for basic amino acids ARGxt + HEXT <-> ARG canl_I
YNL270C ALP I Protein with strong similarity to permeases ARGxt + HEXT <-> ARG
alp!
YKR039W gapI General amino acid permease ASNxt + HEXT <-> ASN gap3 YCL025C AGP 1 Amino acid permease for most neutral amino acids ASNxt + HEXT <-> ASN gap21 YDR508C GNP! Glutaminepermease(high affinity) ASNxt + HEXT <-> ASN gnp2 YPL265W DIPS Dicarboxylic amino acid permease ASNxt+HEXT <-> ASN dip6 YFLO55W AGP3 Amino acid permease for serine, aspartate, and ASPxt + HEXT <->
ASP agp3_2 glutamate YKR039W gap! General amino acid permease ASPxt + HEXT <-> ASP gap4 YPL265W DIPS Dicarboxylic amino acid permease ASPxt+ HEXT <-> ASP dip?
YKR039W gap 1 General amino acid permease CYSxt+HEXT<->CYS gap5 YDR508C GNP1 Glutamine permease (high affinity) CYSxt+HEXT <-> CYS gnp3 YBR068C BAP2 Branched chain amino acid permmease CYSxt + HEXT <-> CYS bap2_1 YDR046C BAP3 Branched chain amino acid permease CYSxt+HEXT <-> CYS bap3_1 YBR069C VAP l Amino acid permease CYSxt+HEXT <-> CYS vap7 YOL02OW TAT2 Tryptophan permease CYSxt + HEXT <-> CYS tat7 YKR039W gapI General amino acid permease GLYxt + HEXT <-> GLY gap6 YOL02OW TAT2 Tryptophan permease GLYxt+HEXT <-> GLY tat6 YPL265W DIPS Dicarboxylic amino acid permease GLYxt+HEXT <-> GLY dips YOR348C PUT4 Proline permease GLYxt + HEXT <-> GLY puts YKR039W gapI General amino acid permease GLNxt + HEXT <-> GLN gap7 YCL025C AGP1 Amino acid permease for most neutral amino acids GLNxt+HEXT <->
GLN gap22 YDR508C GNPI Glutamine permease (high affinity) GLNxt + HEXT <-> GLN gnpl YPL265W DIPS Dicarboxylic amino acid permease GLNxt + HEXT <-> GLN dip9 YGRI9IW HIPI Histidinepermease HISxt + HEXT <-> HIS hipl YKR039W gap 1 General amino acid permease HISxt + HEXT <-> HIS gap9 YCL025C AGPI Amino acid permease for most neutral amino acids HISxt+ HEXT <->
HIS gap23 YBR069C VAP1 Amino acid permease N!Sxt + NEXT <> HIS vap6 YBR069C TAT! Amino acid permease that transports valine, leucine, ILExt+ HEXT
<-> ILE tat!-2 isleucine, tyrosine, tryptophan, and threonine YKR039W gap! General amino acid permease ILExt+ HEXT <-> ILE gaplO
YCL025C AGP 1 Amino acid permease for most neutral amino acids ILExt+ HEXT <->
ILE gap32 YBR068C BAP2 Branched chain amino acid permease ILExt + HEXT <-> ILE bap2_2 YDR046C BAP3 Branched chain amino acid pennease ILExt + HEXT<-> ILE bap3_2 YBR069C VAPI Amino acid permease ILExt+ HEXT <-> ILE vap3 YBR069C TATI Amino acid permease that transports valine, leucine, LEUxt+HEXT <-> LEU tatl 3 isleucine, tyrosine, tryptophan, and threonine YKR039W gap! General amino acid permease LEUxt + HEXT <-> LEU gapl 1 YCL025C AGP1 Amino acid permease for most neutral amino acids LEUxt+HEXT <->
LEU gap33 YBR068C BAP2 Branched chain amino acid pennease LEUxt+HEXT <-> LEU bap2_3 YDR046C BAP3 Branched chain amino acid permease LEUxt+ HEXT <-> LEU bap3_3 YBR069C VAP! Amino acid permease LEUxt + HEXT <-> LEU vap4 YDR508C GNP I Glutamine permease (high affinity) LEUxt + HEXT <-> LEU gnp7 YKR039W gap! General amino acid permease METxt + HEXT <-> MET gapl3 YCL025C AGP 1 Amino acid permease for most neutral amino acids METxt + HEXT <-> MET gap26 YDR508C GNP 1 Glutamine permease (high affinity) METxt + HEXT <-> MET gnp4 YBR068C BAP2 Branched chain amino acid permease METxt + HEXT <-> MET bap2_4 YDR046C BAP3 Branched chain amino acid pennease METxt + HEXT <-> MET bap3_4 YGRO55W MUP 1 High-affinity methionine permease METxt+ HEXT <-> MET mupl YHL036W MUP3 Low-affinity methionine permease METxt + HEXT <-> MET mup3 YKR039W gap 1 General amino acid permease PHExt + HEXT <-> PHEN gap 14 YCL025C AGP1 Amino acid permease for most neutral amino acids PHExt+HEXT <->
PHEN gap29 YOL02OW TAT2 Tryptophan permease PHExt + HEXT <-> PHEN tat4 YBR068C BAP2 Branched chain amino acid permease PHExt+HEXT <-> PHEN bap2_5 YDR046C BAP3 Branched chain amino acid permease PHExt+HEXT <-> PHEN bap3_5 YKR039W gap! General amino acid permease PROxt+HEXT <-> PRO gap15 YOR348C PUT4 Proline permease PROxt+ HEXT <-> PRO put6 YBR069C TATI Amino acid permease that transports valine, leucine, TYPxt+ HEXT
<-> TRP tatl_6 isleucine, tyrosine, tryptophan, and threonine YKR039W gap' General amino acid permease TRPxt4- NEXT <-> TRP gapl8 YBR069C VAPI Amino acid permease TRPxt + HEXT <-> TRP vap2 YOL02OW TAT2 Tryptophan permease TRPxt+HEXT <-> TRP tat3 YBR068C BAP2 Branched chain amino acid permease TRPxt + HEXT <-> TRP bap2 6 YDR046C BAP3 Branched chain amino acid permease TYPxt+HEXT <-> TRP bap3_6 YBR069C TATI Amino acid permease that transports valine, leucine, TYRxt+HEXT <-> TYR tatl_7 isleucine, tyrosine, tryptophan, and threonine YKR039W gap! General amino acid permease TYRxt+HEXT <-> TYR gap19 YCL025C AGPI Amino acid permease for most neutral amino acids TYRxt + HEXT <->
TYR gap28 YBR068C BAP2 Branched chain amino acid permease TYRxt + HEXT <-> TYR bap2_7 YBR069C VAPI Amino acid permease TYRxt + HEXT <-> TYR vapI
YOL02OW TAT2 Tryptophanpermease TYRxt + HEXT <-> TYR tat2 YDR046C BAP3 Branched chain amino acid permease TYRxt + HEXT <-> TYR bap3_7 YKR039W gap 1 General amino acid permease VALxt + HEXT <-> VAL gap20 YCL025C AGPI Amino acid permease for most neutral amino acids VALxt+HEXT <->
VAL gap31 YDR046C BAP3 Branched chain amino acid permease VALxt+HEXT <-> VAL bap3_8 YBR069C VAPI Amino acid permease VALxt + HEXT <-> VAL vap5 YBR068C BAP2 Branched chain amino acid permease VALxt+HEXT <-> VAL bap2_8 YFL055 W AGP3 Amino acid permease for serine, aspartate, and SERxt + HEXT <->
SER agp3_1 glutamate YCL025C AGP 1 Amino acid permease for most neutral amino acids SERxt + HEXT <-> SER gap27 YDR508C GNP! Glutamine permease (high affinity) SERxt + HEXT <-> SER gnp5 YKR039W gap! General amino acid permease SERxt+HEXT <-> SER gap16 YPL265W DIPS Dicarboxylicamino acid permease SERxt + HEXT <-> SER dipl' YBR069C TATI Amino acid permease that transports valine, leucine, THRxt+HEXT <-> THR tat'_1 isleucine, tyrosine, tryptophan, and threonine YCL025C AGP 1 Amino acid permease for most neutral amino acids THRxt+ HEXT <->
THR gap30 YKR039W gap! General amino acid permease THRxt+HEXT <-> THR gap17 YDR508C GNP 1 Glutamine permease (high affinity) THRxt+ HEXT <-> THR gnp6 YNL268W LYPI Lysine specific permease (high affinity) LYSxt+HEXT <-> LYS lypl YKR039W gap! General amino acid permease LYSxt+HEXT <-> LYS gap 12 YLL061 W MMP1 High affinity S-methylmethionine permease MMETxt + HEXT -> MMET
mmpl YPL274W SAM3 High affinity S-adenosylmethionine permease SAMxt+HEXT -> SAM
sam3 YOR348C PUT4 Proline permease GABAxt + HEXT -> GABA put7 YDL21O W uga4 Amino acid permease with high specificity for GABA GABAxt+ HEXT -> GABA uga4 YBR132C AGP2 Plasma membrane camitine transporter CARxt <-> CAR agp2 YGLO77C HNMI Choline permease CHOxt + HEXT -> MET hnml YNRO56C BIOS transmembrane regulator of KAPA/DAPA transport BIOxt+HEXT->BIO
bio5a YDL21OW uga4 Amino acid permease with high specificity for GABA ALAVxt + HEXT -> ALAV uga5 YKR039W gap! General amino acid permease ORNxt+ HEXT <-> ORN gaplb YEL063C canl Permease for basic amino acids ORNxt + HEXT <-> ORN canlb Putrescine PTRSCxt + HEXT -> PTRSC ptrup Spermidine & putrescine SPRMDxt + HEXT -> SPRMD sprup 1 YKR093W PTR2 Dipeptide DIPEPxt+HEXT ->DIPEP ptr2 YKR093W PTR2 Oligopeptide OPEPxt+HEXT->OPEP ptr3 YKR093W PTR2 Peptide PEPTxt+HEXT->PEPT ptr4 YBR021W FUR4 Uracil URAxt + HEXT -> URA umupl Nicotinamide mononucleotide transporter NMNxt + HEXT -> NMN nmnup YER056C FCY2 Cytosine purine permease CYTSxt + HEXT -> CYTS fcy2_1 YER056C FCY2 Adenine ADxt+ HEXT -> AD fcy2_2 YER056C FCY2 Guanine GNxt + HEXT <-> GN fcy2_3 YER060W FCY21 Cytosine purine permease CYTSxt + HEXT -> CYTS fcy2l_l YER060W FCY21 Adenine ADxt+HEXT-> AD fcy2l 2 YER060W FCY21 Guanine GNxt+HEXT<-> GN fcy21_3 YER060W-A FCY22 Cytosine purine permeate CYTSxt + HEXT -> CYTS fcy22_1 YER060W-A FCY22 Adenine ADxt + HEXT -> AD fcy22_2 YER060W-A FCY22 Guanine GNxt+HEXT<-> GN fcy22_3 YGLI86C YGL186 Cytosine purine permease CYTSxt + HEXT -> CYTS cytup 1 C
YGL186C YGLI86 Adenine ADxt + HEXT -> AD adupI
C
YGL186C YGL186 Guanine GNxt + HEXT <-> GN gnup C
G-system ADNxt + HEXT -> ADN ncgup 1 G-system GSNxt + HEXT -> GSN ncgup3 YBL042C FUII Uridine permease, G-system URIxt+ HEXT -> URI uriup G-system CYTDxt + HEXT -> CYTD ncgup4 G-system (transports all nucleosides) INSxt+ HEXT -> INS ncgup5 G-system XTSINExt + HEXT -> XTSINE ncgup6 G-system DTxt+ HEXT -> DT ncgup7 G-system DINxt + HEXT -> DIN ncgup8 G-system DGxt + HEXT -> DG negup9 G-system DAxt + HEXT -> DA ncgup 10 G-system DCxt + HEXT -> DC ncgup l l G-system DUxt + HEXT -> DU ncgup 12 C-system ADNxt + HEXT -> ADN necupl YBL042C FUII Uridinepermeate,C-system URIxt+HEXT->URI nccup2 C-system CYTDxt + HEXT -> CYTD nccup3 C-system DTxt + HEXT -> DT nccup4 C-system DAxt+ HEXT -> DA nccup5 C-system DCxt + HEXT ->DC nccup6 C-system DUxt + HEXT -> DU nccup7 Nucleosides and deoxynucleoside ADNxt + HEXT -> ADN ncup 1 Nucleosides and deoxynucleoside GSNxt + HEXT -> GSN ncup2 YBL042C FUII Uridine permeate, Nucleosides and deoxynucleoside URlxt+ HEXT ->
URI ncup3 Nucleosides and deoxynucleoside CYTDxt + HEXT -> CYTD ncup4 Nucleosides and deoxynucleoside INSxt+ HEXT -> INS ncup5 Nucleosides and deoxynucleoside DTxt+HEXT -> DT ncup7 Nucleosides and deoxynucleoside DINxt + HEXT -> DIN ncup8 Nucleosides and deoxynucleoside DGxt+ HEXT -> DG ncgu9 Nucleosides and deoxynucleoside DAxt+ HEXT -> DA ncup 10 Nucleosides and deoxynucleoside DCxt + HEXT ->DC ncup 1 l Nucleosides and deoxynucleoside DUxt + HEXT -> DU ncupl2 Hypoxanthine HYXNxt+ HEXT <-> HYXN hyxnup Xanthine XANxt <-> XAN xanup Metabolic By-Products YCR032W BPHI Probable acetic acid export pump, Acetate transport ACxt+HEXT <->
AC acup Formate transport FORxt <-> FOR forup Ethanol transport ETHxt <-> ETH ethup Succinate transport SUCCxt + HEXT <-> SUCC succup YKL217W JEN1 Pyruvatelactate proton symport PYRxt + HEXT -> PYR jenl_l Other Compounds YHLO16C dur3 Urea active transport UREAxt + 2 HEXT <-> UREA dur3 YOR121C MEP1 Ammonia transport NH3xt<->NH3 mep1 YNL142W MEP2 Ammonia transport, low capacity high affinity NH3xt <-> NH3 mep2 YPR138C MEP3 Ammonia transport, high capacity low affinity NH3xt <-> NH3 mep3 YJLI29C trkl Potassium transporter of the plasma membrane, high Kxt + HEXT <->
K trkl affinity, member of the potassium transporter (TRK) family of membrane transporters YBR294W SUL1 Sulfate permease SLFxt -> SLF sull YLR092W SUL2 Sulfate permeate SLFxt -> SLF sul2 YGRI25W YGR125 Sulfate permease SLFxt -> SLF sulup W
YMLI23C pho84 inorganic phosphate transporter, transmembrane protein PIxt +
HEXT <-> PI pho84 Citrate CITxt + HEXT <-> CIT citup Dicarboxylates FUMxt + HEXT <-> FUM fumup Fatty acid transport C 140xt -> C140 faup 1 Fatty acid transport C 160xt -> C160 faup2 Fatty acid transport C161xt - Cl6l faup3 Fatty acid transport C180xt -> CI 80 faup4 Fatty acid transport C 181 xt -> CI 81 faup5 a-Ketoglutarate AKGxt + NEXT <-> AKG akgup YLR138W NHAI Putative Na+/H+ antiporter NAxt<->NA+HEXT nhal YCR028C FEN2 Pantothenate PNTOxt + HEXT <-> PNTO fen2 ATP drain flux for constant maintanence requirements ATP -> ADP + PI atpmt YCR024c-a PMPI H+-ATPase subunit, plasma membrane ATP -> ADP + PI + HEXT pmpl YELO 17c-a PMP2 H+-ATPase subunit, plasma membrane ATP -> ADP + PI + HEXT pmp2 YGLOO8c PMAI H+-transporting P-type ATPase, major isoform, plasma ATP -> ADP +
PI + HEXT pmal membrane YPL036w PMA2 H+-transporting P-type ATPase, minor isoform, plasma ATP -> ADP +
PI + HEXT pma2 membrane Glyceraldehyde transport GLALxt <-> GLAL glaltx Acetaldehyde transport ACALxt <-> ACAL acaltx YLR237W THI7 Thiamine transport protein THMxt + HEXT -> THIAMIN thml YOR071C YOR071 Probable low affinity thiamine transporter THMxt+HEXT ->
THIAMIN thm2 C
YOR192C YORI92 Probable low affinity thiamine transporter THMxt+ HEXT ->
THIAMIN thm3 C
YIR028W da14 ATNxt -> ATN dal4 YJRI52W dal5 ATTxt->ATT dal5 MTHNxt <-> MTHN mthup PAPxt <-> PAP papx DTTPxt <-> DTTP dttpx THYxt <-> THY + HEXT thyx GA6Pxt <-> GA6P ga6pup YGR065C VHTI H+Ibiotin symporter and member of the allantoate BTxt + HEXT <->
BT btup permease family of the major facilitator superfamily AONAxt + HEXT <-> AONA kapaup DANNAxt + NEXT <-> DANNA dapaup OGTxt -> OGT ogtup SPRMxt -> SPRM sprmup PIMExt -> PIME pimeup Oxygen transport O2xt <-> 02 o2tx Carbon dioxide transport CO2xt <-> C02 co2tx YORO 11 W AUS 1 ERGOSTxt <-> ERGOST ergup YOR011 W AUS 1 Putative sterol transporter ZYMSTxt <-> ZYMST zymup RFLAVxt + HEXT -> RIBFLAV rflup [00551 Standard chemical names for the acronyms used to identify the reactants in the reactions of Table 2 are provided in Table 3.

Abbreviation Metabolite 13GLUCAN 1,3-beta-D-Glucan 13PDG 3-Phospho-D-glyceroyl phosphate 23DAACP 2,3-Dehydroacyl-[acyl-carrier-protein]
23PDG 2,3-Bisphospho-D-glycerate 2HDACP Hexadecenoyl-[acp]
2MANPD ("alpha"-D-mannosyl)(,2)-"beta"-D-mannosyl-diacetylchitobio syldiphosphod olichol 2N6H 2-Nonaprenyl-6-hydroxyphenol Demethylubiqui none-9 2NMHMBm 3-Demethylubiqui none-9M

2NPMBm 2-Nonaprenyl-6-methoxy-l,4-benzoquinoneM
2NPMMBm 2-Nonaprenyl-3-methyl-6-methoxy-l,4-benzoquinoneM
2NPMP 2-Nonaprenyl-6-methoxyphenol 2NPMPm 2-Nonaprenyl-6-methoxyphenol M

Nonaprenylphen of 2PG 2-Phospho-D-glycerate 3DDAH7P 2-Dehydro-3-deoxy-D-arabino-heptonate 7-phosphate 3HPACP (3R)-3-Hydroxypalmito yl-[acyl-carrier protein]
3PG 3-Phospho-D-glycerate 3PSER 3-Phosphoserine 3PSME 5-0-(1-Carboxyvinyl)-phosphoshikima to Hydroxybenzoat e 4HLT 4-Hydroxy-L-threonine 4HPP 3-(4-Hydroxyphenyl) pyruvate 4PPNCYS (R)-4'-Phosphopantoth enoyl-L-cysteine 4PPNTE Pantetheine 4'-phosphate 4PPNTEm Pantetheine 4'-phosphateM
4PPNTO D-4'-Phosphopantoth enate 5MTA 5'-Methylthioaden osine 6DGLC D-Gal alpha I->6D-Glucose A6RP 5-Amino-6-ribitylamino-2,4 (I H, 3H)-pyrimidinedione A6RP5P 5-Amino-6-(5'-phosphoribosyla mino)umcil A6RP5P2 5-Amino-6-(5'-phosphoribityla mino)umcil AACCOA Acetoacetyl-CoA
AACP Acyl-[acyl-carrier-protein]
AATRE6P alpha,alpha'-Trehalose 6-phosphate ABUTm 2-Aceto-2-hydroxy butyrateM
AC Acetate ACACP Acyl-[acyl-carrier protein]
ACACPm Acyl-[acyl-carrier protein)M
ACAL Acetaldehyde ACALm AcetaldehydeM

Acetylcarnitine ACARm 0-Acetylcamitine M
ACCOA Acetyl-CoA
ACCOAm Acetyl-CoAM
ACLAC 2-Acetolactate ACLACm 2-AcetolactateM
Acm AcetateM

Indoleacetonitril e ACOA Acyl-CoA
ACP Acyl-carrier protein ACPm Acyl-carrier proteinM
ACTAC Acetoacetate ACTACm AcetoacetateM
ACYBUT gamma-Amino-gamma-cyanobutanoate AD Adenine ADCHOR 4-amino-4-deoxychorismat e Adm AdenineM
ADN Adenosine ADNm AdenonsineM
ADP ADP
ADPm ADPM
ADPRIB ADPribose ADPRIBm ADPriboseM
AGL3P Acyl-sn-glycerol 3-phosphate AHHMD 2-Amino-7,8-dihydro-4-hydroxy-6-(diphosphooxym ethyi)pteridine AHHMP 2-Amino-4-hydroxy-6-hydroxymethyl-7,8-dihydropteridine AHM 4-Amino-5-hydroxymethyl-methylpyrimidin e AHMP 4-Amino-2-methyl-5-phosphomethylp yrimidine AHMPP 2-Methyl-4-amino-5-hydroxymethylp yrimidine diphosphate AHTD 2-Amino-4-hydroxy-6-(erythro-1,2,3-trihydroxypropy I)-dihydropteridine triphosphate AICAR 1-(5'-Phosphoribosyl) -5-amino-4-imidazolecarbox amide AIR Aminoimidazole ribotide AKA 2-Oxoadipate AKAm 2-OxoadipateM

AKG 2-Oxoglutarate AKGm 2-OxoglutarateM

Dehydropantoat e AKPm 2-Dehydropantoat eM
ALA L-Alanine ALAGLY R-S-Alanylglycine ALAm L-AlanineM

Aminolevulinate ALAVm 5-Aminolevulinate M
ALTRNA L-Arginyl-tRNA(Arg) Aminomuconate 6-semialdehyde Aminoadipate Aminoadipate 6-semialdehyde AMG Methyl-D-glucoside AMP AMP
AMPm AMPM

Aminomuconate AN Anthranilate AONA 8-Amino-7-oxononanoate APEP Nalpha-Acetylpeptide Aminopropanal APROP alpha-Aminopropiono nitrile APRUT N-Acetylputrescine APS Adenylylsulfate ARAB D-Arabinose ARABLAC D-Arabinono-1,4-lactone ARG L-Arginine ARGSUCC N-(L-Arginino)succin ate ASER O-Acetyl-L-serine ASN L-Asparagine ASP L-Aspartate Acetylspermidin e ASPm L-AspartateM
ASPRM Nl-Acetylspermine ASPSA L-Aspartate 4-semialdehyde ASPTRNA L-Asparaginyl-tRNA(Asn) ASPTRNAm L-Asparaginyl-tRNA(Asn)M
ASUC N6-(1,2-Dicarboxyethyl) -AMP
AT3P2 Acyldihydroxya cetone phosphate ATN Allantoin ATP ATP
ATPm ATPM
ATRNA tRNA(Arg) ATRP P1,P4-Bis(5'-adenosyl) tetraphosphate ATT Allantoate bALA beta-Alanine BASP 4-Phospho-L-aspartate bDG6P beta-D-Glucose 6-phosphate bDGLC beta-D-Glucose BIO Biotin BT Biotin CIOOACP Decanoyl-[acp]
C120ACP Dodecanoyl-[acyl-carrier protein]
C120ACPm Dodecanoyl-[acyl-carrier protein]M
C140 Myristic acid C140ACP Myristoyl-[acyl-carrier protein]
C140ACPm Myristoyl-[acyl-carrier protein]M
C141ACP Tetradecenoyl-[acyl-carrier protein]
C141ACPm Tetradecenoyl-[acyl-carrier protein]M
C160 Palmitate C160ACP Hexadecanoyl-[acp]
C160ACPm Hexadecanoyl-[acp]M
C161 1-Hexadecene C161ACP Palmitoyl-[acyl-carrier protein]
C161ACPm Palmitoyl-[acyl-carrier protein]M
C16A C16_aldehydes C180 Stearate C180ACP Stearoyl-[acyl-carrier protein]
C180ACPm Stearoyl-[acyl-carrier protein]M
C181 1-Octadecene C18IACP Oleoyl-[acyl-carrier protein]
CI81ACPm Oleoyl-[acyl-carrier protein]M
C182ACP Linolenoyl-[acyl-carrier protein]
C182ACPm Linolenoyl-[acyl-carrier protein]M
CAASP N-Carbamoyl-L-aspartate CAIR 1-(5-Phospho-D-ribosyl)-5-amino-4-imidazolecarbox ylate CALH 2-(3-Carboxy-3-aminopropyl)-L-histidine cAMP 3',5'-Cyclic AMP
CAP Carbamoyl phosphate CAR Carnitine CARm CarnitineM

Isopropylmalate CBHCAPm 3-Isopropylmalate M
cCMP 3',5'-Cyclic CMP
cdAMP 3',5'-Cyclic dAMP
CDP CDP
CDPCHO CDPcholine CDPDG CDPdiacylglyce rol CDPDGm CDPdiacylglyce rolM
CDPETN CDPethanolami ne CER2 Ceramide-2 CER3 Ceramide-3 CGLY Cys-Gly cGMP 3',5'-Cyclic GMP

Carboxyhexano yl-CoA
CHIT Chitin CHITO Chitosan CHO Choline CHOR Chorismate cIMP 3',5'-Cyclic IMP
CIT Citrate CITm CitrateM
CITR L-Citrulline CLm CardiolipinM
CMP CMP
CMPm CMPM
CMUSA 2-Amino-3-carboxymuconat e semialdehyde C02m C02M
COA CoA
COAm CoAM
CPAD5P 1-(2-Carboxyphenyla mino)-I-deoxy-D-ribulose 5-phosphate CPP Coproporphyrin ogen CTP CTP
CTPm CTPM
CYS L-Cysteine CYTD Cytidine CYTS Cytosine D45 PI 1 -Phosphatidyl-D-myo-inositol 4,5-bisphosphate D6PGC 6-Phospho-D-gluconate D6PGL D-Glucono-l,5-lactone 6-phosphate D6RP5P 2,5-Diamino-6-hydroxy-4-(5'-phosphoribosyla mino)-pyrimidine D8RL 6,7-Dimethyl-8-(1-D-ribityl)lumazine DA Deoxyadenosine DADP dADP
DAGLY Diacylglycerol DAMP dAMP
dAMP dAMP
DANNA 7,8-Diaminononano ate DAPRP 1,3-Diaminopropane DATP dATP
DB4P L-3,4-Dihydroxy-2-butanone 4-phosphate DC Deoxycytidine DCDP dCDP
DCMP dCMP
DCTP dCTP
DFUC alpha-D-Fucoside DG Deoxyguanosine DGDP dGDP
DGMP dGMP
DGPP Diacylglycerol pyrophosphate DGTP dGTP
DHF Dihydrofolate DHFm DihydrofolateM
DHMVAm (R)-2,3-dihydroxy-3-methylbutanoate M
DHP 2-Amino-4-hydroxy-6-(D-erythro-1,2,3-trihydroxypropy 1)-7,8-dihydropteridine DHPP Dihydroneopteri is phosphate DHPT Dihydropteroate Dehydroshikima to DHSP Sphinganine 1-phosphate Dehydrosphinga nine DHVALm (R)-3-Hydroxy-3-methyl-2-oxobutanoateM
DIMGP D-erythro-l-(Imidazol-4-yl)glycerol 3-phosphate DIN Deoxyinosine DIPEP Dipeptide DISACIP 2,3-bis(3-hydroxytetradec anoyl)-D-glucosaminyl-1,6-beta-D-2,3-bis(3-hydroxytetradec anoyl)-beta-D-glucosaminyl 1-phosphate DLIPOm Dihydrolipoami deM
DMPP Dimethylallyl diphosphate DMZYMST 4,4-Dimethylzymost erol DOL Dolichol DOLMANP Dolichyl beta-D-mannosyl phosphate DOLP Dolichyl phosphate DOLPP Dehydrodolichol diphosphate DOROA (S)-Dihydroorotate DPCOA Dephospho-CoA
DPCOAm Dephospho-CoAM
DPTH 2-[3-Carboxy-3-(methylammoni o)ProPyl)-L-histidine Dehydroquinate DRIP Deoxy-ribose (-phosphate DR5P 2-Deoxy-D-ribose 5-phosphate DRIB Deoxyribose DSAM S-Adenosylmethio ninamine DT Thymidine DTB Dethiobiotin DTBm DethiobiotinM
DTDP dTDP
DTMP dTMP
DTP 1-Deoxy-d-threo-2-pentulose DTTP dTTP
DU Deoxyuridine DUDP dUDP
DUMP dUMP
DUTP dUTP
E4P D-Erythrose 4-phosphate EPM Epimelibiose EPST Episterol ER4P 4-Phospho-D-erythronate ERGOST Ergosterol ERTEOL Ergosta-5,7,22,24(28)-tetraenol ERTROL Ergosta-5,7,24(28)-trienol ETH Ethanol ETHm EthanolM
ETHM Ethanolamine F1P D-Fructose 1-phosphate F26P D-Fructose 2,6-bisphosphate F6P beta-D-Fructose 6-phosphate FAD FAD
FADH2m FADI-12M
FADm FADM
FALD Formaldehyde FDP beta-D-Fructose 1,6-bisphosphate FERIm Ferricytochrome cm FEROm Ferrocytochrom ecM
FEST Fecosterol FGAM 2-(Formamido)-N 1-(5'-phosphoribosyl) acetamidine FGAR 5'-Phosphoribosyl-N-formylglycinami de FGT S-Formylglutathio ne FKYN L-Formylkynureni ne FMN FMN
FMNm FMNM
FMRNAm N-Formylmethiony I-tRNAM
FOR Formate FORm FormateM
FPP trans,trans-Farnesyl diphosphate FRU D-Fructose Formyltetrahydr ofolate FTHFm 10-Formyltetrahydr ofolateM

Fumarylacetoac elate FUC beta-D-Fucose FUM Fumarate FUMm FumarateM
GIP D-Glucose 1-phosphate G6P alpha-D-Glucose 6-phosphate GAIP D-Glucosamine 1-phosphate GA6P D-Glucosamine 6-phosphate Aminobutanoate Aminobutyralde hyde GABALm 4-Aminobutyralde hydeM
GABAm 4-Aminobutanoate M
GALlP D-Galactose 1-phosphate GAR 5'-Phosphoribosylg lycinamide GBAD 4-Guanidino-butanamide GBAT 4-Guanidino-butanoate GC gamma-L-Glutamyl-L-cysteine GDP GDP
GDPm GDPM
GDPMAN GDPmannose GGL Galactosylglycer of GL Glycerol GL3P sn-Glycerol 3-phosphate GL3Pm sn-Glycerol 3-phosphateM
GLAC D-Galactose GLACL l-alpha-D-Galactosyl-myo-inositol GLAL Glycolaldehyde GLAM Glucosamine GLC alpha-D-Glucose GLCN Gluconate GLN L-Glutamane GLP Glycylpeptide GLT L-Glucitol GLU L-Glutamate GLUGSAL L-Glutamate 5-semialdehyde GLUGSALm L-Glutamate 5-semialdehydeM
GLUm GlutamateM

GLUP alpha-D-Glutamyl phosphate GLX Glyoxylate GLY Glycine GLYCOGEN Glycogen GLYm GlycineM
GLYN Glycerone GMP GMP
GN Guanine GNm GuanineM
GPP Geranyl diphosphate GSN Guanosine GSNm GuanosineM
GTP GTP
GTPm GTPM
GTRNA L-Glutamyl-tRNA(Glu) GTRNAm L-Glutamyl-tRNA(Glu)M
GTRP PI,P4-Bis(5'-guanosyl) tetraphosphate H2S Hydrogen sulfide H2S03 Sulfite H3MCOA (S)-3-Hydroxy-methylglutaryl-CoA
H3MCOAm (S)-3-Hydroxy-methylglutaryl-CoAM
HACNm But-l-ene-l,2,4-tricarboxylateM
HACOA (3S)-3-Hydroxyacyl-CoA

Hydroxyanthran ilate HBA 4-Hydroxy-benzyl alcohol Hydroxybutane-1,2,4-tricarboxylate HCITm 2-Hydroxybutane-1,2,4-tricarboxylateM
HCYS Homocysteine HEXT H+EXT
HHTRNA L-Histidyl-tRNA(His) HIB (S)-3-Hydroxyisobuty rate HIBCOA (S)-3-Hydroxyisobuty ryl-CoA
HICITm Homoisocitrate M
HIS L-Histidine HISOL L-Histidinol HISOLP L-Histidinol phosphate Hydroxykynure nine Hm H+M
HMB Hydroxymethyl bilane HOMOGEN Homogentisate HPRO trans-4-Hydroxy-L-proline HSER L-Homoserine HTRNA tRNA(His) HYXAN Hypoxanthine IAC Indole-3-acetate IAD Indole-3-acetamide Methylpropanoy I-CoA
ICIT Isocitrate ICITm IsocitrateM
IDP IDP
IDPm IDPM
IGP Indoleglycerol phosphate IGST 4,4-Dimethylcholest a-8, 14,24-trienol IIMZYMST Intermediate M
ethylzymosterol II
IIZYMST Intermediate_Zy mosterol II
ILE L-Isoleucine ILEm L-IsoleucineM
IMACP 3-(Imidazol-4-yl)-2-oxopropyl phosphate IMP IMP
IMZYMST Intermediate M
ethylzymosterol _I
INAC Indoleacetate INS Inosine IPC Inositol phosphorylcera mide Isopropylmalate IPPMALm 2-Isopropylmalate M
IPPP Isopentenyl diphosphate ISUCC a-Iminosuccinate ITCCOAm Itaconyl-CoAM
ITCm ItaconateM
ITP ITP
ITPm ITPM

Methylbutanoyl-CoA
IZYMST Intermediate_Zy mosterol_I
K Potassium KYN L-Kynurenine LAC (R)-Lactate LACALm (S)-LactaldehydeM
LACm (R)-LactateM
LCCA a Long-chain carboxylic acid LEU L-Leucine LEUm L=LeucineM
LGT (R)-S-Lactoylglutathio ne LGTm (R)-S-Lactoylglutathio neM
LIPIV 2,3,21,31-tetrakis(3-hydroxytetradec anoyl)-D-glucosaminyl-I,6-beta-D-glucosamine 1,4'-bisphosphate LIPOm LipoamideM
LIPX Lipid X
LLACm (S)-LactateM
LLCT L-Cystathionine LLTRNA L-lysyl-tRNA(Lys) LLTRNAm L-lysyl-tRNA(Lys)M
LNST Lanosterol LTRNA tRNA(Lys) LTRNAm tRNA(Lys)M
LYS L-Lysine LYSm L-LysineM
MAACOA a-Methylacetoacet yl-CoA

Maleylacetoacet ate MACOA 2-Methylprop-2-enoyl-CoA
MAL Malate MALACP Malonyl-[acyl-carrier protein]
MALACPm Malonyl-[acyl-carrier protein]M
MALCOA Malonyl-CoA
MALm MalateM
MALT Malonate MALTm MalonateM
MAN alpha-D-Mannose MANIP alpha-D-Mannose 1-phosphate MAN2PD beta-D-Mannosyldiacet ylchitobiosyldip hosphodolichol MAN6P D-Mannose 6-phosphate MANNAN Mannan MBCOA Methylbutyryl-CoA
MCCOA 2-Methylbut-2-enoyl-CoA
MCRCOA 2-Methylbut-2-enoyl-CoA
MDAP Meso-diaminopimelate MELI Melibiose MELT Melibiitol MET L-Methionine METH Methanethiol METHF 5,10-Methenyltetrahy drofolate METHFm 5,10-Methenyltetrahy drofolateM
METTHF 5,10-Methylenetetrah ydrofolate METTHFm 5,10-Methylenetetrah ydrofolateM

Methylglutacon yl-CoA
MHIS N(pai)-Methyl-L-histidine MHVCOA a-Methyl-b-hydroxyvaleryl-CoA
MI myo-Inositol MI1P IL-myo-Inositol I-phosphate MIP2C Inositol-mannose-P-inositol-P-ceramide MIPC Mannose-inositol-P-ceramide MK Menaquinone MLT Maltose MMCOA Methylmalonyl-CoA
MMET S-Methylmethioni ne MMS (S)-Methylmalonate semialdehyde MNT D-Mannitol MNT6P D-Mannitol 1-phosphate Methyltetrahydr ofolate MTHFm 5-Methyltetrahydr ofolateM
MTHGXL Methylglyoxal MTHN Methane MTHNm MethaneM

Methyltetrahydr opteroyltri-L-glutamate MTRNAm L-Methionyl-tRNAM
MVL (R)-Mevalonate MVLm (R)-MevalonateM
MYOI myo-Inositol Methylzymstero I
N4HBZ 3-Nonaprenyl-4-hydroxybenzoat e NA Sodium NAAD Deamino-NAD+
NAADm Deamino-NAD+M
NAC Nicotinate NACm NicotinateM
NAD NAD+
NADH NADH
NADHm NADHM
NADm NAD+M
NADP NADP+
NADPH NADPH
NADPHm NADPHM
NADPm NADP+M
NAG N-Acetylglucosami ne NAGAIP N-Acetyl-D-glucosamine 1-phosphate NAGA6P N-Acetyl-D-glucosamine 6-phosphate NAGLUm N-Acetyl-L-glutamateM
NAGLUPm N-Acetyl-L-glutamate 5-phosphateM
NAGLUSm N-Acetyl-L-glutamate 5-semialdehydeM
NAM Nicotinamide NAMm NicotinamideM
NAMN Nicotinate D-ribonucleotide NAMNm Nicotinate D-ribonucleotideM
NAORNm N2-Acetyl-L-omithineM

NH3m NH3M
NH4 NH4+
NPP all-trans-Nonaprenyl diphosphate NPPm all-trans-Nonaprenyl diphosphateM
NPRAN N-(5-Phospho-D-ribosyl)anthranil ate 02 Oxygen 02m OxygenM
OA Oxaloacetate OACOA 3-Oxoacyl-CoA
OAHSER O-Acetyl-L-homoserine OAm OxaloacetateM
OBUT 2-Oxobutanoate OBUTm 2-OxobutanoateM
OFP Oxidized flavoprotein OGT Oxidized glutathione OHB 2-Oxo-3-hydroxy-4-phosphobutanoa to OHm HO-M
OICAP 3-Carboxy-4-methyl-2-oxopentanoate OICAPm 3-Carboxy-4-methyl-2-oxopentanoateM
OIVAL (R)-2-Oxoisovalerate O1VALm (R)-2-Oxoisovalerate M
OMP Orotidine 5'-phosphate OMVAL 3-Methyl-2-oxobutanoate OMVALm 3-Methyl-2-oxobutanoateM
OPEP Oligopeptide ORN L-Omithine ORNm L-OmithineM
OROA Orotate OSLHSER O-Succinyl-L-homoserine OSUC Oxalosuccinate OSUCm Oxalosuccinate M
OTHIO Oxidized thioredoxin OTHIOm Oxidized thioredoxinM
OXA Oxaloglutarate OXAm Oxaloglutarate M
P5C (S)-l-Pyrroline-5-carboxylate P5Cm (S)-1-Pyrroline-5-carboxylateM
P5P Pyridoxine phosphate PA Phosphatidate Aminobenzoate PAC Phenylacetic acid Phenylacetamid e PALCOA Palmitoyl-CoA
PAm PhosphatidateM
PANT (R)-Pantoate PANTm (R)-PantoateM
PAP Adenosine 3',5'-bisphosphate PAPS 3'-Phosphoadenyly Isulfate PBG Porphobilinogen PC Phosphatidylcho line PC2 Sirohydrochlori n PCHO Choline phosphate PDLA Pyridoxamine PDLA5P Pyridoxamine phosphate PDME Phosphatidyl-N-dimethylethanol amine PE Phosphatidyleth anolamine PEm Phosphatidyleth anolamineM
PEP Phosphoenolpyr ovate PEPD Peptide PEPm Phosphoenolpyr uvateM
PEPT Peptide PETHM Ethanolamine phosphate PGm Phosphatidylgly cerolM
PGPm Phosphatidylgly cerophosphateM
PHC L-1-Pyrroline-3-hydroxy-5-carboxylate PHE L-Phenylalanine PHEN Prephenate Phosphonooxyp yruvate PHPYR Phenylpyruvate PHSER O-Phospho-L-homoserine PHSP Phytosphingosin e I-phosphate PHT O-Phospho-4-hydroxy-L-threonine PI Orthophosphate PIm Orthophosphate M
PIME Pimelic Acid PINS 1-Phosphatidyl-D-myo-inositol PINS4P 1-Phosphatidyl-1D-myo-inositol 4-phosphate PINSP 1-Phosphatidyl-1D-myo-inositol 3-phosphate PL Pyridoxal PL5P Pyridoxal phosphate PMME Phosphatidyl-N-methylethanola mine PMVL (R)-5-Phosphomevalo nate PNTO (R)-Pantothenate PPHG Protoporphyrino gen IX
PPHGm Protoporphyrino gen IXM
PPI Pyrophosphate PPIn Pyrophosphate M
PPDCm Protoporphyrin M

Isopropylmaleat e PPMVL (R)-5-Diphosphomeva lonate Phosphoribosyla mine PRBAMP Nl-(5-Phospho-D-ribosyl)-AMP
PRBATP Nl-(5-Phospho-D-ribosyl)-ATP
PRFICA 1-(5'-Phosphoribosyl) -5-formamido-4-imidazolecarbox amide PRFP 5-(5-Phospho-D-ribosylaminofor mimino)-1-(5-phosphoribosyl) -imidazole-4-carboxamide PRLP N-(5'-Phospho-D-1'-ribulosylformim ino)-5-amino-l-(5"-phospho-D-ribosyl)-4-imidazolecarbox amide PRO L-Proline PROm L-ProlineM
PROPCOA Propanoyl-CoA
PRPP 5-Phospho-alpha-D-ribose 1-diphosphate PRPPm 5-Phospho-alpha-D-ribose I -diphosphateM
PS Phosphatidylseri ne PSm Phosphatidylseri neM
PSPH Phytosphingosin e PTHm HemeM
PTRC Putrescine PTRSC Putrescine PURISP Pseudouridine 5'-phosphate PYR Pyruvate PYRDX Pyridoxine PYRm PyruvateM
Q Ubiquinone-9 QA Pyridine-2,3-dicarboxylate QAm Pyridine-2,3-dicarboxylateM
QH2 Ubiquinol QH2m UbiquinolM
Qm Ubiquinone-9M
RIP D-Ribose 1-phosphate R5P D-Ribose 5-phosphate RADP 4-(1-D-Ribitylamino)-5-amino-2,6-dihydroxypyrimi dine RAF Raffinose RFP Reduced flavoprotein RGT Glutathione RGTm GlutathioneM
RIB D-Ribose RIBFLAVm RiboflavinM
RIBOFLAV Riboflavin RIPm alpha-D-Ribose 1-phosphateM
RLSP D-Ribulose 5-phosphate RMN D-Rhamnose RTHIO Reduced thioredoxin RTHIOm Reduced thioredoxinM
S Sulfur S17P Sedoheptulose 1,7-bisphosphate S23E (S)-2,3-Epoxysqualene S7P Sedoheptulose 7-phosphate SACP N6-(L-1,3-Dicarboxypropy I)-L-lysine SAH S-Adenosyl-L-homocysteine SAHm S-Adenosyl-L-homocysteineM
SAICAR 1-(5-Phosphoribosyl) -5-amino-4-(N-succinocarboxa mide)-imidazole SAM S-Adenosyl-L-methionine SAMm S-Adenosyl-L-methionineM
SAMOB S-Adenosyl-4-methylthio-2-oxobutanoate SAPm S-Aminomethyldi hydrolipoylprote inM
SER L-Serine SERm L-SerineM
SLF Sulfate SLFm SulfateM
SME Shikimate SMESP Shikimate 3-phosphate SOR Sorbose SORIP Sorbose (-phosphate SOT D-Sorbitol SPH Sphinganine SPMD Spermidine SPRM Spermine SPRMD Spermidine SQL Squalene SUC Sucrose SUCC Succinate SUCCm SuccinateM
SUCCOAm Succinyl-CoAM
SUCCSAL Succinate semialdehyde Glyceraldehyde 3-phosphate T3P2 Glycerone phosphate T3P2m Glycerone phosphateM
TAG16P D-Tagatose 1,6-bisphosphate TAG6P D-Tagatose 6-phosphate TAGLY Triacylglycerol TCOA Tetradecanoyl-CoA
TGLP N-Tetradecanoylgl ycylpeptide THE Tetrahydrofolate THFG Tetrahydrofolyl-[Glu](n) THFm Tetrahydrofolate M
THIAMIN Thiamin THMP Thiamin monophosphate THPTGLU Tetrahydroptero yltri-L-glutamate THR L-Threonine THRm L-ThreonineM
THY Thymine THZ 5-(2-Hydroxyethyl)-4-methylthiazole THZP 4-Methyl-5-(2-phosphoethyl)-thiazole TPI D-myo-inositol 1,4,5-trisphosphate TPP Thiamin diphosphate TPPP Thiamin triphosphate THE alpha,alpha-Trehalose TRE6P alpha,alpha'-Trehalose 6-phosphate TRNA tRNA
TRNAG tRNA(Glu) TRNAGm tRNA(Glu)M
TRNAm tRNAM
TRP L-Tryptophan TRPm L-TryptophanM
TRPTRNAm L-Tryptophanyl-tRNA(Trp)M
TYR L-Tyrosine UDP UDP
UDPG UDPglucose UDPG23A UDP-2,3-bis(3-hydroxytetradec anoyl)glucosami ne UDPG2A UDP-3-O-(3-hydroxytetradec anoyl)-D-glucosamine UDPG2AA UDP-3-O-(3-hydroxytetradec anoyl)-N-acetylglucosami ne UDPGAL UDP-D-galactose UDPNAG UDP-N-acetyl-D-galactosamine UDPP Undecaprenyl diphosphate UGC (-)-Ureidoglycolate UMP UMP
UPRG Uroporphyrinog en III
URA Uracil UREA Urea UREAC Urea-1-carboxylate URI Uridine UTP UTP
VAL L-Valine X5P D-Xylose-5-phosphate XAN Xanthine XMP Xanthosine 5'-phosphate XTSINE Xanthosine XTSN Xanthosine XUL D-Xylulose XYL D-Xylose ZYMST Zymosterol [0056] Depending upon the particular environmental conditions being tested and the desired activity, a reaction network data structure can contain smaller numbers of reactions such as at least 200, 150, 100 or 50 reactions. A reaction network data structure having relatively few reactions can provide the advantage of reducing computation time and resources required to perform a simulation. When desired, a reaction network data structure having a particular subset of reactions can be made or used in which reactions that are not relevant to the particular simulation are omitted. Alternatively, larger numbers of reactions can be included in order to increase the accuracy or molecular detail of the methods of the invention or to suit a particular application. Thus, a reaction network data structure can contain at least 300, 350, 400, 450, 500, 550, 600 or more reactions up to the number of reactions that occur in or by S. cerevisiae or that are desired to simulate the activity of the full set of reactions occurring in S. cerevisiae. A reaction network data structure that is substantially complete with respect to the metabolic reactions of S.
cerevisiae provides the advantage of being relevant to a wide range of conditions to be simulated, whereas those with smaller numbers of metabolic reactions are limited to a particular subset of conditions to be simulated.

[0057] A S. cerevisiae reaction network data structure can include one or more reactions that occur in or by S. cerevisiae and that do not occur, either naturally or following manipulation, in or by another prokaryotic organism, such as Escherichia coli, Haemophilus influenzae, Bacillus subtilis, Helicobacter pylori or in or by another eukaryotic organism, such as Homo sapiens. Examples of reactions that are unique to S. cerevisiae compared at least to Escherichia coli, Haemophilus influenzae, and Helicobacterpylori include those identified in Table 4. It is understood that a S. cerevisiae reaction network data structure can also include one or more reactions that occur in another organism. Addition of such heterologous reactions to a reaction network data structure of the invention can be used in methods to predict the consequences of heterologous gene transfer in S.
cerevisiae, for example, when designing or engineering man-made cells or strains.

Table 4. Reactions specific to S. cerevisiae metabolic network glkl_3, hxkl_l, hxk2_l, hxkl_4, hxk2 4, pflcl_3, idhl, idpl_l, idpl_2, idp2_1, idp3_1, idp2_2, idp3_2, 1sc1R, pycl, pyc2, cyb2, dldl, ncpl, cytr_, cyto, atpl, pmal, pma2, pmpl, pmp2, coxl, rbkl_2, achl_l, achl_2, sfal_1R, unkrxl lR, pdcl, pdc5, pdc6, lys20, adh1R, adh3R, adh2R, adh4R, adh5R, sfal_2R, psal, pfk26, pfk27, fbp26, gal7R, mell_2, mell_3, mell_4R, mell_5R, mell_6R, mell_7R, fsp2b, sorl, gsyl, gsy2, fksl, fks3, gsc2, tpsl, tps3, tsll, tps2, athl, nthl, nth2, fdhl, tfola, tfolb, dur1R, dur2, nit2, cyrl, gukl_3R, ade2R, pdel, pde2_l, pde2_2, pde2_3, pde2 4, pde2_5, apa2, apal_1, apal_3, apal_2R, ura2_1, ura4R, ural_1R, uralOR, ura5R, ura3, npkR, furl, fcyl, tdkl, tdk2, urkl_l, urkl_2, urkl_3, deoalR, deoa2R, cddl_1, eddl_2, cdc8R, dutl, cdc2l, cmka2R, dcdlR, ura71 2, ura8_2, deg1R, pus 1R, pus2R, pus4R, ural_2R, aral_l, aral_2, gnalR, pcmlaR, qrilR, chsl, chs2, chs3, put2_l, put2, gltl, gdh2, cat2, yatl, mhtl, sam4, ecm40_2, cpa2, ura2_2, arg3, spe3, spe4, amd, amd2_l, atma, msrl, mas, ded8l, hom6_l, cys4, glyl, agtR, gcv2R, sahl, met6, cys3, met17_1, metl7hR, dph5, met3, metl4, metl7_2, metl7_3, lys2l, lys20a, lys3R, lys4R, lysl2R, lysl2bR, amitR, lys2_1, lys2_2, lys9R, lyslaR, krsl, mskl, pro2_1, gpslR, gps2R, pro3_3, pro3 4, pro3_1, pro3_5, dallR, dal2R, dal3R, his4_3, htsl, hmtl, tyrl, ctal, cttl, ald6, ald4_2, ald5_1, tdo2, kfor , kynu_l, kmo, kynu_2, bnal, aaaa, aaab, aaac, tyrdega, tyrdegb, tyrdegc, trydegd, mswl, amd2_2, amd2_3, spra, sprb, sprc, sprd, spre, dysl, leu4, leul_2R, pclig, xapalR, xapa2R, xapa3R, ynkl_6R, ynkl_9R, udpR, pyrhlR, pyrh2R, cmpg, ushal, usha2, usha5, usha6, ushal 1, gpxlR, gpx2R, hyr1R, ecm38, nit2_1, nit2_2, nmtl, natl, nat2, bgl2, exgl, exg2, sprl, thi80_l, thi80_2, unkrxn8, phol1, finnl_l, fmnl_2, pdx3_2R, pdx3_3R, pdx3_4R, pdx3_1, pdx3_5, biol, foll_4, ftfa, ftfb, fol3R, met7R, rmalR, metl2, metl3, misl_2, ade3_2, mtdl, fmtl, TypeII_l, Typel_2, TypelI_4, TypeII_3, TypeIl_6, TypelI_5, TypelI_9, TypelI_8, Typell_7, clOOsn, c180sy, c182sy, faalR, faa2R, faa3R, faa4R, fox2bR, pot 1_i, erglO_1R, ergiO_2R, Gatl_2, Gat2_2, ADHAPR, AGAT, slcl, Gatl_l, Gat2_1, cholaR, cholbR, cho2, opi3_l, opi3_2, ckil, pctl, cptl, ekil, ectl, ept1R, inol, impal, pisl, torl, tor2, vps34, pikl, sst4, fabl, mss4, plcl, pgs 1 R, crdl, dppl, lpp 1, hmgsR, hmg l R, hmg2R, erg l2_1, erg 12_2, erg 12_3, erg12 4, erg8, mvdl, erg9, ergl, erg7, unkrxn3, unkrxn4, cdisoa, ergl 1_1, erg24, erg25_1, erg26_1, ergll_2, erg25_2, erg26_2, ergll_3, erg6, erg2, erg3, ergs, erg4, lcbl, lcb2, tsclO, sur2, csyna, csynb, scs7, aurl, csg2, surl, iptl, leb4_1, lcb5_1, lcb4_2, lcb5_2, lcb3, ysr3, dpll, sec59, dpml, pmtl, pmt2, pmt3, pmt4, pmts, pmt6, kre2, ktrl, ktr2, ktr3, ktr4, ktr6, yurl, hor2, rhr2, cdal, cda2, daga, dakl, dak2, gpdl, nadglR, nadg2R, nptl, nadi, mnadphps, mnadgiR, mnadg2R, mnptl, mnadi, heml, bet2, cogl, coq2, coxlO, raml, rer2, srtl, mo2R, mco2R, methR, mmthnR, mnh3R, mthfR, mmthfR, mserR, mglyR, mcbhR, moicapR, mproR, mcmpR, macR, macar , mcar_, maclacR, mactcR, moivaiR, momvalR, mpmalRR, mslf, mthrR, maka, aacl, aac3, pet9, mirlaR, mirldR, dicl 2R, dicl_1R, die 1_3, mmltR, moabR, ctp1_1R, ctp1_2R, ctp1_3R, pyrcaR, mlacR, gcaR, gcb, ort1R, crcl, gut2, gpd2, mt3p, mgl3p, mfad, mriboR, mdtbR, mmcoaR, mmv1R, mpaR, mppntR, madR, mprppR, mdhfR, mqaR, moppR, msamR, msahR, sfcl, odc1R, odc2R, hxtl_2, hxt10_2, hxt1l_2, hxt13_2, hxt15_2,1ixt16_2, hxtl7_2, hxt2_2, hxt3_2, hxt4_2, hxt5_2, hxt6_2, hxt7_2, hxt8_5, hxt9_2, sucup, akmupR, sorupR, arbup1R, gltlupb, gal2_3, hxtl_l, hxt10_1, hxtl 1, hxtll_l, hxtl3_1, hxtl5_1, hxtl6_1, hxtl7_1, hxt2_l, hxt3_1, hxt4, hxt4_l, hxt5_1, hxt6_l, hxt7_1, hxt8_4, hxt9_1, stll_l, gaupR, mmpl, mltup, mntup, nagup, rmnup, ribup, treup_2, treup_l, xylupR, uga5, bap2_1R, bap3_1R, gap5R, gnp3R, tat7R, vap7R, sam3, put7, uga4, dip9R, gap22R, gap7R, gnplR, gap23R, gap9R, hip1R, vap6R, bap2_4R, bap3_4R, gap 13R, gap26R, gnp4R, mup1R, mup3R, bap2_5R, bap3_5R, gapl4R, gap29R, tat4R, ptrup, sprup1, ptr2, ptr3, ptr4, mnadd2, fcy2_3R, fcy2l_3R, fcy22_3R, gnupR, hyxnupR, nccup3, nccup4, nccup6, nccup7, ncgup4, ncgup7, ncgupl1, ncgup 12, ncup4, ncup7, ncup 11, ncup 12, ethupR, sul1, sul2, sulup, citupR, amgupR, atpmt, glaltxR, dal4, dal5, mthupR, papxR, thyxR, ga6pupR, btupR, kapaupR, dapaupR, ogtup, sprmup, pimeup, thin I, thm2, thm3, rflup, hnml, ergupR, zymupR, lixtl_5, hxtl0_3, hxtl 1_3, hxtl 3_3, hxtl5_3, hxtl6_3, hxtl7_3, hxt2_3, hxt3_3, hxt4_3, hxt5_3, hxt6_3, hxt7_3, hxt8_6, hxt9_3, itri, itr2, bio5a, agp2R, dttpxR, gltup [0058] A reaction network data structure or index of reactions used in the data structure such as that available in a metabolic reaction database, as described above, can be annotated to include information about a particular reaction. A reaction can be annotated to indicate, for example, assignment of the reaction to a protein, macromolecule or enzyme that performs the reaction, assignment of a gene(s) that codes for the protein, macromolecule or enzyme, the Enzyme Commission (EC) number of the particular metabolic reaction or Gene Ontology (GO) number of the particular metabolic reaction, a subset of reactions to which the reaction belongs, citations to references from which information was obtained, or a level of confidence with which a reaction is believed to occur in S. cerevisiae. A
computer readable medium or media of the invention can include a gene database containing annotated reactions. Such information can be obtained during the course of building a metabolic reaction database or model of the invention as described below.

[0059] As used herein, the term "gene database" is intended to mean a computer readable medium or media that contains at least one reaction that is annotated to assign a reaction to one or more macromolecules that perform the reaction or to assign one or more nucleic acid that encodes the one or more macromolecules that perform the reaction. A gene database can contain a plurality of reactions some or all of which are annotated. An annotation can include, for example, a name for a macromolecule; assignment of a function to a macromolecule; assignment of an organism that contains the macromolecule or produces the macromolecule; assignment of a subcellular location for the macromolecule;
assignment of conditions under which a macromolecule is being expressed or being degraded;
an amino acid or nucleotide sequence for the macromolecule; or any other annotation found for a macromolecule in a genome database such as those that can be found in Saccharomyces Genome Database maintained by Stanford University, or Comprehensive Yeast Genome Database maintained by MIPS.

[00601 A gene database of the invention can include a substantially complete collection of genes and/or open reading frames in S. cerevisiae or a substantially complete collection of the macromolecules encoded by the S. cerevisiae genome. Alternatively, a gene database can include a portion of genes or open reading frames in S. cerevisiae or a portion of the macromolecules encoded by the S. cerevisiae genome. The portion can be at least 10%, 15%, 20%, 25%, 50%, 75%, 90% or 95% of the genes or open reading frames encoded by the S.
cerevisiae genome, or the macromolecules encoded therein. A gene database can also include macromolecules encoded by at least a portion of the nucleotide sequence for the S.
cerevisiae genome such as at least 10%, 15%, 20%, 25%, 50%, 75%, 90% or 95% of the S.
cerevisiae genome. Accordingly, a computer readable medium or media of the invention can include at least one reaction for each macromolecule encoded by a portion of the S. cerevisiae genome.

[00611 An in silico S. cerevisiae model according to the invention can be built by an iterative process which includes gathering information regarding particular reactions to be added to a model, representing the reactions in a reaction network data structure, and performing preliminary simulations wherein a set of constraints is placed on the reaction network and the output evaluated to identify errors in the network. Errors in the network such as gaps that lead to non-natural accumulation or consumption of a particular metabolite can be identified as described below and simulations repeated until a desired performance of the model is attained. An exemplary method for iterative model construction is provided .in Example I.

[0062] Thus, the invention provides a method for making a data structure relating a plurality of S. cerevisiae reactants to a plurality of S. cerevisiae reactions in a computer readable medium or media. The method includes the steps of. (a) identifying a plurality of S.
cerevisiae reactions and a plurality of S. cerevisiae reactants that are substrates and products of the S. cerevisiae reactions; (b) relating the plurality of S. cerevisiae reactants to the plurality of S. cerevisiae reactions in a data structure, wherein each of the S. cerevisiae reactions includes a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating the substrate and the product;
(c) making a constraint set for the plurality of S. cerevisiae reactions; (d) providing an objective function; (e) determining at least one flux distribution that minimizes or maximizes the objective function when the constraint set is applied to the data structure, and (f) if at least one flux distribution is not predictive of S. cerevisiae physiology, then adding a reaction to or deleting a reaction from the data structure and repeating step (e), if at least one flux distribution is predictive of S. cerevisiae physiology, then storing the data structure in a computer readable medium or media.

[0063] Information to be included in a data structure of the invention can be gathered from a variety of sources including, for example, the scientific literature or an annotated genome sequence of S. cerevisiae such as the Genbank, a site maintained by the NCBI
(ncbi.nlm.gov), the CYGD database, a site maintained by MIPS, or the SGD
database, a site maintained by the School of Medicine at Stanford University, etc.

[0064] In the course of developing an in silico model of S. cerevisiae metabolism, the types of data that can be considered include, for example, biochemical information which is information related to the experimental characterization of a chemical reaction, often directly indicating a protein(s) associated with a reaction and the stoichiometry of the reaction or indirectly demonstrating the existence of a reaction occurring within a cellular extract;
genetic information which is information related to the experimental identification and genetic characterization of a gene(s) shown to code for a particular protein(s) implicated in carrying out a biochemical event; genomic information which is information related to the identification of an open reading frame and functional assignment, through computational sequence analysis, that is then linked to a protein performing a biochemical event;

physiological information which is information related to overall cellular physiology, fitness characteristics, substrate utilization, and phenotyping results, which provide evidence of the assimilation or dissimilation of a compound used to infer the presence of specific biochemical event (in particular translocations); and modeling information which is information generated through the course of simulating activity of S.
cerevisiae using methods such as those described herein which lead to predictions regarding the status of a reaction such as whether or not the reaction is required to fulfill certain demands placed on a metabolic network.

[00651 The majority of the reactions occurring in S. cerevisiae reaction networks are catalyzed by enzymes/proteins, which are created through the transcription and translation of the genes found on the chromosome(s) in the cell. The remaining reactions occur through non-enzymatic processes. Furthermore, a reaction network data structure can contain reactions that add or delete steps to or from a particular reaction pathway.
For example, reactions can be added to optimize or improve performance of a S. cerevisiae model in view of empirically observed activity. Alternatively, reactions can be deleted to remove intermediate steps in a pathway when the intermediate steps are not necessary to model flux through the pathway. For example, if a pathway contains 3 nonbranched steps, the reactions can be combined or added together to give a net reaction, thereby reducing memory required to store the reaction network data structure and the computational resources required for manipulation of the data structure. An example of a combined reaction is that for fatty acid degradation shown in Table 2, which combines the reactions for acyl-CoA
oxidase, hydratase-dehydrogenase-epimerase, and acetyl-CoA C-acyltransferase of beta-oxidation of fatty acids.

[00661 The reactions that occur due to the activity of gene-encoded enzymes can be obtained from a genome database that lists genes or open reading frames identified from genome sequencing and subsequent genome annotation. Genome annotation consists of the locations of open reading frames and assignment of function from homology to other known genes or empirically determined activity. Such a genome database can be acquired through public or private databases containing annotated S. cerevisiae nucleic acid or protein sequences. If desired, a model developer can perform a network reconstruction and establish the model content associations between the genes, proteins, and reactions as described, for example, in Covert et al. Trends in Biochemical Sciences 26:179-186 (2001) and Palsson, WO 00/46405.

[0067] As reactions are added to a reaction network data structure or metabolic reaction database, those having known or putative associations to the proteins/enzymes which enable/catalyze the reaction and the associated genes that code for these proteins can be identified by annotation. Accordingly, the appropriate associations for some or all of the reactions to their related proteins or genes or both can be assigned. These associations can be used to capture the non-linear relationship between the genes and proteins as well as between proteins and reactions. In some cases, one gene codes for one protein which then perform one reaction. However, often there are multiple genes which are required to create an active enzyme complex and often there are multiple reactions that can be carried out by one protein or multiple proteins that can carry out the same reaction. These associations capture the logic (i.e. AND or OR relationships) within the associations. Annotating a metabolic reaction database with these associations can allow the methods to be used to determine the effects of adding or eliminating a particular reaction not only at the reaction level, but at the genetic or protein level in the context of running a simulation or predicting S.
cerevisiae activity.

[0068] A reaction network data structure of the invention can be used to determine the activity of one or more reactions in a plurality of S. cerevisiae reactions independent of any knowledge or annotation of the identity of the protein that performs the reaction or the gene encoding the protein. A model that is annotated with gene or protein identities can include reactions for which a protein or encoding gene is not assigned. While a large portion of the reactions in a cellular metabolic network are associated with genes in the organism's genome, there are also a substantial number of reactions included in a model for which there are no known genetic associations. Such reactions can be added to a reaction database based upon other information that is not necessarily related to genetics such as biochemical or cell based measurements or theoretical considerations based on observed biochemical or cellular activity. For example, there are many reactions that are not protein-enabled reactions.
Furthermore, the occurrence of a particular reaction in a cell for which no associated proteins or genetics have been currently identified can be indicated during the course of model building by the iterative model building methods of the invention.

[0069] The reactions in a reaction network data structure or reaction database can be assigned to subsystems by annotation, if desired. The reactions can be subdivided according to biological criteria, such as according to traditionally identified metabolic pathways (glycolysis, amino acid metabolism and the like) or according to mathematical or computational criteria that facilitate manipulation of a model that incorporates or manipulates the reactions. Methods and criteria for subdividing a reaction database are described in further detail in Schilling et al., J. Theor. Biol. 203:249-283 (2000). The use of subsystems can be advantageous for a number of analysis methods, such as extreme pathway analysis, and can make the management of model content easier. Although assigning reactions to subsystems can be achieved without affecting the use of the entire model for simulation, assigning reactions to subsystems can allow a user to search for reactions in a particular subsystem, which may be useful in performing various types of analyses.
Therefore, a reaction network data structure can include any number of desired subsystems including, for example, 2 or more subsystems, 5 or more subsystems, 10 or more subsystems, 25 or more subsystems or 50 or more subsystems.

[0070] The reactions in a reaction network data structure or metabolic reaction database can be annotated with a value indicating the confidence with which the reaction is believed to occur in S. cerevisiae. The level of confidence can be, for example, a function of the amount and form of supporting data that is available. This data can come in various forms including published literature, documented experimental results, or results of computational analyses.
Furthermore, the data can provide direct or indirect evidence for the existence of a chemical reaction in a cell based on genetic, biochemical, and/or physiological data.

[0071] The invention further provides a computer readable medium, containing (a) a data structure relating a plurality of S. cerevisiae reactants to a plurality of S.
cerevisiae reactions, wherein each of the S. cerevisiae reactions includes a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating the substrate and the product, and (b) a constraint set for the plurality of S. cerevisiae reactions.

[0072] Constraints can be placed on the value of any of the fluxes in the metabolic network using a constraint set. These constraints can be representative of a minimum or maximum allowable flux through a given reaction, possibly resulting from a limited amount of an enzyme present. Additionally, the constraints can determine the direction or reversibility of any of the reactions or transport fluxes in the reaction network data structure.
Based on the in vivo environment where S. cerevisiae lives the metabolic resources available to the cell for biosynthesis of essential molecules for can be determined.
Allowing the corresponding transport fluxes to be active provides the in silico S.
cerevisiae with inputs and outputs for substrates and by-products produced by the metabolic network.

[00731 Returning to the hypothetical reaction network shown in Figure 1, constraints can be placed on each reaction in the exemplary format, shown in Figure 3, as follows. The constraints are provided in a format that can be used to constrain the reactions of the stoichiometric matrix shown in Figure 2. The format for the constraints used for a matrix or in linear programming can be conveniently represented as a linear inequality such as Rj <_ vj _<< ai : j = 1.... n (Eq. 1) where vj is the metabolic flux vector, R9 is the minimum flux value and aj is the maximum flux value. Thus, aj can take on a finite value representing a maximum allowable flux through a given reaction or (3j can take on a finite value representing minimum allowable flux through a given reaction. Additionally, if one chooses to leave certain reversible reactions or transport fluxes to operate in a forward and reverse manner the flux may remain unconstrained by setting 13j to negative infinity and aj to positive infinity as shown for reaction R2 in Figure 3. If reactions proceed only in the forward reaction J3j is set to zero while aj is set to positive infinity as shown for reactions R1, R3, R4, R5, and R6 in Figure 3. As an example, to simulate the event of a genetic deletion or non-expression of a particular protein, the flux through all of the corresponding metabolic reactions related to the gene or protein in question are reduced to zero by setting aj and [3;
to be zero. Furthermore, if one wishes to simulate the absence of a particular growth substrate, one can simply constrain the corresponding transport fluxes that allow the metabolite to enter the cell to be zero by setting aj and (3j to be zero. On the other hand if a substrate is only allowed to enter or exit the cell via transport mechanisms, the corresponding fluxes can be properly constrained to reflect this scenario.

[0074] The in silico S. cerevisiae model and methods described herein can be implemented on any conventional host computer system, such as those based on Intel®
microprocessors and running Microsoft Windows operating systems. Other systems, such as those using the UNIX or LINUX operating system and based on IBM®, DEC®
or Motorola® microprocessors are also contemplated. The systems and methods described herein can also be implemented to run on client-server systems and wide-area networks, such as the Internet.

[0075] Software to implement a method or model of the invention can be written in any well-known computer language, such as Java, C, C++, Visual Basic, FORTRAN or COBOL
and compiled using any well-known compatible compiler. The software of the invention normally runs from instructions stored in a memory on a host computer system.
A memory or computer readable medium can be a hard disk, floppy disc, compact disc, magneto-optical disc, Random Access Memory, Read Only Memory or Flash Memory. The memory or computer readable medium used in the invention can be contained within a single computer or distributed in a network. A network can be any of a number of conventional network systems known in the art such as a local area network (LAN) or a wide area network (WAN).
Client-server environments, database servers and networks that can be used in the invention are well known in the art. For example, the database server can run on an operating system such as UNIX, running a relational database management system, a World Wide Web application and a World Wide Web server. Other types of memories and computer readable media are also contemplated to function within the scope of the invention.

[0076] A database or data structure of the invention can be represented in a markup language format including, for example, Standard Generalized Markup Language (SGML), Hypertext markup language (HTML) or Extensible Markup language (XML). Markup languages can be used to tag the information stored in a database or data structure of the invention, thereby providing convenient annotation and transfer of data between databases and data structures. In particular, an XML format can be useful for structuring the data representation of reactions, reactants and their annotations; for exchanging database contents, for example, over a network or internet; for updating individual elements using the document object model; or for providing differential access to multiple users for different information content of a data base or data structure of the invention. XML programming methods and editors for writing XML code are known in the art as described, for example, in Ray, LearningXML O'Reilly and Associates, Sebastopol, CA (2001).

[00771 A set of constraints can be applied to a reaction network data structure to simulate the flux of mass through the reaction network under a particular set of environmental conditions specified by a constraints set. Because the time constants characterizing metabolic transients and/or metabolic reactions are typically very rapid, on the order of milli-seconds to seconds, compared to the time constants of cell growth on the order of hours to days, the transient mass balances can be simplified to only consider the steady state behavior.
Referring now to an example where the reaction network data structure is a stoichiometric matrix, the steady state mass balances can be applied using the following system of linear equations S = v = 0 (Eq. 2) where S is the stoichiometric matrix as defined above and v is the flux vector. This equation defines the mass, energy, and redox potential constraints placed on the metabolic network as a result of stoichiometry. Together Equations 1 and 2 representing the reaction constraints and mass balances, respectively, effectively define the capabilities and constraints of the metabolic genotype and the organism's metabolic potential. All vectors, v, that satisfy Equation 2 are said to occur in the mathematical nullspace of S. Thus, the null space defines steady-state metabolic flux distributions that do not violate the mass, energy, or redox balance constraints. Typically, the number of fluxes is greater than the number of mass balance constraints, thus a plurality of flux distributions satisfy the mass balance constraints and occupy the null space. The null space, which defines the feasible set of metabolic flux distributions, is further reduced in size by applying the reaction constraints set forth in Equation 1 leading to a defined solution space. A point in this space represents a flux distribution and hence a metabolic phenotype for the network. An optimal solution within the set of all solutions can be determined using mathematical optimization methods when provided with a stated objective and a constraint set. The calculation of any solution constitutes a simulation of the model.

[0078] Objectives for activity of S. cerevisiae can be chosen to explore the improved use of the metabolic network within a given reaction network data structure. These objectives can be design objectives for a strain, exploitation of the metabolic capabilities of a genotype, or physiologically meaningful objective functions, such as maximum cellular growth.
Growth can be defined in terms of biosynthetic requirements based on literature values of biomass composition or experimentally determined values such as those obtained as described above. Thus, biomass generation can be defined as an exchange reaction that removes intermediate metabolites in the appropriate ratios and represented as an objective function. In addition to draining intermediate metabolites this reaction flux can be formed to utilize energy molecules such as ATP, NADH and NADPH so as to incorporate any growth dependent maintenance requirement that must be met. This new reaction flux then becomes another constraint/balance equation that the system must satisfy as the objective function.
Using the stoichiometric matrix of Figure 2 as an example, adding such a constraint is analogous to adding the additional column Vgrowtl, to the stoichiometric matrix to represent fluxes to describe the production demands placed on the metabolic system.
Setting this new flux as the objective function and asking the system to maximize the value of this flux for a given set of constraints on all the other fluxes is then a method to simulate the growth of the organism.

[0079] Continuing with the example of the stoichiometric matrix applying a constraint set to a reaction network data structure can be illustrated as follows. The solution to equation 2 can be formulated as an optimization problem, in which the flux distribution that minimizes a particular objective is found. Mathematically, this optimization problem can be stated as:
Minimize Z (Eq. 3) where (Eq. 4) Z=Ici =V1 where Z is the objective which is represented as a linear combination of metabolic fluxes vi using the weights c; in this linear combination. The optimization problem can also be stated as the equivalent maximization problem; i.e. by changing the sign on Z. Any commands for solving the optimization problem can be used including, for example, linear programming commands.

[0080] A computer system of the invention can further include a user interface capable of receiving a representation of one or more reactions. A user interface of the invention can also be capable of sending at least one command for modifying the data structure, the constraint set or the commands for applying the constraint set to the data representation, or a combination thereof. The interface can be a graphic user interface having graphical means for making selections such as menus or dialog boxes. The interface can be arranged with layered screens accessible by making selections from a main screen. The user interface can provide access to other databases useful in the invention such as a metabolic reaction database or links to other databases having information relevant to the reactions or reactants in the reaction network data structure or to S. cerevisiae physiology. Also, the user interface can display a graphical representation of a reaction network or the results of a simulation using a model of the invention.

[0081] Once an initial reaction network data structure and set of constraints has been created, this model can be tested by preliminary simulation. During preliminary simulation, gaps in the network or "dead-ends" in which a metabolite can be produced but not consumed or where a metabolite can be consumed but not produced can be identified.
Based on the results of preliminary simulations areas of the metabolic reconstruction that require an additional reaction can be identified. The determination of these gaps can be readily calculated through appropriate queries of the reaction network data structure and need not require the use of simulation strategies, however, simulation would be an alternative approach to locating such gaps.

[0082] In the preliminary simulation testing and model content refinement stage the existing model is subjected to a series of functional tests to determine if it can perform basic requirements such as the ability to produce the required biomass constituents and generate predictions concerning the basic physiological characteristics of the particular organism strain being modeled. The more preliminary testing that is conducted the higher the quality of the model that will be generated. Typically the majority of the simulations used in this stage of development will be single optimizations. A single optimization can be used to calculate a single flux distribution demonstrating how metabolic resources are routed determined from the solution to one optimization problem. An optimization problem can be solved using linear programming as demonstrated in the Examples below. The result can be viewed as a display of a flux distribution on a reaction map. Temporary reactions can be added to the network to determine if they should be included into the model based on modeling/simulation requirements.

[0083] Once a model of the invention is sufficiently complete with respect to the content of the reaction network data structure according to the criteria set forth above, the model can be used to simulate activity of one or more reactions in a reaction network.
The results of a simulation can be displayed in a variety of formats including, for example, a table, graph, reaction network, flux distribution map or a phenotypic phase plane graph.

[0084] Thus, the invention provides a method for predicting a S. cerevisiae physiological function. The method includes the steps of (a) providing a data structure relating a plurality of S. cerevisiae reactants to a plurality of S. cerevisiae reactions, wherein each of the S.
cerevisiae reactions includes a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating said substrate and said product; (b) providing a constraint set for the plurality of S.
cerevisiae reactions; (c) providing an objective function, and (d) determining at least one flux distribution that minimizes or maximizes the objective function when the constraint set is applied to the data structure, thereby predicting a S. cerevisiae physiological function.

[0085] As used herein, the term "physiological function," when used in reference to S.
cerevisiae, is intended to mean an activity of a S. cerevisiae cell as a whole. An activity included in the term can be the magnitude or rate of a change from an initial state of a S.
cerevisiae cell to a final state of the S. cerevisiae cell. An activity can be measured qualitatively or quantitatively. An activity included in the term can be, for example, growth, energy production, redox equivalent production, biomass production, development, or consumption of carbon, nitrogen, sulfur, phosphate, hydrogen or oxygen. An activity can also be an output of a particular reaction that is determined or predicted in the context of substantially all of the reactions that affect the particular reaction in a S.
cerevisiae cell or substantially all of the reactions that occur in a S. cerevisiae cell.
Examples of a particular reaction included in the term are production of biomass precursors, production of a protein, production of an amino acid, production of a purine, production of a pyrimidine, production of a lipid, production of a fatty acid, production of a cofactor, or transport of a metabolite. A
physiological function can include an emergent property which emerges from the whole but not from the sum of parts where the parts are observed in isolation (see for example, Palsson Nat. Biotech 18:1147-1150 (2000)).

[0086] A physiological function of S. cerevisiae reactions can be determined using phase plane analysis of flux distributions. Phase planes are representations of the feasible set which can be presented in two or three dimensions. As an example, two parameters that describe the growth conditions such as substrate and oxygen uptake rates can be defined as two axes of a two-dimensional space. The optimal flux distribution can be calculated from a reaction network data structure and a set of constraints as set forth above for all points in this plane by repeatedly solving the linear programming problem while adjusting the exchange fluxes defining the two-dimensional space. A finite number of qualitatively different metabolic pathway utilization patterns can be identified in such a plane, and lines can be drawn to demarcate these regions. The demarcations defining the regions can be determined using shadow prices of linear optimization as described, for example in Chvatal, Linear Programming New York, W.H. Freeman and Co. (1983). The regions are referred to as regions of constant shadow price structure. The shadow prices define the intrinsic value of each reactant toward the objective function as a number that is either negative, zero, or positive and are graphed according to the uptake rates represented by the x and y axes. When the shadow prices become zero as the value of the uptake rates are changed there is a qualitative shift in the optimal reaction network.

[0087] One demarcation line in the phenotype phase plane is defined as the line of optimality (LO). This line represents the optimal relation between respective metabolic fluxes. The LO can be identified by varying the x-axis flux and calculating the optimal y-axis flux with the objective function defined as the growth flux. From the phenotype phase plane analysis the conditions under which a desired activity is optimal can be determined.
The maximal uptake rates lead to the definition of a finite area of the plot that is the predicted outcome of a reaction network within the environmental conditions represented by the constraint set. Similar analyses can be performed in multiple dimensions where each dimension on the plot corresponds to a different uptake rate. These and other methods for using phase plane analysis, such as those described in Edwards et al., Biotech Bioeng. 77:27-36(2002), can be used to analyze the results of a simulation using an in silico S. cerevisiae model of the invention.

[0088] A physiological function of S. cerevisiae can also be determined using a reaction map to display a flux distribution. A reaction map of S. cerevisiae can be used to view reaction networks at a variety of levels. In the case of a cellular metabolic reaction network a reaction map can contain the entire reaction complement representing a global perspective.
Alternatively, a reaction map can focus on a particular region of metabolism such as a region corresponding to a reaction subsystem described above or even on an individual pathway or reaction. An example of a reaction map showing a subset of reactions in a reaction network of S. cerevisiae is shown in Figure 4.

[0089] The invention also provides an apparatus that produces a representation of a S.
cerevisiae physiological function, wherein the representation is produced by a process including the steps of: (a) providing a data structure relating a plurality of S. cerevisiae reactants to a plurality of S. cerevisiae reactions, wherein each of the S.
cerevisiae reactions includes a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating said substrate and said product; (b) providing a constraint set for the plurality of S. cerevisiae reactions; (c) providing an objective function; (d) determining at least one flux distribution that minimizes or maximizes the objective function when the constraint set is applied to the data structure, thereby predicting a S. cerevisiae physiological function, and (e) producing a representation of the activity of the one or more S. cerevisiae reactions.

[0090] The methods of the invention can be used to determine the activity of a plurality of S. cerevisiae reactions including, for example, biosynthesis of an amino acid, degradation of an amino acid, biosynthesis of a purine, biosynthesis of a pyrimidine, biosynthesis of a lipid, metabolism of a fatty acid, biosynthesis of a cofactor, transport of a metabolite and metabolism of an alternative carbon source. In addition, the methods can be used to determine the activity of one or more of the reactions described above or listed in Table 2.
[0091] The methods of the invention can be used to determine a phenotype of a S.
cerevisiae mutant. The activity of one or more S. cerevisiae reactions can be determined using the methods described above, wherein the reaction network data structure lacks one or more gene-associated reactions that occur in S. cerevisiae. Alternatively, the methods can be used to determine the activity of one or more S. cerevisiae reactions when a reaction that does not naturally occur in S. cerevisiae is added to the reaction network data structure. Deletion of a gene can also be represented in a model of the invention by constraining the flux through the reaction to zero, thereby allowing the reaction to remain within the data structure. Thus, simulations can be made to predict the effects of adding or removing genes to or from S.
cerevisiae. The methods can be particularly useful for determining the effects of adding or deleting a gene that encodes for a gene product that performs a reaction in a peripheral metabolic pathway.

[0092] A drug target or target for any other agent that affects S. cerevisiae function can be predicted using the methods of the invention. Such predictions can be made by removing a reaction to simulate total inhibition or prevention by a drug or agent.
Alternatively, partial inhibition or reduction in the activity a particular reaction can be predicted by performing the methods with altered constraints. For example, reduced activity can be introduced into a model of the invention by altering the aj or 13j values for the metabolic flux vector of a target reaction to reflect a finite maximum or minimum flux value corresponding to the level of inhibition. Similarly, the effects of activating a reaction, by initiating or increasing the activity of the reaction, can be predicted by performing the methods with a reaction network data structure lacking a particular reaction or by altering the aj or [3j values for the metabolic flux vector of a target reaction to reflect a maximum or minimum flux value corresponding to the level of activation. The methods can be particularly useful for identifying a target in a peripheral metabolic pathway.

[0093] Once a reaction has been identified for which activation or inhibition produces a desired effect on S. cerevisiae function, an enzyme or macromolecule that performs the reaction in S. cerevisiae or a gene that expresses the enzyme or macromolecule can be identified as a target for a drug or other agent. A candidate compound for a target identified by the methods of the invention can be isolated or synthesized using known methods. Such methods for isolating or synthesizing compounds can include, for example, rational design based on known properties of the target (see, for example, DeCamp et al., Protein Engineering Principles and Practice, Ed. Cleland and Craik, Wiley-Liss, New York, pp. 467-506 (1996)), screening the target against combinatorial libraries of compounds (see for example, Houghten et al., Nature, 354, 84-86 (1991); Dooley et al., Science, 266, 2019-2022 (1994), which describe an iterative approach, or R. Houghten et al.
PCT/US91/08694 and U.S. Patent 5,556,762 which describe a positional-scanning approach), or a combination of both to obtain focused libraries. Those skilled in the art will know or will be able to routinely determine assay conditions to be used in a screen based on properties of the target or activity assays known in the art.

[0094] A candidate drug or agent, whether identified by the methods described above or by other methods known in the art, can be validated using an in silico S.
cerevisiae model or method of the invention. The effect of a candidate drug or agent on S.
cerevisiae physiological function can be predicted based on the activity for a target in the presence of the candidate drug or agent measured in vitro or in vivo. This activity can be represented in an in silico S. cerevisiae model by adding a reaction to the model, removing a reaction from the model or adjusting a constraint for a reaction in the model to reflect the measured effect of the candidate drug or agent on the activity of the reaction. By running a simulation under these conditions the holistic effect of the candidate drug or agent on S.
cerevisiae physiological function can be predicted.

[0095] The methods of the invention can be used to determine the effects of one or more environmental components or conditions on an activity of S. cerevisiae. As set forth above, an exchange reaction can be added to a reaction network data structure corresponding to uptake of an environmental component, release of a component to the environment, or other environmental demand. The effect of the environmental component or condition can be further investigated by running simulations with adjusted aj or 3j values for the metabolic flux vector of the exchange reaction target reaction to reflect a finite maximum or minimum flux value corresponding to the effect of the environmental component or condition. The environmental component can be, for example an alternative carbon source or a metabolite that when added to the environment of S. cerevisiae can be taken up and metabolized. The environmental component can also be a combination of components present for example in a minimal medium composition. Thus, the methods can be used to determine an optimal or minimal medium composition that is capable of supporting a particular activity of S.
cerevisiae.

[0096] The invention further provides a method for determining a set of environmental components to achieve a desired activity for S. cerevisiae. The method includes the steps of (a) providing a data structure relating a plurality of S. cerevisiae reactants to a plurality of S.
cerevisiae reactions, wherein each of the S. cerevisiae reactions includes a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating the substrate and the product; (b) providing a constraint set for the plurality of S. cerevisiae reactions; (c) applying the constraint set to the data representation, thereby determining the activity of one or more S. cerevisiae reactions (d) determining the activity of one or more S. cerevisiae reactions according to steps (a) through (c), wherein the constraint set includes an upper or lower bound on the amount of an environmental component and (e) repeating steps (a) through (c) with a changed constraint set, wherein the activity determined in step (e) is improved compared to the activity determined in step (d).

[0097] The following examples are intended to illustrate but not limit the present invention.

EXAMPLE I
Reconstruction of the metabolic network of S. cerevisiae [0098] This example shows how the metabolic network of S. cerevisiae can be reconstructed.

[0099] The reconstruction process was based on a comprehensive search of the current knowledge of metabolism in S. cerevisiae as shown in Figure 5. A reaction database was built using the available genomic and metabolic information on the presence, reversibility, localization and cofactor requirements of all known reactions. Furthermore, information on non-growth-dependent and growth-dependent ATP requirements and on the biomass composition was used.

[0100] For this purpose different online reaction databases, recent publications and review papers (Table 5 and 9), and established biochemistry textbooks (Zubay, Biochemistry Wm.C.
Brown Publishers, Dubuque, IA (1998); Stryer, Biochemistry W.H. Freeman, New York, NY
(1988)) were consulted. Information on housekeeping genes of S. cerevisiae and their functions were taken from three main yeast on-line resources:

= The MIPS Comprehensive Yeast Genome Database (CYGD) (Mewes et al., Nucleic Acids Research 30(1): 31-34 (2002));
= The Saccharonzyces Genome Database (SGD) (Cherry et al., Nucleic Acids Research 26(l): 73-9 (1998));
= The Yeast Proteome Database (YPD) (Costanzo et al., Nucleic Acids Research 29(1): 75-9 (2001)).

[0101] The following metabolic maps and protein databases (available online) were investigated:

= Kyoto Encyclopedia of Genes and Genomes database (KEGG) (Kanehisa et al., Nucleic Acids Research 28(1): 27-30 (2000));
= The Biochemical Pathways database of the Expert Protein Analysis System database (ExPASy) (Appel et al., Trends Biochem Sci. 19(6): 258-260 (1994));
= ERGO from Integrated Genomics (www.integratedgenomics.com) = SWISS-PROT Protein Sequence database (Bairoch et al., Nucleic Acids Research 28(1): 45-48 (2000)).

[0102] Table 5 lists additional key references that were consulted for the reconstruction of the metabolic network of S. cerevisiae.

Table 5 Amino acid biosynthesis Strathern et al., The Molecular biology of the yeast Saccharomyces metabolism and gene pression Cold Spring Harbor Laboratory, Cold Spring Harbor, N.Y. (1982)) Lipid synthesis Daum et al., Yeast 14(16): 1471-510 (1998);
Dickinson et al., The metabolism and molecular physiology of Saccharomyces cerevisiae Taylor & Francis, London; Philadelphia (1999);
Dickson et al., Methods Enzymol. 311:3-9 (2000);
Dickson, Annu Rev Biochem 67: 27-48 (1998);
Parks, CRC Crit Rev Microbiol 6(4): 301-41 (1978)) Nucleotide Metabolism Strathern et al., supara (1982)) Oxidative phosphorylation and electron transport (Verduyn et al., Antonie Van Leeuwenhoek 59(1): 49-63 (1991);
Overkamp et al., J. of Bacteriol 182(10): 2823-2830 (2000)) Primary Metabolism Zimmerman et al., Yeast sugar metabolism : biochemistry, genetics, biotechnology, and applications Technomic Pub., Lancaster, PA (1997);
Dickinson et al., su ra (1999);
Strathern et al., supra (1982)) Transport across the cytoplasmic membrane Paulsen et al., FEBS Lett 430(1-2): 116-125 (1998);
Wieczorke et al., FEBS Lett 464(3): 123-128 (1999);
Regenberg et al., Curr Genet 36(6): 317-328 (1999);
Andre, Yeast 11(16): 1575-1611 (1995)) Transport across the mitochondrial membrane Palmieri et al., J Bioenerg Biomembr 32(1): 67:77 (2000);
Palmieri et al., Biochim Biophys Acta 1459(2-3): 363-369 (2000);
Palmieri et al., J Biol Chem 274(32):22184-22190 (1999);
Palmieri et al., FEBS Lett 417(1): 114-118 (1997);
Paulsen et al., supra (1998);
Pallotta et al., FEBS Lett 428(3): 245-249 (1998);
Tzagologg et al. Mitochondria Plenum Press, New York (1982); Andre Yeast 11(16): 1575-611 (1995)) [0103] All reactions are localized into the two main compartments, cytosol and mitochondria, as most of the common metabolic reactions in S. cerevisiae take place in these compartments. Optionally, one or more additional compartments can be considered.
Reactions located in vivo in other compartments or reactions for which no information was available regarding localization were assumed to be cytosol. All corresponding metabolites were assigned appropriate localization and a link between cytosol and mitochondria was established through either known transport and shuttle systems or through inferred reactions to meet metabolic demands.

[0104] After the initial assembly of all the metabolic reactions the list was manually examined for resolution of detailed biochemical issues. A large number of reactions involve cofactors utilization, and for many of these reactions the cofactor requirements have not yet been completely elucidated. For example, it is not clear whether certain reactions use only NADH or only NADPH as a cofactor or can use both cofactors, whereas other reactions are known to use both cofactors. For example, a mitochondrial aldehyde dehydrogenase encoded by ALD4 can use both NADH and NADPH as a cofactor (Remize et al. Appl Environ Microbiol 66(8): 3151-3159 (2000)). In such cases, two reactions are included in the reconstructed metabolic network.

[0105] Further considerations were taken into account to preserve the unique features of S.
cerevisiae metabolism. S. cerevisiae lacks a gene that encodes the enzyme transhydrogenase.
Insertion of a corresponding gene from Azetobacter vinelandii in S. cerevisiae has a major impact on its phenotypic behavior, especially under anaerobic conditions (Niessen et al.
Yeast 18(1): 19-32 (2001)). As a result, reactions that create a net transhydrogenic effect in the model were either constrained to zero or forced to become irreversible.
For instance, the flux carried by NADH dependent glutamate dehydrogenase (Gdh2p) was constrained to zero to avoid the appearance of a net transhydrogenase activity through coupling with the NADPH
dependent glutamate dehydrogenases (Gdhlp and Gdh3p).

[0106] Once a first generation model is prepared, microbial behavior can be modeled for a specific scenario, such as anaerobic or aerobic growth in continuous cultivation using glucose as a sole carbon source. Modeling results can then be compared to experimental results. If modeling and experimental results are in agreement, the model can be considered as correct, and it is used for further modeling and predicting S. cerevisiae behavior. If the modeling and experimental results are not in agreement, the model has to be evaluated and the reconstruction process refined to determine missing or incorrect reactions, until modeling and experimental results are in agreement. This iterative process is shown in Figure 5 and exemplified below.

EXAMPLE II
Calculation of the P/O ratio [0107] This example shows how the genome-scale reconstructed metabolic model of S.
cerevisiae was used to calculate the P/O ratio, which measures the efficiency of aerobic respiration. The P/O ratio is the number of ATP molecules produced per pair of electrons donated to the electron transport system (ETS).

[0108] Linear optimization was applied, and the in silico P/O ratio was calculated by first determining the maximum number of ATP molecules produced per molecule of glucose through the electron transport system (ETS), and then interpolating the in silico P/O ratio using the theoretical relation (i.e. in S. cerevisiae for the P/O ratio of 1.5, 18 ATP molecules are produced).

[0109] Experimental studies of isolated mitochondria have shown that S.
cerevisiae lacks site I proton translocation (Verduyn et al., Antonie Van Leeuwenhoek 59(1): 49-63 (1991)).
Consequently, estimation of the maximum theoretical or "mechanistic" yield of the ETS
alone gives a P/O ratio of 1.5 for oxidation of NADH in S. cerevisiae grown on glucose (Verduyn et al., supra (1991)). However, based on experimental measurements, it has been determined that the net in vivo P/O ratio is approximately 0.95 (Verduyn et al., supra (1991)).
This difference is generally thought to be due to the use of the mitochondrial transmembrane proton gradient needed to drive metabolite exchange, such as the proton-coupled translocation of pyruvate, across the inner mitochondrial membrane. Although simple diffusion of protons (or proton leakage) would be surprising given the low solubility of protons in the lipid bilayer, proton leakage is considered to contribute to the lowered P/O
ratio due to the relatively high electrochemical gradient across the inner mitochondrial membrane (Westerhoff and van Dam, Thermodynamics and control of biological free-energy transduction Elsevier, New York, NY (1987)).

[0110] Using the reconstructed network, the P/O ratio was calculated to be 1.04 for oxidation of NADH for growth on glucose by first using the model to determine the maximum number of ATP molecules produced per molecule of glucose through the electron transport system (ETS) (YATP,max=12.5 ATP molecules/glucose molecule via ETS
in silico). The in silico P/O ratio was then interpolated using the theoretical relation (i.e. 18 ATP molecules per glucose molecule are produced theoretically when the P/O
ratio is 1.5).
The calculated P/O ratio was found to be close to the experimentally determined value of 0.95. Proton leakage, however, was not included in the model, which suggests that the major reason for the lowered P/O ratio is the use of the proton gradient for solute transport across the inner mitochondrial membrane. This result illustrates the importance of including the complete metabolic network in the analysis, as the use of the proton gradient for solute transport across the mitochondrial membrane contributes significantly to the operational P/O
ratio.

EXAMPLE III
Phenotypic phase plane analysis [0111] This example shows how the S. cerevisiae metabolic model can be used to calculate the range of characteristic phenotypes that the organism can display as a function of variations in the activity of multiple reactions.

[0112] For this analysis, 02 and glucose uptake rates were defined as the two axes of the two-dimensional space. The optimal flux distribution was calculated using linear programming (LP) for all points in this plane by repeatedly solving the LP
problem while adjusting the exchange fluxes defusing the two-dimensional space. A finite number of quantitatively different metabolic pathway utilization patterns were identified in the plane, and lines were drawn to demarcate these regions. One demarcation line in the phenotypic phase plane (PhPP) was defined as the line of optimality (LO), and represents the optimal relation between the respective metabolic fluxes. The LO was identified by varying the x-axis (glucose uptake rate) and calculating the optimal y-axis (02 uptake rate), with the objective function defined as the growth flux. Further details regarding Phase-Plane Analysis are provided in Edwards et al., Biotechnol. Bioeng. 77:27-36 (2002) and Edwards et al., Nature Biotech. 19:125-130 (2001)).

[0113] As illustrated in Figure 6, the S. cerevisiae PhPP contains 8 distinct metabolic phenotypes. Each region (P1-P8) exhibits unique metabolic pathway utilization that can be summarized as follows:

[0114] The left-most region is the so-called "infeasible" steady state region in the PhPP, due to stoichiometric limitations.

[0115] From left to right:

[0116] PI: Growth is completely aerobic. Sufficient oxygen is available to complete the oxidative metabolism of glucose to support growth requirements. This zone represents a futile cycle. Only CO2 is formed as a metabolic by-product. The growth rate is less than the optimal growth rate in region P2. The P1 upper limit represents the locus of points for which the carbon is completely oxidized to eliminate the excess electron acceptor, and thus no biomass can be generated.

[0117] P2: Oxygen is slightly limited, and all biosynthetic cofactor requirements cannot be optimally satisfied by oxidative metabolism. Acetate is formed as a metabolic by-product enabling additional high-energy phosphate bonds via substrate level phosphorylation.
With the increase of 02 supply, acetate formation eventually decreases to zero.

[0118] P3: Acetate is increased and pyruvate is decreased with increase in oxygen uptake rate.

[0119] P4: Pyruvate starts to increase and acetate is decreased with increase in oxygen uptake rate. Ethanol production eventually decreases to zero.

[0120] P5: The fluxes towards acetate formation are increasing and ethanol production is decreasing.

[0121] P6: When the oxygen supply increases, acetate formation increases and ethanol production decreases with the carbon directed toward the production of acetate.
Besides succinate production, malate may also be produced as metabolic by-product.
[0122] P7: The oxygen supply is extremely low, ethanol production is high and succinate production is decreased. Acetate is produced at a relatively low level.

[0123] P8: This region is along the Y-axis and the oxygen supply is zero. This region represents completely anaerobic fermentation. Ethanol and glycerol are secreted as a metabolic by-product. The role of NADH-consuming glycerol formation is to maintain the cytosol redox balance under anaerobic conditions (Van Dijken and Scheffers Yeast 2(2): 123-7 (1986)).

[0124] Line of Optimality: Metabolically, the line of optimality (LO) represents the optimal utilization of the metabolic pathways without limitations on the availability of the substrates. On an oxygen/glucose phenotypic phase plane diagram, LO represents the optimal aerobic glucose-limited growth of S. cerevisiae metabolic network to produce biomass from unlimited oxygen supply for the complete oxidation of the substrates in the cultivation processes. The line of optimality therefore represents a completely respiratory metabolism, with no fermentation by-product secretion and the futile cycle fluxes equals zero.

[0125] Thus, this example demonstrates that Phase Plane Analysis can be used to determine the optimal fermentation pattern for S. cerevisiae, and to determine the types of organic byproducts that are accumulated under different oxygenation conditions and glucose uptake rates.

EXAMPLE IV
Calculation of line of optimality and respiratory quotient [0126] This example shows how the S. cerevisiae metabolic model can be used to calculate the oxygen uptake rate (OUR), the carbon dioxide evolution rate (CER) and the respiration quotient (RQ), which is the ratio of CER over OUR.

[0127] The oxygen uptake rate (OUR) and the carbon dioxide evolution rate (CER) are direct indicators of the yeast metabolic activity during the fermentation processes. RQ is a key metabolic parameter that is independent of cell number. As illustrated in Figure 7, if the S. cerevisiae is grown along the line of optimality, LO, its growth is at optimal aerobic rate with all the carbon sources being directed to biomass formation and there are no metabolic by-products secreted except CO2. The calculated RQ along the LO is a constant value of 1.06; the RQ in P1 region is less than 1.06; and the RQ in the remaining regions in the yeast PhPP are greater than 1.06. The RQ has been used to determine the cell growth and metabolism and to control the glucose feeding for optimal biomass production for decades (Zeng et al. Biotechnol. Bioeng. 44:1107-1114 (1994)). Empirically, several researchers have proposed the values of 1.0 (Zigova, J Biotechnol 80: 55-62 (2000). Journal of Biotechnology), 1.04 (Wang et al., Biotechnol & Bioeng 19:69-86 (1977)) and 1.1 (Wang et al., Biotechnol. & Bioeng. 21:975-995 (1979)) as optimal RQ which should be maintained in fed-batch or continuous production of yeast's biomass so that the highest yeast biomass could be obtained (Dantigny et al., Appl. Microbiol. Biotechnol. 36:352-357 (1991)).
The constant RQ along the line of optimality for yeast growth by the metabolic model is thus consistent with the empirical formulation of the RQ through on-line measurements from the fermentation industry.

EXAMPLE V
Computer simulations [0128] This example shows computer simulations for the change of metabolic phenotypes described by the yeast PhPP.

[0129] A piece-wise linearly increasing function was used with the oxygen supply rates varying from completely anaerobic to fully aerobic conditions (with increasing oxygen uptake rate from 0 to 20 mmol per g cell-hour). A glucose uptake rate of 5 mmol of glucose per g (dry weight)-hour was arbitrarily chosen for these computations. As shown in Figure 8A, the biomass yield of the in silico S. cerevisiae strain was shown to increase from P8 to P2, and become optimal on the LO. The yield then started to slowly decline in P1 (futile cycle region). At the same time, the RQ value declines in relation to the increase of oxygen consumption rate, reaching a value of 1.06 on the LO1 and then further declining to become less than 1.

[0130] Figure 8B shows the secretion rates of metabolic by-products; ethanol, succinate, pyruvate and acetate with the change of oxygen uptake rate from 0 to 20 mmol of oxygen per g (dry weight)-h. Each one of these by-products is secreted in a fundamentally different way in each region. As oxygen increases from 0 in P7, glycerol production (data not shown in this figure) decreases and ethanol production increases. Acetate and succinate are also secreted.

EXAMPLE VI
Modeling of phenotypic behavior in chemostat cultures [0131] This example shows how the S. cerevisiae metabolic model can be used to predict optimal flux distributions that would optimize fermentation performance, such as specific product yield or productivity. In particular, this example shows how flux based analysis can be used to determine conditions that would minimize the glucose uptake rate of S. cerevisiae grown on glucose in a continuous culture under anaerobic and under aerobic conditions.
[0132] In a continuous culture, growth rate is equivalent to the dilution rate and is kept at a constant value. Calculations of the continuous culture of S. cerevisiae were performed by fixing the in silico growth rate to the experimentally determined dilution rate, and minimizing the glucose uptake rate. This formulation is equivalent to maximizing biomass production given a fixed glucose uptake value and was employed to simulate a continuous culture growth condition. Furthermore, a non growth dependent ATP maintenance of 1 mmol/gDW, a systemic P/O ratio of 1.5 (Verduyn et al. Antonie Van Leeuwenhoek 59(1): 49-63 (1991)), a polymerization cost of 23.92 mmol ATP/gDW, and a growth dependent ATP
maintenance of 35.36 mmol ATPIgDW, which is simulated for a biomass yield of 0.51 gDW/h, are assumed. The sum of the latter two terms is included into the biomass equation of the genome-scale metabolic model.

[0133] Optimal growth properties of S. cerevisiae were calculated under anaerobic glucose-limited continuous culture at dilution rates varying between 0.1 and 0.4 h71. The computed by-product secretion rates were then compared to the experimental data (Nissen et al. Microbiology 143(1): 203-18 (1997)). The calculated uptake rates of glucose and the production of ethanol, glycerol, succinate, and biomass are in good agreement with the independently obtained experimental data (Figure 9). The relatively low observed acetate and pyruvate secretion rates were not predicted by the in silico model since the release of these metabolites does not improve the optimal solution of the network.

[0134] It is possible to constrain the in silico model further to secrete both, pyruvate and acetate at the experimental level and recompute an optimal solution under these additional constraints. This calculation resulted in values that are closer to the measured glucose uptake rates (Figure 9A). This procedure is an example of an iterative data-driven constraint-based modeling approach, where the successive incorporation of experimental data is used to improve the in silico model. Besides the ability to describe the overall growth yield, the model allows further insight into how the metabolism operates. From further analysis of the metabolic fluxes at anaerobic growth conditions the flux through the glucose-6-phosphate dehydrogenase was found to be 5.32% of the glucose uptake rate at dilution rate of 0.1 h"1, which is consistent with experimentally determined value (6.34%) for this flux when cells are operating with fermentative metabolism (Nissen et al., Microbiology 143(1):

(1997)).

[0135] Optimal growth properties of S. cerevisiae were also calculated under aerobic glucose-limited continuous culture in which the Crabtree effect plays an important role. The molecular mechanisms underlying the Crabtree effect in S. cerevisiae are not known. The regulatory features of the Crabtree effect (van Dijken et al. Antonie Van Leeuwenhoek 63(3-4):343-52 (1993)) can, however, be included in the in silico model as an experimentally determined growth rate-dependent maximum oxygen uptake rate (Overkamp et al.
J. of Bacteriol 182(10): 2823-30 (2000))). With this additional constraint and by formulating growth in a chemostat as described above, the in silico model makes quantitative predictions about the respiratory quotient, glucose uptake, ethanol, C02, and glycerol secretion rates under aerobic glucose-limited continuous condition (Fig. 10).

EXAMPLE VII
Analysis of deletion of genes involved in central metabolism in S. cerevsiae [0136] This example shows how the S. cerevisiae metabolic model can be used to determine the effect of deletions of individual reactions in the network.

[0137] Gene deletions were performed in silico by constraining the flux(es) corresponding to a specific gene to zero. The impact of single gene deletions on growth was analysed by simulating growth on a synthetic complete medium containing glucose, amino acids, as well as purines and pyrimidines.

[0138] In silico results were compared to experimental results as supplied by the Saccharomyces Genome Database (SGD) (Cherry et al., Nucleic Acids Research 26(1):73-79 (1998)) and by the Comprehensive Yeast Genome Database (Mewes et al., Nucleic Acids Research 30(1):31-34 (2002)). In 85.6% of all considered cases (499 out of 583 cases), the in silico prediction was in qualitative agreement with experimental results. An evaluation of these results can be found in Example VIII. For central metabolism, growth was predicted under various experimental conditions and 81.5% (93 out of 114 cases) of the in silico predictions were in agreement with in vivo phenotypes.

[0139] Table 6 shows the impact of gene deletions on growth in S. cerevisiae.
Growth on different media was considered, including defined complete medium with glucose as the carbon source, and minimal medium with glucose, ethanol or acetate as the carbon source.
The complete reference citations for Table 6 can be found in Table 9.

[0140] Thus, this example demonstrates that the in silico model can be used to uncover essential genes to augment or circumvent traditional genetic studies.

Table 6 Defined Medium Complete Minimal Minimal Minimal Carbon Source Glucose Glucose Acetate Ethanol Gene in silico/ in silicol in silicol in silicol References:
in vivo in vivo in vivo in vivo (Minimal media) ACO1 +/+ -/- (Gangloff et al., 1990) CDC19# +/- +/- (Boles et al., 1998) CITI +/+ +/+ (Kim et al., 1986) CIT2 +/+ +/+ (Kim et al., 1986) CIT3 +/+
DAL7 +/+ +/+ +/+ +/+ (Hartig et al., 1992) ENO1 +/+
ENO2$$ +l- +/-FBA1 * +/- +/-(Sedivy and Fraenkel, 1985;
FBPI +/+ +l+ Gancedo and Delgado, 1984) FUMI +/+
GLKI +/+
GND1## +/- +/-GND2 +l+
GPM111 +/- +/-GPM2 +/+
GPM3 +/+
HXK1 +/+
HXK2 +/+

ICLI +/+ +/+ (Smith et al., 1996) IDH1 +l+ +l+ (Cupp and McAlister-Henn, 1992) IDH2 +l+ +l+ (Cupp and McAlister-Henn, 1992) IDPI +/+ +/+ (Loftus et al., 1994) IDP2 +/+ +/+ (Loftus et al., 1994) IDP3 +l+
KGD1 +l+ +l+ (Repetto and Tzagoloff, 1991) KGD2 +/+ +/+ (Repetto and Tzagoloff, 1991) LPDI +/+
LSC1 +/+ +/+ +l+ (Przybyla-Zawislak et al., 1998) LSC2 +l+ +/+ +/+ (Przybyla-Zawislak et al., 1998) MAE] +/+ +/+ +/+ (Boles et al., 1998) MDH1 +/+ +/+ +/- (McAlister-Henn and Thompson, 1987) MDH2 +l+ (McAlister-Henn and Thompson, 1987) MDH3 +/+
MLS1 +/+ +/+ +/+ +/+ (Hartig et al., 1992) OSM1 +l+
PCK1 +/+
PDCI +l+ +l+ (Flikweert et al., 1996) PDC5 +/+ +/+ (Flikweert et al., 1996) PDC6 +l+ +l+ (Flikweert et al., 1996) PFKI +/+ +/+ (Clifton and Fraenkel, 1982) PFK2 +/+ +/+ (Clifton and Fraenkel, 1982) PGI1 * & +/- +/- (Clifton et al., 1978) PGK1 * +/- +/-PGMI +l+ +l+ (Boles et al., 1994) PGM2 +/+ +/+ (Boles et al., 1994) PYCI +l+ +/+ (Wills and Melham, 1985) PYC2 +/+
PYK2 +/+ +/+ +/+ (Boles et al., 1998; McAlister-Henn and Thompson, 1987) RKI1 -l-RPEI +l+
SOL1 +/+
SOL2 +/+
SOL3 +/+
SOLO +l+
TALI +/+ +/+ (Schaaff-Gerstenschlager and Zimmermann, 1993) TDHJ +/+
TDH2 +/+
TDH3 +l+
TKL1 +/+ +/+ (Schaff-Gerstenschlager and Zimmermann, 1993) TKL2 +/+
TPII i'$ +/-ZWF1 +/+ +l+ (Schaaff-Gerstenschlager and Zimmermann, 1993) +/- Growth/no growth # The isoenyzme Pyk2p is glucose repressed, and cannot sustain growth on glucose.
* Model predicts single deletion mutant to be (highly) growth retarded.

$ Growth of single deletion mutant is inhibited by glucose.
& Different hypotheses exist for why Pgilp deficient mutants do not grow on glucose, e.g. the pentose phosphate pathway in S. cerevisiae is insufficient to support growth and cannot supply the EMP
pathway with sufficient amounts of fructose-6-phosphate and glyceraldehydes-3-phosphate (Boles, 1997).
The isoenzymes Gpm2p and Gpm3p cannot sustain growth on glucose. They only show residual in vivo activity when they are expressed from a foreign promoter (Heinisch et al., 1998).
## Gndlp accounts for 80% of the enzyme activity. A mutant deleted in GND1 accumulates gluconate-6-phosphate, which is toxic to the cell (Schaaff-Gerstenschlager and Miosga, 1997).

$$ ENO 1 plays central role in gluconeogenesis whereas ENO2 is used in glycolysis (Muller and Entian, 1997).

EXAMPLE VIII
Large-scale gene deletion analysis in S. cerevisiae [0141] A large-scale in silico evaluation of gene deletions in S. cerevisiae was conducted using the genome-scale metabolic model. The effect of 599 single gene deletions on cell viability was simulated in silico and compared to published experimental results. In 526 cases (87.8%), the in silico results were in agreement with experimental observations when growth on synthetic complete medium was simulated. Viable phenotypes were predicted in 89.4% (496 out of 555) and lethal phenotypes are correctly predicted in 68.2%
(30 out of 44) of the cases considered.

[0142] The failure modes were analyzed on a case-by-case basis for four possible inadequacies of the in silico model: 1) incomplete media composition; 2) substitutable biomass components; 3) incomplete biochemical information; and 4) missing regulation.
This analysis eliminated a number of false predictions and suggested a number of experimentally testable hypotheses. The genome-scale in silico model of S.
cerevisiae can thus be used to systematically reconcile existing data and fill in knowledge gaps about the organism.

[0143] Growth on complete medium was simulated under aerobic condition. Since the composition of a complete medium is usually not known in detail, a synthetic complete medium containing glucose, twenty amino acids (alanine, arginine, asparagine, aspartate, cysteine, glutamine, glutamate, glycine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, proline, serine, threonine, tryptophane, tyrosine, valine) and purines (adenine and guanine) as well as pyrimidines (cytosine and thymine) was defined for modeling purposes. Furthermore, ammonia, phosphate, and sulphate were supplied. The in silico results were initially compared to experimental data from a competitive growth assay (Winzeler et al., Science 285:901-906 (1999)) and to available data from the IMPS and SGD
databases (Mewes et al., Nucleic Acids Research 30(1):31-34 (2002); Cherry et al., Nucleic Acids Research 26(1):73-79 (1998)). Gene deletions were simulated by constraining the flux through the corresponding reactions to zero and optimizing for growth as previously described (Edwards and Palsson, Proceedings of the National Academy of Sciences 97(10):5528-5533 (2000)). For this analysis, a viable phenotype was defined as a strain that is able to meet all the defined biomass requirements and thus grow. Single gene deletion mutants that have a reduced growth rate compared to the wild type simulation are referred to as growth retarded mutants.

[0144] The analysis of experimental data was approached in three steps:

^ The initial simulation using the synthetic medium described above, referred to as simulation 1.
^ False predictions of simulation 1 were subsequently examined to determine if the failure was due to incomplete information in the in silico model, such as missing reactions, the reversibility of reactions, regulatory events, and missing substrates in the synthetic complete medium. In simulation 2, any such additional information was introduced into the in silico model and growth was re-simulated for gene deletion mutants whose in silico phenotype was not in agreement with its in vivo phenotype.
^ A third simulation was carried out, in which dead end pathways (i.e.
pathways leading to intracellular metabolites that were not further connected into the overall network), were excluded from the analysis (simulation 3).

[0145] The effect of single gene deletions on the viability of S. cerevisiae was investigated for each of the 599 single gene deletion mutants. The in silico results were categorized into four groups:

1. True negatives (correctly predicted lethal phenotype);
2. False negatives (wrongly predicted lethal phenotype);

3. True positives (correctly predicted viable phenotypes);
4. False positives (wrongly predicted viable phenotypes).

[0146] In simulation 1, 509 out of 599 (85%) simulated phenotypes were in agreement with experimental data. The number of growth retarding genes in simulation 1 was counted to be 19, a surprisingly low number. Only one deletion, the deletion of TPI1, had a severe impact on the growth rate. Experimentally, a deletion in TPI1 is lethal (Ciriacy and Breitenbach, J Bacteriol 139(1):152-60 (1979)). In silico, a tpil mutant could only sustain a specific growth rate of as low as 17% of the wild type. All other growth retarding deletions sustained approximately 99% of wild type growth, with the exception of a deletion of the mitochondrial ATPase that resulted in a specific growth rate of approximately 90% of wild type.

[0147] Predictions of simulation 1 were evaluated in a detailed manner on a case-by-case basis to determine whether the false predictions could be explained by:

1. Medium composition used for the simulation;
2. The biomass composition used in the simulation;
3. Incomplete biochemical information; and 4. Effects of gene regulation.

[0148] Analysis of the false predictions from simulation 1 based on these possible failure modes resulted in model modifications that led to 526 out of 599 correctly predicted phenotypes (87.8%), i.e. simulation 2.

[0149] Simulation 3 uncovered some 220 reactions in the reconstructed network that are involved in dead end pathways. Removing these reactions and their corresponding genes from the genome-scale metabolic flux balance model, simulation 3 resulted in 473 out of 530 (89.6%) correctly predicted phenotypes of which 91.4% are true positive and 69.8% are true negative predictions.

[0150] Table 7 provides a summary of the large-scale evaluation of the effect of in silico single gene deletions in S. cerevisiae on viability.

Table 7 Genes Simulation 1 2 involved in 3 dead end pathways Number of deletion 599 599 530 Predicted Total 509 526 475 True positive 481 496 51 445 True negative 28 30 0 30 False positive 63 59 17 42 False negative 27 14 1 13 Overall Prediction 85.0% 87.8% 89.6%
Positive Prediction 88.4% 89.4% 91.4%
Negative Prediction 50.9% 68.2% 69.8%

[01511 A comprehensive list of all the genes used in the in silico deletion studies and results of the analysis are provided in Table 8. Table 8 is organized according to the categories true negative, false negative, true positive and false positive predictions. Genes highlighted in grey boxes, such asINOI', corresponded initially to false predictions (simulation 1); however, evaluation of the false prediction and simulation 2 identified these cases as true predictions. ORFs or genes that are in an open box, such as TRR2 were excluded in simulation 3, as the corresponding reactions catalysed steps in dead end pathways.

Table 8 False Positive ACS2 FU-RI BET2 CDC19 CDC21 CDC8 CYRl ED-8-1] DFRI IM--11 DUT1 YSI ENO22 ER 10 FADI FMN1 FOLI FOL2 FOL3 GFAI GPM1EMlEM12 EM13 EMI EM2 EM3 EM4 HIP1 QRIl RER2 V5 U

False Negative True Negative ACCT ADE13 CDS1 DPM1 ERG1 ERG7 ERGS ERG9 ERGll ERG12 ERG20 ERG25 ERG26 ERG27 GLNI GUKI IDIJ IPPI MVD1 PGI1 PGK1 PIS] PMI40 PSAI RKI1 SAHI SEC53 TRR1 YDR531 W

True Positive ALP] ASP] ATH1 ATP1 BAP2 BAP3 BATI BAT2 BGL2 I02 WI-0-31 IO ZO NA1 CANT CARL

CDDl CEM1 CHA1 CHS1 CHS2 CHS3 CITI CIT2 CIT3 CKII COQJ COQ2 CO 3 FCOQ CO COX]
COXIO CPA2 CPTI CRCI CRDI CSG2 CTA1 CTP1 CTTI CYB2 CYS3 CYS4 DAK1 DAK2 DAL]

DAL3 DAL4 AL DAL7 DCDI DEG1 DICI DIP5 DLDI PH5 DPL1 DURI DUR3 ECM17 ECM.31 ECM40 ECT1 Jul ENO1 PT1 ERG2 ERG24, ERR] ERR2 EXGI EXG2 FAAI FAA2 FAA3 FAA4 F'ASi FBP1 FBP26 FCY1 FCY2 FKS1 FKS3 FLXI MT1 FO-X-21 FRDS FUII FUMI FUN63 GAD] GAL] GALIO GAL2 GAL7 GAP] GCVI GCV2 GDHI GDH2 GDH3 GLC3 GLKI GLO1 GLO2 GSH2 GSYI GSY2 GUA1 GUT] GUT2 EM--14 HISI HIS2 HIS3 HIS4 HIS5 HIS6 HIS7 HMGI

HXT17 HXT2 HXT3 H)T4 HXT5 HXT6 HXT7 HXTS HXT9 HYRI ICL1 ICL2 IDHI IDP1 IDP2 ILV2 J' NO PTI ITR1 ITR2 JENI KGD1 KRE2 KTR1 KTR2 KTR3 KTR4 KTR6 LCB3 LCB4 LCBS EUI
LEU2 LEU4 PD1 PPl LSCI LSC2 LYPI LYSI LYS12 LYS2 LYS20 LYS21 LYS4 LYS9 MAE1 MA

MET16 MET] 7 ME T2 MET22 ME T3 ME T7 MHTI MIRI MIST MLSI MPI SEI SKl MSRI SWI

ORTI OSM1 PAD] PCK1 CTl PDA] PDCI PDC5 PDC6 PDE1 PDE2 PDX3 PFK1 PFK2 PFK26 PMTS
PMT6 NCI PNP1 POS5 FO-TI PPA2 PRM4 PRM5 PRM6 PRO] PRO2 PRSI PRS2 PRS3 PRS4 FSD2 PTR2 PUR5 PUS] PUS2 PUS4 PUT] PUT2 PUT4 PYCI PYC2 PYK2 QPT1 RAM] RBK1 SUL1 SUL2 SUR] SUR2 TALI TATI TAT2 TDH1 TDH2 TDH3 THI20 THI21 THI22 THI6 THI7 THR1 THR4 TKLI TKL2 TOR] TPSI 1'P52 TPS3 TRK1 TRP1 TRP2 TRP3 TRP4 TRPS TRR2 TSLI TYRI
UGA1 UGA4 URA! URA2 URA3 URA4 U-RA5 URA7 URA8 URA10 URH1 URK1 UTRI VAPI

YFR055W YGRO12W YGR043C YGR125W YGR287CY1L145C. YIL167W YJL070C YJL200C

[0152] The following text describes the analysis of the initially false predictions of simulation 1 that were performed, leading to simulation 2 results.

Influence of media composition on simulation results:

[0153] A rather simple synthetic complete medium composition was chosen for simulation 1. The in silico medium contained only glucose, amino acids and nucleotides as the main components. However, complete media often used for experimental purposes, e.g.
the YPD medium containing yeast extract and peptone, include many other components, which are usually unknown.

[0154] False negative predictions: The phenotype of the following deletion mutants:
ecrnlA, yi1145cA, erg2 A, erg24 A, fasl A, ural A, ura2 A, ura3 A and ura4 A
were falsely predicted to be lethal in simulation 1. In simulation 2, an additional supplement of specific substrate could rescue a viable phenotype in silico and as the supplemented substrate may be assumed to be part of a complex medium, the predictions were counted as true. positive predictions in simulation 2. For example, both Ecml and Yi1145c are involved in pantothenate synthesis. Ecml catalyses the formation of dehydropantoate from 2-oxovalerate, whereas Yill45c catalyses the final step in pantothenate synthesis from R-alanine and panthoate. In vivo, ecrl A, and yil145c A mutants require pantothenate for growth (White et al., J Biol Chem 276(14): 10794-10800 (2001)). By supplying pantothenate to the synthetic complete medium in silico, the model predicted a viable phenotype and the growth rate was similar to in silico wild type S. cerevisiae.

[0155] Similarly other false predictions could be traced to medium composition:

= Mutants deleted in ERG2 or ERG24 are auxotroph for ergosterol (Silve et al., Mol Cell Biol 16(6): 2719-2727 (1996); Bourot and Karst, Gene 165(1): 97-102 (1995)).
Simulating growth on a synthetic complete medium supplemented with ergosterol allowed the model to accurately predict viable phenotypes.
= A deletion of FAS1 (fatty acid synthase) is lethal unless appropriate amounts of fatty acids are provided, and by addition of fatty acids to the medium, a viable phenotype was predicted.
= Strains deleted in URA1, URA2, URA3, or URA4 are auxotroph for uracil (Lacroute, J Bacteriol 95(3): 824-832 (1968)), and by supplying uracil in the medium the model predicted growth.

[0156] The above cases were initially false negative predictions, and simulation 2 demonstrated that these cases were predicted as true positive by adjusting the medium composition.

[0157] False positive predictions: Simulation 1 also contained false positive predictions, which may be considered as true negatives or as true positives. Contrary to experimental results from a competitive growth assay (Winzeler et al., Science 285: 901-906 (1999)), mutants deleted in ADE13 are viable in vivo on a rich medium supplemented with low concentrations of adenine, but grow poorly (Guetsova et al., Genetics 147(2):

(1997)). Adenine was supplied in the in silico synthetic complete medium. By not supplying adenine, a lethal mutant was predicted. Therefore, this case was considered as a true negative prediction.

[0158] A similar case was the deletion of GLN1, which codes a glutamine synthase, the only pathway to produce glutamine from ammonia. Therefore, glnl d mutants are glutamine auxotroph (Mitchell, Genetics 111(2):243-58 (1985)). In a complex medium, glutamine is likely to be deaminated to glutamate, particularly during autoclaving. Complex media are therefore likely to contain only trace amounts of glutamine, and glnld mutants are therefore not viable. However, in silico, glutamine was supplied in the complete synthetic medium and growth was predicted. By not supplying glutamine to the synthetic complete medium, the model predicted a lethal phenotype resulting in a true negative prediction.

[0159] Ilv3 and Ilv5 are both involved in branched amino acid metabolism. One may expect that a deletion of IL V3 or IL V5 could be rescued with the supply of the corresponding amino acids. For this, the model predicted growth. However, contradictory experimental data exists. In a competitive growth assay lethal phenotypes were reported.
However, earlier experiments showed that ilv3O and ilv5i\ mutants could sustain growth when isoleucine and valine were supplemented to the medium, as for the complete synthetic medium. Hence, these two cases were considered to be true positive predictions.

Influence of the definition of the biomass equation [0160] The genome-scale metabolic model contains the growth requirements in the form of biomass composition. Growth is defined as a drain of building blocks, such as amino acids, lipids, nucleotides, carbohydrates, etc., to form biomass. The number of biomass components is 44 (see Table 1). These building blocks are essential for the formation of cellular components and they have been used as a fixed requirement for growth in the in silico simulations. Thus, each biomass component had to be produced by the metabolic network otherwise the organism could not grow in silico. In vivo, one often finds deletion mutants that are not able to produce the original biomass precursor or building block;
however, other metabolites can replace these initial precursors or building blocks. Hence, for a number of strains a wrong phenotype was predicted in silico for this reason.

[0161] Phosphatidylcholine is synthesized by three methylation steps from phosphatidylethanolamine (Dickinson and Schweizer, The metabolism and molecular physiology of Saccharomyces cerevisiae Taylor & Francis, London ; Philadelphia (1999)).
The first step in the synthesis of phosphatidylcholine from phosphatidylethanolamine is catalyzed by a methyltransferase encoded by CHO2 and the latter two steps are catalyzed by phospholipid methyltransferase encoded by OPI3. Strains deleted in CHO2 or OPI3 are viable (Summers et al., Genetics 120(4): 909-922 (1988); Daum et al., Yeast 14(16): 1471-1510 (1998)); however, either null mutant accumulates mono- and dimethylated phosphatidylethanolamine under standard conditions and display greatly reduced levels of phosphatidylcholine (Daum et al., Yeast 15(7): 601-614 (1999)). Hence, phosphatidylethanolamine can replace phosphatidylcholine as a biomass component. In silico, phosphatidylcholine is required for the formation of biomass. One may further speculate on whether an alternative pathway for the synthesis of phosphatidylcholine is missing in the model, since Daum et al., supra (1999) detected small amounts of phosphatidylcholine in cho20 mutants. An alternative pathway, however, was not included in the in silico model.

[0162] Deletions in the ergosterol biosynthetic pathways of ERG3, ERGO, ERG5 or ERG6 lead in vivo to viable phenotypes. The former two strains accumulate ergosta-8,22,24 (28)-trien-3-beta-ol (Bard et al., Lipids 12(8): 645-654 (1977); Zweytick et al., FEBS Lett 470(1):
83-87 (2000)), whereas the latter two accumulate ergosta-5,8-dien-3beta-ol (Hata et al., J
Biochem (Tokyo) 94(2): 501-510 (1983)), or zymosterol and smaller amounts of cholesta-5,7,24-trien-3-beta-ol and cholesta-5,7,22,24-trien-3-beta-ol (Bard et al., supra (1977); Parks et al., Crit Rev Biochem Mol Biol 34(6): 399-404 (1999)), respectively, components that were not included in the biomass equations.

[0163] The deletion of the following three genes led to false positive predictions: RER2, SEC59 and QIRJ. The former two are involved in glycoprotein synthesis and the latter is involved in chitin metabolism. Both chitin and glycoprotein are biomass components.
However, for simplification, neither of the compounds was considered in the biomass equation. Inclusion of these compounds into the biomass equation may improve the prediction results.

Incomplete biochemical information [0164] For a number of gene deletion mutants (inmlA, met6A, ynkl A, pho84O. psd2A, tps2A) , simulation 1 produced false predictions that could not be explained by any of the two reasons discussed above nor by missing gene regulation (see below). Further investigation of the metabolic network including an extended investigation of biochemical data from the published literature showed that some information was missing initially in the in silico model or information was simply not available.

[0165] Inml catalyses the ultimate step in inositol biosynthesis from inositol 1-phosphate to inositol (Murray and Greenberg, Mol Microbiol 36(3): 651-661 (2000)). Upon deleting INMI, the model predicted a lethal phenotype in contrary to the experimentally observed viable phenotype. An isoenzyme encoded by IMP2 was initially not included in the model, which may take over the function of INMI and this addition would have led to a correct prediction. However, an inml Aimp2A in vivo double deletion mutant is not inositol auxotroph (Lopez et al., Mol Microbiol 31(4): 1255-1264 (1999)). Hence, it appears that alternative routes for the production of inositol probably exist. Due to the lack of comprehensive biochemical knowledge, effects on inositol biosynthesis and the viability of strains deleted in inositol biosynthetic genes could not be explained.

[0166] Met6A mutants are methionine auxotroph (Thomas and Surdin-Kerjan, Microbiol Mol Biol Rev 61(4):503-532 (1997)), and growth may be sustained by the supply of methionine or S-adenosyl-L-methionine. In silico growth was supported neither by the addition of methionine nor by the addition of S-adenosyl-L-methionine.
Investigation of the metabolic network showed that deleting MET6 corresponds to deleting the only possibility for using 5-methyltetrahydrofolate. Hence, the model appears to be missing certain information. A possibility may be that the carbon transfer is carried out using 5-methyltetrahydropteroyltri-L-glutamate instead of 5-methyltetrahydrofolate. A
complete pathway for such a by-pass was not included in the genome-scale model.

[0167] The function of Ynkip is the synthesis of nucleoside triphosphates from nucleoside diphosphates. YNKlA mutants have a 10-fold reduced Ynklp activity (Fukuchi et al., Genes 129(1):141-146 (1993)), though this implies that there may either be an alternative route for the production of nucleoside triphosphates or a second nucleoside diphosphate kinase, even though there is no ORF in the genome with properties that indicates that there is a second nucleoside diphosphate kinase. An alternative route for the production of nucleoside triphosphate is currently unknown (Dickinson et al., supra (1999)), and was therefore not included in the model, hence a false negative prediction.

[0168] PH084 codes for a high affinity phosphate transporter that was the only phosphate transporter included in the model. However, at least two other phosphate transporters exist, a second high affinity and Na+ dependent transporter Pho89 and a low affinity transporter (Persson et al., Biochim Biophys Acta 1422(3): 255-72 (1999)). Due to exclusion of these transporters a lethal pho840 mutant was predicted. Including PH089 and a third phosphate transporter, the model predicted a viable deletion mutant.

[0169] In a null mutant of PSD2, phosphatidylethanolamine synthesis from phosphatidylserine is at the location of Psd1 (Trotter et al., J Biol Chem 273(21): 13189-13196 (1998)), which is located in the mitochondria. It has been postulated that phosphatidylserine can be transported into the mitochondria and phosphatidylethanolamine can be transported out of the mitochondria. However, transport of phosphatidylethanolamine and phosphatidylserine over the mitochondrial membrane was initially not included in the model. Addition of these transporters to the genome-scale flux balance model allowed in silico growth of a PSD2 deleted mutant.

[0170] Strains deleted in TPS2 have been shown to be viable when grown on glucose (Bell et al., J Biol Chem 273(50): 33311-33319 (1998)). The reaction carried out by Tps2p was modeled as essential and as the final step in trehalose synthesis from trehalose 6-phosphate. However, the in vivo viable phenotype shows that other enzymes can take over the hydrolysis of trehalose 6-phosphate to trehalose from Tps2p (Bell et al., supra (1998)).
The corresponding gene(s) are currently unknown. Inclusion of a second reaction catalyzing the final step of trehalose formation allowed for the simulation of a viable phenotype.

[0171] Strains deleted in ADE3 (C 1 -tetrahydrofolate synthase) and ADK1 (Adenylate kinase) could not be readily explained. It is possible that alternative pathways or isoenzyme-coding genes for both functions exist among the many orphan genes still present in the S.
cerevisiae.

[0172] The reconstruction process led to some incompletely modeled parts of metabolism.
Hence, a number of false positive predictions may be the result of gaps (missing reactions) within pathways or between pathways, which prevent the reactions to completely connect to the overall pathway structure of the reconstructed model. Examples include:

= Sphingolipid metabolism. It has not yet been fully elucidated and therefore was not included completely into the model nor were sphingolipids considered as building blocks in the biomass equation.
= Formation of tRNA. During the reconstruction process some genes were included responsible for the synthesis of tRNA (DED81, HTS1, KRSJ, YDR41 C, YGL245 go.
= However, pathways of tRNA synthesis were not fully included.
= Heme synthesis was considered in the reconstructed model (HEM], HEM12, HEM13, HEM15, HEM2, HEM3, HEM4). However no reaction was included that metabolized heme in the model.
= Hence, the incomplete structure of metabolic network may be a reason for false prediction of the phenotype of aurl , lcbld, lcb2A, tsc]Oi, ded8ld, htslzl, krslA, ydr4lcd, ygl245wd, herald, heml2A, heml3A, heml5d, hem2d, hem3d, and hem4d deletion mutants. Reaction reversibility. The CHOI gene encodes a phosphatidylserine synthase, an integral membrane protein that catalyses a central step in cellular phospholipid biosynthesis. In vivo, a deletion in CHOI is viable (Winzeler et al., Science 285: 901-906 (1999)). However, mutants are auxotrophic for choline or ethanolamine on media containing glucose as the carbon source (Birner et al., Mol Biol Cell 12(4): 997-1007 (2001)).
= Nevertheless, the model did not predict growth when choline and/or ethanolamine were supplied. Further investigation of the genome-scale model showed that this might be due to defining reactions leading from phosphatidylserine to phosphatidylcholine via phosphatidylethanolamine exclusively irreversible. By allowing these reactions to be reversible, either supply of choline and ethanolamine could sustain growth in silico.

Gene Regulation [0173] Whereas many false negative predictions could be explained by either simulation of growth using the incorrect in silico synthetic complete medium or by initially missing information in the model, many false positives may be explained by in vivo catabolite expression, product inhibition effects or by repressed isoenzymes, as kinetic and other regulatory constraints were not included in the genome-scale metabolic model.

[0174] A total of 17 false positive predictions could be related to regulatory events. For a deletion of CDC19, ACS2 or ENO2 one may usually expect that the corresponding isoenzymes may take over the function of the deleted genes. However, the corresponding genes, either PYK2, ACS 1 or ENO 1, respectively, are subject to catabolite repression (Boles et al., J Bacteriol 179(9): 2987-2993 (1997); van den Berg and Steensma, Eur J
Biochem 231(3): 704-713 (1995); Zimmerman et al., Yeast sugar metabolism : biochemistr , genetics, biotechnology, and applications Technomic Pub., Lancaster, PA (1997)). A
deletion of GPM1 should be replaced by either of the two other isoenzymes, Gpm2 and Gpm3;
however for the two latter corresponding gene products usually no activity is found (Heinisch et al., Yeast 14(3): 203-13 (1998)).

[0175] Falsely predicted growth phenotypes can often be explained when the corresponding deleted metabolic genes are involved in several other cell functions, such as cell cycle, cell fate, communication, cell wall integrity, etc. The following genes whose deletions yielded false positive predictions were found to have functions other than just metabolic function: ACS2, BET2, CDC19, CDC8, CYR], DIM], ENO2, FAD], GFA1, GPM1, HIP], MSS4, PET9, PIK], PMAJ, STT4, TOR2. Indeed, a statistical analysis of the MIPS functional catalogue (http://mips.gsf.de/proj/yeast/) showed that in general it was more likely to have a false prediction when the genes that had multiple functions were involved in cellular communication, cell cycling and DNA processing or control of cellular organization.
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[0176]
[0177]

Claims (51)

THE EMBODIMENTS FOR WHICH AN EXCLUSIVE PROPERTY OR PRIVILEGE
ARE CLAIMED ARE DEFINED AS FOLLOWS:

1. A computer readable medium or media, comprising:
(a) a data structure relating a plurality of Saccharomyces cerevisiae reactants to a plurality of Saccharomyces cerevisiae reactions, wherein each of said Saccharomyces cerevisiae reactions comprises a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating said substrate and said product, wherein at least one of said Saccharomyces cerevisiae reactions is annotated to indicate an associated gene, and wherein a plurality of chemically and electrochemically balanced reactions are assigned to a plurality of different membranous compartments;
(b) a gene database comprising information characterizing said associated gene;
(c) a constraint set for said plurality of Saccharomyces cerevisiae reactions, and (d) commands for determining at least one flux distribution for said plurality of chemically and electrochemically balanced reactions across said plurality of different membranous compartments that minimizes or maximizes an objective function when said constraint set is applied to said data structure, wherein said at least one flux distribution is predictive of a Saccharomyces cerevisiae physiological function.

2. The computer readable medium or media of claim 1, wherein said plurality of reactions comprises at least one reaction from a peripheral metabolic pathway.

3. The computer readable medium or media of claim 2, wherein said peripheral metabolic pathway is selected from the group consisting of amino acid biosynthesis, amino acid degradation, purine biosynthesis, pyrimidine biosynthesis, lipid biosynthesis, fatty acid metabolism, cofactor biosynthesis, cell wall metabolism and transport processes.

4. The computer readable medium or media of claim 1, wherein said Saccharomyces cerevisiae physiological function is selected from the group consisting of growth, energy production, redox equivalent production, biomass production, production of biomass precursors, production of a protein, production of an amino acid, production of a purine, production of a pyrimidine, production of a lipid, production of a fatty acid, production of a cofactor, production of a cell wall component, transport of a metabolite, and consumption of carbon, nitrogen, sulfur, phosphate, hydrogen or oxygen.

5. The computer readable medium or media of claim 1, wherein said Saccharomyces cerevisiae physiological function is selected from the group consisting of degradation of a protein, degradation of an amino acid, degradation of a purine, degradation of a pyrimidine, degradation of a lipid, degradation of a fatty acid, degradation of a cofactor and degradation of a cell wall component.

6. The computer readable medium or media of claim 1, wherein said data structure comprises a set of linear algebraic equations.

7. The computer readable medium or media of claim 1, wherein said data structure comprises a matrix.

8. The computer readable medium or media of claim 1, wherein said commands comprise an optimization problem.

9. The computer readable medium or media of claim 1, wherein said commands comprise a linear program.

10. The computer readable medium or media of claim 1, wherein a first substrate or product in said plurality of Saccharomyces cerevisiae reactions is assigned to a first compartment and a second substrate or product in said plurality of Saccharomyces cerevisiae reactions is assigned to a second compartment.

11. The computer readable medium or media of claim 1, wherein a plurality of said Saccharomyces cerevisiae reactions is annotated to indicate a plurality of associated genes and wherein said gene database comprises information characterizing said plurality of associated genes.

12. A computer readable medium or media, comprising:
(a) a data structure relating a plurality of Saccharomyces cerevisiae reactants to a plurality of Saccharomyces cerevisiae reactions, wherein each of said Saccharomyces cerevisiae reactions comprises a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating said substrate and said product, and wherein a plurality of chemically and electrochemically balanced reactions are assigned to a plurality of different membranous compartments;
(b) a constraint set for said plurality of Saccharomyces cerevisiae reactions, and (c) commands for determining at least one flux distribution for said plurality of chemically and electrochemically balanced reactions across said plurality of different membranous compartments that minimizes or maximizes an objective function when said constraint set is applied to said data structure, wherein said at least one flux distribution is predictive of Saccharomyces cerevisiae growth.

13. A method for predicting a Saccharomyces cerevisiae physiological function, comprising:
(a) providing a data structure relating a plurality of Saccharomyces cerevisiae reactants to a plurality of reactions, wherein each of said Saccharomyces cerevisiae reactions comprises a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating said substrate and said product, wherein at least one of said Saccharomyces cerevisiae reactions is annotated to indicate an associated gene, and wherein a plurality of chemically and electrochemically balanced reactions are assigned to a plurality of different membranous compartments;
(b) providing a constraint set for said plurality of Saccharomyces cerevisiae reactions;
(c) providing an objective function, and (d) determining at least one flux distribution for said plurality of chemically and electrochemically balanced reactions across said plurality of different membranous compartments that minimizes or maximizes said objective function when said constraint set is applied to said data structure, thereby predicting a Saccharomyces cerevisiae physiological function related to said gene.

14. The method of claim 13, wherein said plurality of Saccharomyces cerevisiae reactions comprises at least one reaction from a peripheral metabolic pathway.

15. The method of claim 13, wherein said peripheral metabolic pathway is selected from the group consisting of amino acid biosynthesis, amino acid degradation, purine biosynthesis, pyrimidine biosynthesis, lipid biosynthesis, fatty acid metabolism, cofactor biosynthesis, cell wall metabolism and transport processes.

16. The method of claim 13, wherein said Saccharomyces cerevisiae physiological function is selected from the group consisting of growth, energy production, redox equivalent production, biomass production, production of biomass precursors, production of a protein, production of an amino acid, production of a purine, production of a pyrimidine, production of a lipid, production of a fatty acid, production of a cofactor, production of a cell wall component, transport of a metabolite, and consumption of carbon, nitrogen, sulfur, phosphate, hydrogen or oxygen.

17. The method of claim 13, wherein said Saccharomyces cerevisiae physiological function is selected from the group consisting of glycolysis, the TCA cycle, pentose phosphate pathway, respiration, biosynthesis of an amino acid, degradation of an amino acid, biosynthesis of a purine, biosynthesis of a pyrimidine, biosynthesis of a lipid, metabolism of a fatty acid, biosynthesis of a cofactor, metabolism of a cell wall component, transport of a metabolite and metabolism of a carbon source, nitrogen source, oxygen source, phosphate source, hydrogen source or sulfur source.

18. The method of claim 13, wherein said data structure comprises a set of linear algebraic equations.

19. The method of claim 13, wherein said data structure comprises a matrix.

20. The method of claim 13, wherein said flux distribution is determined by linear programming.

21. The method of claim 13, further comprising:
(e) providing a modified data structure, wherein said modified data structure comprises at least one added reaction, compared to the data structure of part (a), and (f) determining at least one flux distribution that minimizes or maximizes said objective function when said constraint set is applied to said modified data structure, thereby predicting a Saccharomyces cerevisiae physiological function.

22. The method of claim 21, further comprising identifying at least one participant in said at least one added reaction.

23. The method of claim 22, wherein said identifying at least one participant comprises associating a Saccharomyces cerevisiae protein with said at least one reaction.

24. The method of claim 23, further comprising identifying at least one gene that encodes said protein.

25. The method of claim 22, further comprising identifying at least one compound that alters the activity or amount of said at least one participant, thereby identifying a candidate drug or agent that alters a Saccharomyces cerevisiae physiological function.

26. The method of claim 13, further comprising:
(e) providing a modified data structure, wherein said modified data structure lacks at least one reaction compared to the data structure of part (a), and (f) determining at least one flux distribution that minimizes or maximizes said objective function when said constraint set is applied to said modified data structure, thereby predicting a Saccharomyces cerevisiae physiological function.

27. The method of claim 26, further comprising identifying at least one participant in said at least one reaction.

28. The method of claim 27, wherein said identifying at least one participant comprises associating a Saccharomyces cerevisiae protein with said at least one reaction.

29. The method of claim 28, further comprising identifying at least one gene that encodes said protein that performs said at least one reaction.

30. The method of claim 27, further comprising identifying at least one compound that alters the activity or amount of said at least one participant, thereby identifying a candidate drug or agent that alters a Saccharomyces cerevisiae physiological function.

31. The method of claim 13, further comprising:
(e) providing a modified constraint set, wherein said modified constraint set comprises a changed constraint for at least one reaction compared to the constraint for said at least one reaction in the data structure of part (a), and (f) determining at least one flux distribution that minimizes or maximizes said objective function when said modified constraint set is applied to said data structure, thereby predicting a Saccharomyces cerevisiae physiological function.

32. The method of claim 31, further comprising identifying at least one participant in said at least one reaction.

33. The method of claim 32, wherein said identifying at least one participant comprises associating a Saccharomyces cerevisiae protein with said at least one reaction.

34. The method of claim 33, further comprising identifying at least one gene that encodes said protein.

35. The method of claim 32, further comprising identifying at least one compound that alters the activity or amount of said at least one participant, thereby identifying a candidate drug or agent that alters a Saccharomyces cerevisiae physiological function.

36. The method of claim 13, further comprising providing a gene database relating one or more reactions in said data structure with one or more genes or proteins in Saccharomyces cerevisiae.

37. A method for predicting Saccharomyces cerevisiae growth, comprising:
(a) providing a data structure relating a plurality of Saccharomyces cerevisiae reactants to a plurality of Saccharomyces cerevisiae reactions, wherein each of said Saccharomyces cerevisiae reactions comprises a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating said substrate and said product, and wherein a plurality of chemically and electrochemically balanced reactions are assigned to a plurality of different membranous compartments;
(b) providing a constraint set for said plurality of Saccharomyces cerevisiae reactions;
(c) providing an objective function, and (d) determining at least one flux distribution for said plurality of chemically and electrochemically balanced reactions across said plurality of different membranous compartments that minimizes or maximizes said objective function when said constraint set is applied to said data structure, thereby predicting Saccharomyces cerevisiae growth.

38. A method for making a data structure relating a plurality of Saccharomyces cerevisiae reactants to a plurality of Saccharomyces cerevisiae reactions in a computer readable medium or media, comprising:
(a) identifying a plurality of Saccharomyces cerevisiae reactions and a plurality of Saccharomyces cerevisiae reactants that are substrates and products of said Saccharomyces cerevisiae reactions;
(b) relating said plurality of Saccharomyces cerevisiae reactants to said plurality of Saccharomyces cerevisiae reactions in the data structure, wherein each of said Saccharomyces cerevisiae reactions comprises a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating said substrate and said product, and wherein a plurality of chemically and electrochemically balanced reactions are assigned to a plurality of different membranous compartments;
(c) determining a constraint set for said plurality of Saccharomyces cerevisiae reactions;

(d) providing an objective function;
(e) determining at least one flux distribution for said plurality of chemically and electrochemically balanced reactions across said plurality of different membranous compartments that minimizes or maximizes said objective function when said constraint set is applied to said data structure, and (f) if said at least one flux distribution is not predictive of a Saccharomyces cerevisiae physiological function, then adding a reaction to or deleting a reaction from said data structure and repeating step (e), if said at least one flux distribution is predictive of a Saccharomyces cerevisiae physiological function, then storing said data structure in a computer readable medium or media.

39. The method of claim 38, wherein a reaction in said data structure is identified from an annotated genome.

40. The method of claim 39, further comprising storing said reaction that is identified from an annotated genome in a gene database.

41. The method of claim 38, further comprising annotating a reaction in said data structure.

42. The method of claim 41, wherein said annotation is selected from the group consisting of assignment of a gene, assignment of a protein, assignment of a subsystem, assignment of a confidence rating, reference to genome annotation information and reference to a publication.

43. The method of claim 38, wherein step (b) further comprises identifying an unbalanced reaction in said data structure and adding a reaction to said data structure, thereby changing said unbalanced reaction to a balanced reaction.

44. The method of claim 38, wherein said adding a reaction comprises adding a reaction selected from the group consisting of an intra-system reaction, an exchange reaction, a reaction from a peripheral metabolic pathway, reaction from a central metabolic pathway, a gene associated reaction and a non-gene associated reaction.

45. The method of claim 44, wherein said peripheral metabolic pathway is selected from the group consisting of amino acid biosynthesis, amino acid degradation, purine biosynthesis, pyrimidine biosynthesis, lipid biosynthesis, fatty acid metabolism, cofactor biosynthesis, cell wall metabolism and transport processes.

46. The method of claim 38, wherein said Saccharomyces cerevisiae physiological function is selected from the group consisting of growth, energy production, redox equivalent production, biomass production, production of biomass precursors, production of a protein, production of an amino acid, production of a purine, production of a pyrimidine, production of a lipid, production of a fatty acid, production of a cofactor, production of a cell wall component, transport of a metabolite, development, intercellular signaling, and consumption of carbon, nitrogen, sulfur, phosphate, hydrogen or oxygen.

47. The method of claim 38, wherein said Saccharomyces cerevisiae physiological function is selected from the group consisting of degradation of a protein, degradation of an amino acid, degradation of a purine, degradation of a pyrimidine, degradation of a lipid, degradation of a fatty acid, degradation of a cofactor and degradation of a cell wall component.

48. The method of claim 38, wherein said data structure comprises a set of linear algebraic equations.

49. The method of claim 38, wherein said data structure comprises a matrix.

50. The method of claim 38, wherein said flux distribution is determined by linear programming.

51. A data structure relating a plurality of Saccharomyces cerevisiae reactants to a plurality of Saccharomyces cerevisiae reactions, wherein said data structure is produced by a process comprising:

(a) identifying a plurality of Saccharomyces cerevisiae reactions and a plurality of Saccharomyces cerevisiae reactants that are substrates and products of said Saccharomyces cerevisiae reactions;
(b) relating said plurality of Saccharomyces cerevisiae reactants to said plurality of Saccharomyces cerevisiae reactions in the data structure, wherein each of said Saccharomyces cerevisiae reactions comprises a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating said substrate and said product, and wherein a plurality of chemically and electrochemically balanced reactions are assigned to a plurality of different membranous compartments;
(c) determining a constraint set for said plurality of Saccharomyces cerevisiae reactions;
(d) providing an objective function;
(e) determining at least one flux distribution for said plurality of chemically and electrochemically balanced reactions across said plurality of different membranous compartments that minimizes or maximizes said objective function when said constraint set is applied to said data structure, and (f) if said at least one flux distribution is not predictive of Saccharomyces cerevisiae physiology, then adding a reaction to or deleting a reaction from said data structure and repeating step (e), if said at least one flux distribution is predictive of Saccharomyces cerevisiae physiology, then storing said data structure in a computer readable medium or media.