CA2462099A1 - 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

COMPOSITONS AND METHODS FOR MODELING
SACCHAROMYCES CEREVISIAE METABOLISM
[0001] This invention was made with United States Government support under grant NIH RO1HL59234 awarded by the National Institutes of Health. The U.S.
Government has certain rights in this invention.
BACKGROUND OF THE INVENTION

[0002] 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 cerevisiae (S. cerevisiae) reaction networks.

[0003] Saccharomyces cenevisiae 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 2~5: 901-906 (1999)). Up to 30% of positionally cloned genes implicated in human disease have yeast homologs.

[0004] The first eukaryotic genome to be sequenced was that of S. ceYevisiae, and about 6400 open reading frames (or genes) have been identified in the genome. S.
cey~evisiae 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.

[0005] 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. cep°evisiae is currently being exploited as a cell factory for many different bioproducts including insulin.

[0006] 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.

[0007] 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.
cenevisiae reaction networks, such as its metabolic network, which can be used to simulate many different aspects of the cellular behavior of S. ce~evisiae under different conditions.
The present invention satisfies this need, and provides related advantages as well.
SUMMARY OF THE INVENTION

[0008] 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. cef~evisiae 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.
ces°evisiae 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. cef°evisiae 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. cef-evisiae physiological function related to the gene.

[0010] Also provided by the invention is a method for malting a data structure relating a plurality of S cerevisiae reactants to a plurality of S. ceYevisiae reactions in a computer readable medium or media, including: (a) identifying a plurality of S.
ce~evisiae 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. ce~evisiae 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. ce~evisiae 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; Yl, uptake rate value) [0014] Figure 4 shows an exemplary metabolic reaction network in S.
ceYevisiae.

[0015] Figure 5 shows a method for reconstruction of the metabolic network of S.
ce~evisiae. Based on the available information from the genome annotation, biochemical pathway databases, biochemistry textbooks and recent publications, a genome-scale metabolic network for S. ce~evisiae 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. ce~evisiae 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 (mmolelg-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. ce~-evisiae.
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 silico 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 ih silico. Figure l0A shows biomass yield (Yx), and secretion rates of ethanol (Eth), and glycerol (Gly). Figure lOB shows COz secretion rate (q~oz) and respiratory quotient (RQ; i.e.
qcoz~qoz) of the aerobic glucose-limited continuous culture of S. eerevisiae.
(exp, experimental).
DETAILED DESCRIPTION OF THE INVENTION

[0021] The present invention provides an irz 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. ces~evisiae 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.
cer°evisiae 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 izz 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.

[0026] 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 izz 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.
ceYevisiae.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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. ce~evisiae reactions or reactants in the range from 2 to the number of naturally occurnng 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.

[0031] 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.

[0032] 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.
ce~evisiae. The term can include, the rate at which a chemical is consumed or produced by S.
cerevisiae, the rate of growth of S. ce~evisiae 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. ceYevisiae 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. ceYevisiae 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.
cef~evisiae including, for example, strain CEN.PKl 13.7D 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.

[0041] 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.

[0042] 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. ce~evisiae. 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.

[0043] Reactions included in a reaction network data structure can include infra-system or exchange reactions. Infra-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 infra-system reactions can be classified as either being transfonmations 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 cenevisiae 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.

[0047] 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 0.3 02 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 0.028 SLF 0.02 TYR 0.102 ATP 23.9166 VAL 0.265 ADP 23.9166 AMP 0.051 PI 23.9456 GMP 0.051 Biornass1 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 RZ 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 EXt, and the demand exchange reaction, V~.o",tn, w~ch 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 Rb which is a transport reaction that translocates reactant E out of the compartment.

[0051] 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 m x n matrix where m 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 m, and each S"", element corresponds to the stoichiometric coefficient of the reactant m in the reaction denoted n. The stoichiometric matrix includes intra-system reactions such as Rz 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 -EXt and -AXt 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 (Eexternal) 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 (E~xtemal) is correlated by stoichiometric coefficients of 1 and 0, respectively. Demand reactions such as V~o,~,rh 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. cef~evisiae 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, CITl, DAL7, ENO1, FBA1, FBP1, FUMl, GNDl, GPM1, HXKl, ICL1, IDH1, IDH2, IDPI, IDP2, IDP3, KGD1, KGD2, LPD1, LSC1, LSC2, MDH1, MDH2, MDH3, MLS1, PDC1, PFKl, PFK2, PGI1, PGKl, PGM1, PGM2, PYC1, PYC2, PYK2, RKI1, RPE1, SOL1, TAL1, 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, MNADD1, MNADE, MNADF_l, MNADPHPS, MNADGI, MNADG2, MNADH, MNPT1.
Table 2 Locus E.C.GeneGene Description Reaction Rxn # # Name Carbohydrate Metabolism Glycolysis/Gluconeogenesis YCL040W2.7.1.2GLKIGlucokinase GLC+ATP->G6P+ADP glkl YCL040W2.7.1.2GLKIGlucokinase MAN+ATPaMAN6P+ADP glkl YCL040W2.7.1.2GLKIGlucokinase bDGLC+ATP->bDG6P+ADPglkl YFR053C2.7.1.1HXKIHexokinase I (PI) bDGLC + ATP a G6P hxkl_1 (also called Hexokinase+ ADP
A) YFR053C2.7.1.1HXKIHexokinase I (PI) GLC + pTP a G6P + hxkl (also called HexokinaseADP 2 A) YFR063C2.7.1.1HXKIHexokinase I (PI) MAN + ATP -> MAN6P hxkl (also called Hexokinase+ADP 3 A) YFR053C2.7.1.1HXKIHexokinase I (PI) ATP + FRU a pDp + hxkl (also called HexokinaseF6P 4 A) YGL253W2.7.1.1HXK2Hexokinase II (PII) bDGLC + ATP -> G6P hxk2_1 (also called Hexokinase+ ADP
B) YGL253W2.7.1.1HXK2Hexokinase II (PII) GLC+ATP a G6P+ADP hxk2_2 (also called Hexokinase B) YGL2532.7.1.1HXK2Hexokinase II (PII) MAN+ ATP -> MAN6P hxk2_3 W (also called Hexokinase+ ADP
B) YGL2532.7.1.1HXK2Hexokinase II (PII) ATP + FRU a pDp + hxk2_4 W (also called HexokinaseF6P
B) YBR196C5.3.1.9PGIIGlucose-6-phosphate G6P <-> F6P pgil isomerase I

YBR196C5.3.1.9PGIIGlucose-6-phosphate G6P <-> bDG6P pgil isomerase 2 YBR196C5.3.1.9PGIIGlucose-6-phosphate bDG6P <-> F6P pgil isomerase 3 YMR205C2.7.1.11PFK2phosphofructokinase F6P + ATP -> FDP pt beta subunit + ADP

YGR240C2.7.1.11PFKIphosphofroctokinasealphasubunitF6P+pTP->FDP+ADP pfkl YGR240C2.7.1.11PFKIphosphofmctokinasealphasubunitATP+TAG6P->ADP+TAG16Ppfkl YGR240C2.7.1.11PFK phosphofmctokinase ATP + S7P -> ADP pfkl I alpha subunit + S 17P 3 YKL060C4.1.2.13FBAIfructose-bisphosphatealdolaseFDP<->T3P2+T3P1 fbal_1 YDROSOC5.3.1.1TPIItriosephosphate isomeraseT3P2 <aT3P1 tpil YJL052W1.2.1.12TDHIGlyceraldehyde-3-phosphatedehydrogenaselT3P1+PI+NAD<->
NADH+13PDGtdhl YJR009C1.2.1.12TDH2glyceraldehyde3-phosphatedehydrogenaseT3Pl+PI+NAD<a NADH+13PDGtdh2 YGR192C1.2.1.12TDH3Glyceraldehyde-3-phosphateT3P 1 + PI+NAD <a tdh3 dehydrogenase 3 NADH + 13PDG

YCR012W2.7.2.3PGKIphosphoglyceratekinase13PDG+ADP<a3PG+ATP pgkl YKL152C5.4.2.1GPMIPhosphoglycerate mutase13PDG <-> 23PDG gpml_I

YKLl52C5.4.2.1GPMIPhosphoglycerate mutase3PG <-> 2PG gpml -YDL0215.4.2.1GPM2Similar to GPMI (phosphoglycerate3PG <a 2PG SPA' W mutase) YOL056W5.4.2.1GPM3phosphoglycerate mutase3PG <-> 2PG gpm3 YGR254W4.2.1.11ENO1enolase I 2PG <-> PEP enol YHR174W4.2.1.11EN02enolase 2PG <-> PEP eno2 YMR3234.2.1.11ERRIProtein with similarity2PG <-> PEP eno3 W to enolases YPL281C4.2.1.11ERR2enolase related protein2PG <-> PEP eno4 YOR3934.2.1.1ERRIenolase related protein2PG <-> PEP eno5 W I

YAL038W2.7.1.40CDC19Pyrovatekinase PEP+ADP->PYR+ATP cdcl9 YOR347C2.7.1.40PYK2Pyrovate kinase, glucose-repressedPEP + ADP -> PYR+ATPpyk2 isoform YER178w1.2.4.1PDAIpyrovatedehydrogenase(lipoamide)alphachainPYRm+COAm+NADm->NADHm+C02m+pdal precursor, EI component,ACCOAm alpha unit YBR221c1.2.4.1PDB pymvate dehydrogenase I (lipoamide) beta chain precursor, El component, beta unit YNL071w2.3.1.12LATIdihydrolipoamide S-acetyltransferase, E2 component Citrate cycle (TCA
cycle) YNROOIC4.1.3.7CITICitrate synthase, ACCOAm + OAm -> COAmcitl Nuclear encoded mitochondria(+ CITm protein.

YCROOSC4.1.3.7CIT2Citrate synthase, ACCOA+ OA-> COA+CIT cit2 non-mitochondria) citrate synthase YPR0014.1.3.7cit3Citrate synthase, ACCOAm + OAm -> COAmcil3 W Mitochondria( isoform+ CITm of citrate synthase YLR304C4.2.1.3acolAconitase, mitochondria)CITm <-> ICITm acol YJL200C4.2.1.3YJL200C CITm <-> ICITm aco2 aconitate hydratase homolog YNL037C1.1.1.41IDHIIsocitratedehydrogenase(NAD+)mito,subuintlICITm+NADm->C02m+NADHm+AKGmidhi YOR136W1.1.1.41IDH2Isocitrate dehydrogenase (NAD+) mito, subunit2 YDL066W1.1.1.42IDP1Isocitratedehydrogenase(NADP+)ICITm+NADPmaNADPHm+OSUCmidpl-YLR174WLl.1.42IDP2Isocitratedehydrogenase(NADP+)ICIT+NADP->NADPH+OSUCidp2-1 YNL009W1.1.1.42IDP3Isocitratedehydrogenase(NADP+)ICIT+NADP->NADPH+OSUCidp3 YDL066W1.1.1.42IDP Isocitrate dehydrogenaseOSUCm a C02m + AKGm idpl 1 (NADP+) 2 YLR174W1.1.1.42IDP2Isocitratedehydrogenase(NADP+)OSUC->C02+AKG idp2 YNL009Wl.l.1.42IDP3Isocitratedehydrogenase(NADP+)OSUC->C02+AKG idp3 YIL125W1.2.4.2kgdlalpha-ketoglutaratedehydrogenasecomplex,ElAKGm+NADm+COAmaC02m+NADHm+kgdla component SUCCOAm YDR148C2.3.1.61KGD2Dihydrolipoamide S-succinyltransferase, E2 component YGR244C6.2.1.4/6. Succinate--CoAligase(GDP-forming)ATPm+SUCCm+COAm<->ADPm+PIm+Isc2 2.1.5 SUCCOAm YOR142W6.2.1.4/6.LSCIsuccinate-CoAligasealphnsubunitATPm+ITCm+COAm<->ADPm+PIm+lscl 2.1.5 ITCCOAm Electron Transpor!
System, Complex Il YKL141w1.3.5.1SDH3succinate dehydrogenaseSUCCm + FADm <-> sdh3 cytochrome b FUMm + FADH2m YKL148c1.3.5.1SDHIsuccinate dehydrogenase cytochrome b YLL041c1.3.5.1SDH2Succinate dehydrogenase (ubiquinone) iron-sulfur protein subunit YDR178w1.3.5.1SDH4succinate dehydrogenase membrane anchor subunit YLR164w1.3.5.1YLR164strong similarity to SDH4P

w YMR118c1.3.5.1YMR118strong similarity to succinate dehydrogenase c YJL045w1.3.5.1YJL045wstrong similarity to succinate dehydrogenase flavoprotein YEL047c1.3.99.1YEL047csoluble fumarate reductase,FADH2m + FUM -> SUCCfrdsl cytoplasmic + FADm YJRO511.3.99.1osmlMitochondria) solubleFADH2m + FUMm -> osml W fumarate reductase SUCCm + FADm involved in osmotic regulation YPL262W4.2.1.2FUMIFumaratase FUMm<->MALm fuml_I

YPL262W4.2.1.2FUMIFumaratase FUM<->MAL fuml YKL085W1.1.1.37MDHImitochondrialmalatedehydrogenaseMALm+NADm<->NADHm+OAmmdhl YDL078C1.1.1.37MDH3MALATEDEHYDROGENASE,PEROXISOMALMAL+NAD<aNADH+OA mdh3 YOL126C1.1.1.37MDH2malatedehydrogenase,cytoplasmicMAL+NAD<aNADH+OA mdh2 Anaplerolic Reactions YER065C4.1.3.1ICLIisocitratelyase ICIT->GLX+SUCC icll YPR006C4.1.3.1ICL2Isocitrate lynse, ICIT -> GLX + SUCC icl2 may be nonfunctional YBt031C4.1.3.2dal?Malatesynthase ACCOA+GLXaCOA+MAL dal?

YNL117W4.1.3.2MLS1Malatesynthase ACCOA+GLXaCOA+MAL mlsl YKR097W4.1.1.49pcklphosphoenolpyruvatecarboxylkinaseOA+ATPaPEP+C02+ADP pckl YLR377C3.1.3.11FBP1fmctose-1,6-bisphosphataseFDP->F6P+PI fbpl YGL062W6.4.1.1PYCIpyruvatecarboxylase PYR+ATP+C02->ADP+OA+PIpyel YBR218C6.4.1.1PYC2pytuvate carboxylase PYR+ATP + C02 -> pyc2 ADP + OA + PI

YKL029C1.1.1.38MAEImitochondrialmalicenzymeMALm+NADPm->C02m+NADPHm+PYRmmael Pentose phosphate cycle YNL241C1.1.1.49zmflGlucose-6-phosphate-1-dehydrogenaseG6P+NADP<->D6PGL+NADPHzwfl YNR034W3.1.1.31SOLIPossible6-phosphogluconolactonaseD6PGL->D6PGC soil YCR073W-3.1.1.31SOL2Possible 6-phosphogluconolactonaseD6PGL a D6PGC sol2 A

YHRI63W3.1.1.31SOL3Possible6-phosphogluconolactonaseD6PGL->D6PGC sol3 YGR248W3.1.1.31SOL4Possible 6-phosphogluconolactonaseD6PGL a D6PGC sol4 YGR256W1.1.1.44GND26-phophogluconatedehydrogenaseD6PGC+NADP->NADPH+C02+RLSPgnd2 YHR183W1.1.1.44GNDI6-phophogluconatedehydrogenaseD6PGC+NADP->NADPH+C02+RLSPgndl YJL121C5.1.3.1RPEIribulose-5-P 3-epimemseItLSP <-> XSP rpel Y0R095C5.3.1.6RKIIribose-5-P isomemse RLSP <-> RSP rkil YBR117C2.2.1.1TKL2transketolase RSP+XSP<->T3PI+S7P tkl2_1 YBR117C2.2.1.1TKL2transketolase XSP+E4P<->F6P+T3P1 tkl2_2 YPR074C2.2.1.1TKLItransketolase RSP + XSP <-> T3P1 tkll + S7P I

YPR074C2.2.1.1TKLItransketolase XSP+E4P<->F6P+T3P1 tkll YLR354C2.2.1.2TALItransaldolase T3P1 + S7P <-> E4P tall_I
+F6P

YGR043C2.2.1.2YGR043transaldolase T3P1 + S7P <-> FAP tall +F6P 2 -C

YCR036W2.7.1.15RBKIRibokinase RIB+ATPaRSP+ADP rbkl_1 YCR036W2.7.1.15RBKIRibokinase DRIB+ATP->DRSP+ADP rbkl_2 YKL127W5.4.2.2pgmlphosphoglucomutase RIP <-> RSP pgml-1 YKL127W5.4.2.2pgmlphosphoglucomutasel G1P<->G6P pgml YMR105C5.4.2.2pgm2phosphoglucomutase R1P<->RSP pgm2 YMRIOSC5.4.2.2pgm2Phosphoglucomutase G1P<aG6P pgm2_2 Mannose YER003C5.3.1.8PMI40mannose-6-phosphate MAN6P <-> F6P pmi40 isomerase YFL045C5.4.2.8SEC53phosphomannomutase MAN6P <-> MANIP sec53 YDLOSSC2.7.7.13PSAImannose-1-phosphateguanyltransferase,GDP-mannose psal GTP+MANIPaPPI+GDPMAN

pyrophosphorylase Fructose YILI07C2.7.1.105 6-Phosphofructose-2-kinaseATP + F6P -> ADP ptk26 PFK26 + F26P

YOL136C2.7.1.105 6-phosphofructo-2-kinaseATP+F6P->ADP+F26P pflt27 ptk27 YJL155C3.1.3.46FBP26Fructose-2,6-biphosphataseF26P->F6P+PI fbp26 - 2.7.1.56- 1-Phosphofructokinase(Fructose)-phosphatekinase)FIP+ATP->FDP+ADP fre3 SorboseS.c, does not metabolize sorbitol, erythritol, mannitol, xylitol, ribitol, arabinitol, galactinol YJRI59Wl.l.l.l4SORIsorbitoldehydrogenase(L-iditol2-dehydrogenase)SOT+NAD->FRU+NADH sort Galactose metabolism YBR020W2.7.1.6gallgalactokinase GLAC+ATPaGALIP+ADP gall YBR018C2.7.7.10gallgalactose-1-phosphateuridyltransferaseUTP+GAL1P<apPI+UDPGALgall YBR019C5.1.3.2ga110UDP-glucose 4-epimeraseUDPGAL <-> UDPG ga110 YHL012W2.7.7.9YHL012 GIP+UTP<->UDPG+ppI ugpl UTP-Glucose)-PhosphateUridylyltransfemse -W

YKL035W2.7.7.9UGPIUridinephosphoglucosepyrophosphorylaseG1P+UTP<->UDPG+PPI
ugpt-1 YBR184W3.2.1.22YBR184Alpha-galactosidase(melibiase)MELI->GLC+GLAC mall -W

YBRI84W3.2.1.22YBR184Alpha-galactosidase(melibiase)DFUC->GLC+GLAC mall -W

YBR184W3.2.1.22YBR184Alphn-galactosidase(melibiase)RAF->GLAC+SUC mall -W

YBRI84W3.2.1.22YBR184Alpha-galactosidase(melibiase)GLACL<->MYOI+GLAC mall -W

YBR184W3.2.1.22YBR184Alpha-galactosidase EPM <-> MAN +GLAC mall (melibiase) 5 -W

YBR184W3.2.1.22YBR184Alpha-galactosidase(melibiase)GGL<->GL+GLAC mall -W

YBR184W3.2.1.22YBR184Alpha-galactosidase MELT <-> SOT+GLAC mall (melibiase) 7 -W

YBR299W3.2.1.20MAL32Maltase MLT->2GLC ma132a YGR287C3.2.1.20YGR287putative alpha glucosidaseMLT a 2 GLC ma132b C

YGR292W3.2.1.20MAL12Maltase MLT->2GLC ma112a YIL172C3.2.1.20YILI72Cputative alpha glucosidaseMLT-> 2 GLC mall2S

YJL216C3.2.1.20YJL216Cprobable alpha-glucosidaseMLT -> 2 GLC mall2c (MALTase) YJL221C3.2.1.20FSP2homology to maltase(alpha-D-glucosidase)MLT a 2 GLC
fsp2a YJL221C3.2.1.20FSP2homology to mnltase(alpha-D-glucosidase)6DGLC a GLAC + GLC
fsp2b YBR018C2.7.7.12GAL7UDPglucose-hexose-1-phosphateuridylyltransferaseUDPG+GAL1P<aGIP+UDPGALunkrxl0 Trehalose YBR126C2.4.1.15TPSItrehalose-6-Psynthetase,56kDsynthasesubunitofUDPG+G6P->UDP+TRE6Ptpsl trehalose-6-phosphate synthaseUphosphatase complex YMLIOOW2.4.1.15tslltrehalose-6-Psynthetase,123kDregulatorysubunitofUDPG+G6P->UDP+TRE6Ptsll trehalose-6-phosphate synthaseVphosphatase complex\;

homologous to TPS3 gene product YMR261C2.4.1.15TPS3trehalose-6-Psynthetase,IlSkDregulatorysubunitofUDPG+G6PaUDP+TRE6P tps3 trehalose-6-phosphate synthaseVphosphatase complex YDR0743.1.3.12TPS2Trehalose-6-phosphateTRE6P a THE +PI tps2 W phosphatase YPR026W3.2.1.28ATHIAcidtrehalase TRE->2GLC athl YBROO1C3.2.1.28NTH2Neutral trehalase, TRE-> 2 GLC nth2 highly homologous to Nthlp YDROO1C3.2.1.28NTHIneutral trehalase THE a 2 GLC nthl Glycogen (sucorose Metabolism and sugar metabolism) YELO11W2.4.1.18gle3Branching enzyme,l,4-glucan-6-(1,4-glucano)-GLYCOGEN+PI->GIP glc3 transferase YPR160W2.4.1.1GPH1GlycogenphosphorylaseGLYCOGEN+PI->G1P gphl YFRO 2.4.1.11GSYIGlycogen synthase UDPG -> UDP + GLYCOGENgsyl 15C (UDP-gluocse-starch glucosyltransferase) YLR258W2.4.1.11GSY2Glycogensynthase(UDP-gluocse-starchUDPG->UDP+GLYCOGEN gsy2 glucosyltransferase) Pyruvate Metabolism YAL054C6.2.1.1acslacetyl-coenzymeAsynthetaseATPm+ACm+COAm->AMPm+PPIm+acsl ACCOAm YLR153C6.2.1.1ACS2acetyl-coenzymeAsynthetaseATP+AC+COA->AMP+PPI+ACCOAacs2 YDL168W1.2.1.1SFAIFormaldehydedehydrogenase/long-chainFALD+RGT+NAD<->FGT+NADHsfal-1 alcohol dehydrogenase YJL068C3.1.2.12- S-Formylglutathione FGT <-> RGT+ FOR unkrxl hydrolase l YGR087C4.1.1.1PDC6pyruvatedecarboxylasePYR->C02+ACAL pdc6 YLR134W4.1.1.1PDCSpymvatedecarboxylase PYRaC02+ACAL pdc5 YLR044C4.1.1.1pdclpyruvate decarboxylasePYR a C02+ACAL pdcl YBLO15W3.1.2.1ACH1acetylCoAhydrolnse COA+ACaACCOA achl_1 YBLO15W3.1.2.1ACHIacetylCoAhydrolase COAm+ACm->ACCOAm achl_2 YDL1314.1.3.21LYS21probable homocitrate ACCOA+ AKG -> HCIT 1ys21 W synthase, mitochondrial+ COA
isozyme precursor YDL182W4.1.3.21LYS20homocitrate synthase,ACCOA + AKG -> HCIT1ys20 cytosolic isozyme + COA

YDL182W4.1.3.21LYS20Homocitratesynthase ACCOAm+AKGm->HCITm+COAmIys20a YGL256W1.1.1.1adh4alcoholdehydrogenaseisoenzymelVETH+NAD<apCAL+NADH adh4 YMR083W1.1.1.1adh3alcoholdehydrogenaseisoenzymeIIIETHm+NADm<->ACALm+NADHmadh3 YMR303C1.1.1.1adh2alcoholdehydrogenaseIlETH+NAD<->ACAL+NADHadh2 YBR145W1.1.1.1ADHSalcoholdehydrogenaseisoenzymeVETH+NAD<->ACAL+NADHadh5 YOL086C1.1.11adhlAlcoholdehydrogenaselETH+NAD<->ACAL+NADHadhl YDL168W1.1.1.1SFAIAlcoholdehydrogenaseIETH+NAD<->ACAL+NADHsfal -Glyoxylate and dicarboxylate metabolism Glyoxal Pathway YML004C4.4.1.5GLOILactoylglutathione RGT+MTHGXL <-> LGT glol lyase, glyoxalase I

YDR272W3.1.2.6GL02HydroxyncylglutathionehydrolaseLGTaRGT+LAC glo2 YOR040W3.1.2.6GL04glyoxalase II (hydroxyacylglutathioneLGTm a RGTm + LACm glo4 hydrolase) Energyolism Metab Oxtdattvehosphorylatlon P

YBROI1C3.6.1.1ipplInorganicpyrophosphatasePPI->2PI ippl YMR267W3.6.1.1ppa2mitochondrial inorganicPPIm -> 2 PIm ppa2 pyrophosphatase 1.2.2.1FDNGFortnate dehydrogenaseFOR+ Qm -> QH2m fdng + C02 +2 HEXT

YML120C1.6.5.3NDIINADHdehydrogenase(ubiquinone)NADHm+QmaQH2m+NADm ndil YDL085W1.6.5.3NDH2Mitochondrial NADH ndh2 dehydrogenase that catalyzes the NADH+
Qm -> QH2m +NAD

oxidation of cytosolic NADH

YMR145C1.6.5.3NDH1Mitochondrial NADH
dehydrogenase that catalyzes the NADH
+ Qm -> QH2m +NAD
ndhl oxidation of cytosolicNADH

YHR042W1.6.2.4NCPINADPH--ferrihemoproteinreductaseNADPH+2FERIm->NADP+2FEROm ncpl YKLI4Iw1.3.5.1SDH3succinate dehydrogenaseFADH2m + Qm <-> FADm cytochrome b + QH2m fad YKLl48c1.3.5.1SDHIsuccinate dehydrogenase cytochrome b YLL041cL3.5.1SDH2succinate dehydrogenase cytochrome b YDR178w1.3.5.1SDH4succinate dehydrogenase cytochrome b Electron Transport Syslenr, Complex III

YEL024W1.10.2.2RIP ubiquinol-cytochrome 02m + 4 FEROm + 6 Hm 1 c reductase iron-sulfur-> 4 FERIm cyto subunit QOI05L10.2.2CYTBubiquinol-cytochrome c reductase cytochrome b subunit YOR0651.10.2.2CYT1ubiquinol-cytochrome W c reductase cytochrome cl subunit YBL045C1.10.2.2CORIubiquinol-cytochrome c reductase core subunit 1 YPRl911.10.2.2QCRIubiquinol-cytochrome W c reductase core subunit 2 YPRi911.10.2.2QCR2ubiquinol-cytochrome W c reductase YFR033C1.10.2.2QCR6ubiquinol-cytochrome c reductase subunit YDR529C1.10.2.2QCR7ubiquinol-cytochrome c reductase subunit YJL166W1.10.2.2QCR8ubiquinol-cytochrome a reductase subunit YGR183C1.10.2.2QCR9ubiquinol-cytochrame c reductase subunit YHR0011.10.2.2QCR10ubiquinol-cytochrome W- c reductase subunit A

Electron Transport Syslenr, Complex IV

Q00451.9.3.1COXIcytochrome c oxidase QH2m+ 2 FERIm + L5 Hm subunit I -> Qm+ 2 FEROm cytr Q02501.9.3.1COX2cytochrome c oxidase subunit I

Q02751.9.3.1COX3cytochrome c oxidase subunit I

YDL067C1.9.3.1COX9cytochrome c oxidase subunit I

YGL187C1.9.3.1COX4cytochrome c oxidase subunit I

YGL191W1.9.3.1COX13cytochrome c oxidase subunit I

YHRO511.9.3.1COX6cytochrome c oxidase W subunit I

YILI 1.9.3.1COXSBcytochrome c oxidase 1 subunit I
I
W

YLR038C1.9.3.1COX12cytochrome c oxidase subunit I

YLR395C1.9.3.1COX8cytochrome c oxidase subunit I

YMR256C1.9.3.1COX7cytochrome c oxidase subunit I

YNL052W1.9.3.1COXSAcytochrome c oxidase subunit I

ATP
Syn(hase YBL099W3.6.1.34ATPIF1F0-ATPasecomplex,FlalphasubunitADPm+Phn->ATPm+3Hm atpl YPL271W3.6.1.34ATP15F1F0-ATPase complex, FI epsilon subunit YDL004W3.6.1.34ATP F-type H+-transporting 16 ATPase delta chain Q00853.6.134ATP6F1F0-ATPase complex, FO A subunit YBR039W3.6.1.34ATP3F1F0-ATPase complex, Fl gamma subunit YBR127C3.6.1.34VMA2H+-ATPase V 1 domain 60 KD subunit, vacuolar YPL078C3.6.1.34ATP4FIFO-ATPase complex, Fl delta subunit YDR298C3.6.1.34ATPSF1F0-ATPase complex, OSCP subunit YDR377W3.6.1.34ATP17ATP synthase complex, subunit f YJR12IW3.6.1.34ATP2FIFO-ATPasecomplex, Fl betasubunit YKL016C3.6.1.34ATP7F1F0-ATPase complex, FO D subunit YLR295C3.6.1.34ATP14ATPsynthasesubunith Q00803.6.1.34ATP8F-type H+-transporting ATPase subunit 8 Q01303.6.1.34ATP9F-type H+-transporting ATPase subunit c YOL077W-3.6.1.34ATP19ATP synthase k chain, mitochondrial A

YPR020W3.6.1.34ATP20subunit G of the dimeric form of mitochondrial ATP synthase YLR447C3.6.1.34VMA6V-type H+-transporting ATPase subunit AC39 YGR020C3.6.1.34VMA7V-type H+-transporting ATPase subunit F

YKL080W3.6.1.34VMASV-type H+-transporting ATPase subunit C

YDL185W3.6.1.34TFPIV-type H+-transportingATPasesubunitA

YBR127C3.6.1.34VMA2V-type H+-transporting ATPase subunit B

YOR332W3.6.1.34VMA4V-type H+-transporting ATPase subunit E

YEL027W3.6.1.34CUPSV-type H+-transporting ATPase proteolipid subunit YHR026W3.6.1.34PPAtV-type H+-transporting ATPase proteolipid subunit YPL234C3.6,1.34TFP3V-type H+-transporting ATPase proteolipid subunit YMR054W3.6.1.34STV V-type H+-transporting I ATPase subunit I

YOR270C3.6.1.34VPHIV-type H+-transporting ATPase subunit I

YELOSIW3.6.1.34VMA8V-typeH+-transportingATPasesubunitD

YHR039GA VMAIOvacuolar ATP synthase 3.6.1.34 subunit G

YPR036W3.6.1.34VMA13V-type H+-transporting ATPase 54 kD subunit Electron lex IV
Transpor!
System.
Comp Q00451.9.3.1COXIcytochrome-c oxidase 4 FEROm + 02m+ 6 Hm subunit I a 4 FERlm coxl Q02751.9.3.1COX3Cytochrome-c oxidase subunit III, mitochondrially-coded Q02501.9.3.1COX2cytochrome-c oxidase subunit II

YDL067C1.9.3.1COX9Cytochrome-c oxidase YGL187C1.9.3.1COX4cytochrome-c oxidase chain IV

YGL191W1.9.3.1COX13cytochrome-coxidasechainVla YHRO511.9.3.1COX6cytochrome-c oxidase W subunit VI

YLR395C COXS eytochrome-c oxidase 1.9.3.1 chain VIII

YMR256C COX7 cytochrome-c oxidase, 1.9.3.1 subunit VII

YNL052W COXSA cytochrome-c oxidase 1.9.3.1 chain V.A precursor YML054C cyb2 Lactic acid dehydrogenase2 FERIm + LLACm -> cyb2 1.1.2.3 PYRm + 2 FEROm YDL174C DLDl mitochondria! enzyme2 FERIm + LACm a dldl 1.1.2.4 D-Inctate ferricytochromepYRm + 2 FEROm c oxidoreductase Methane metabolism YPL275W YPL275 putativeformatedehydrogenase/putativepseudogeneFOR+NAD->C02+NADH tfola 1.2.1.2 W

YPL276W YPL276 putativeformatedehydrogenase/putativepseudogeneFOR+NAD->C02+NADH tfolb 1,2.1.2 W

YOR388C FDHI Protein with similarityFOR+NAD->C02+NADH fdhl 1.2.1.2 toformatedehydrogenases Nttrogen metabolism YBR208C DURI urea amidolyase containingATP +UREA + C02 <-> durl 6.3.4.6 urea carboxylase / ADP + PI+UREAC

allophanate hydrolase YBR208C DURl Allophanate hydrolaseUREAC -> 2 NH3 + dur2 3.5.1.54 2 C02 YJL12GW NTT2 nitrilase ACNL->INAC+NH3 nit2 3.5.5.1 Sulfur metabolism (Cystein btosynthesls maybe) YJR137C ECM17 Sulfite reductase H2S03 +3 NADPH <-> ecml7 1.8.7.1 H2S+3 NADP

Lipid Metabolism Fatty acid biosynthesis YERO15W FAA2 Long-chain-fatty-acid--CoAligase,Acyl-CoAATP+LCCA+COA<->AMP+pPI+ACOAfaa2 6.2.1.3 synthetase YB.009W FAA3 Long-chain-fatty-acid--CoAligase,Acyl-CoAATP+LCCA+COA<.>AMP+PPI+ACOAfaa3 6.2.1.3 synthetase YOR317 FAAI Long-chain-Fatty-acid--CoAATP + LCCA+ COA <-> faal W 6.2.1.3 lipase, Acyl-CoA AMP + PPI+ACOA

synthetase YMR246W FAA4 Acyl-CoA synthase ATP + LCCA + COA faa4 6.2.1.3 (long-chain fatty acid <-> AMP + pPI+ ACOA
CoA lipase);

contributes to activation of imported myristate YKR009C FOX2 3-Hydroxyacyl-CoAdehydrogenaseHACOA+NAD<->OACOA+NADHfox2b 1.1.1:

YIL160C poll 3-Ketoacyl-CoAthiolaseOACOA+COA->ACOA+ACCOApoll 2.3.1,16 l YPL028W ergl0 Acetyl-CoA C-acetyltransferase,2 ACCOA <-> COA+AACCOAergl0_1 2.3.1.9 ACETOACETYL-COA THIOLASE

2.3.1.9 ergl0 Acetyl-CoA C-acetyltransferase,2 ACCOAm <a COAm+AACCOAmergl0 -COA THIOLASE (mitoch) Fatty Acids Metabolism Mitochondria!
type II
fatty acid synthase YKL192C ACP1 Acylcarrierprotein,componentofmitochondrialtypeIINADHm+Qm->NADm+QH2m ACPl 1.6.5.3 fatty acid synthase YER061C CEMI Beta-ketoacyl-ACP
- synthase, mitochondria!
(3-oxoacyl-[Acyl-carrier-protein]
synthase) YOR221 MCTI Malonyl CoA:acyl C - carrier protein transferase YKLOSSC OARI 3-Oxoacyl-[acyl-carrier-protein]
- reductase YKL192C1Y ACPI/CE Type II fatty ACACPm + 4 MALACPm TypeII-1 1.6.5.3/- acid synthase + 8 NADPHm a g ER061C/YO Ml/MCT NADPm+C100ACPm+4C02m+4ACPm /-I-OSSC

YKL192C/Y ACP 1/CE Type II fatty ACACPm + 5 MALACPm TypeII-2 1.6.5.3/- acid synthase + 10 NADPHm a 10 ER061C/YO MI/MCT NADPm + C120ACPm /-/- + 5 C02m + 5 ACPm OSSC

YKL192C/Y ACP1/CE Type II fatty ACACPm + 6 MALACPm TypeII
1.6.5.3/- acid synthase + 12 NADPHm -> 12 3 ER061C/YO MI/MCT NADPm+C140ACPm+6C02m+6ACPm /-/-OSSC

YKL192C/Y ACPI/CE Type II fatty ACACPm + 6 MALACPm+ TypeII
1.6.5.3/- acid synthase 11 NADPHm a I 1 4 EROGIC/YO Ml/MCT NADPm+C141ACPm+6C02m+GACPm /-/-OSSC
YKL192C/Y ACPI/CE Type II fatty ACACPm+7 MALACPm+ TypeII
1.6.5.3/- acid synthase 14NADPHm-> 14 5 ER061C/YO Ml/MCT NADPm+C160ACPm+7C02m+7ACPm /-/-OSSC

YKL192C/Y ACP1/CE Type II fatty ACACPm+7 MALACPm+ TypeII
1.6.5.3/- acid synthase 13 NADPHm a 13 6 ER061C/YO Ml/MCT NADPm+C161ACPm+7C02m+7ACPm /-/-OSSC

YKLl92C/Y ACP1/CE Type II fatty ACACPm + 8 MALACPm TypeII
1.6.5.3/- acid synthase + 16 NADPHm -> 16 7 ER061C/10 Ml/MCT NADPm+C180ACPm+gC02m+BACPm /-/-OSSC

YKL192C/Y ACP 1/CE Type II fatty ACACPm + 8 MALACPm TypeII
1.6.5.3/- acid synthase ~ + 15 NADPHm a 15 8 ER061C/YO Ml/MCT NADPm+CI8IACPm+8C02m+8ACPm /-/-OSSC

YKL192C/Y ACP 1/CE Type II fatty ACACPm + 8 MALACPm+ Typell_9 1.6.5.3/- acid synthase 14 NADPHm a 14 ER061C/10 Ml/MCT NADPm+C182ACPm+gC02m+8ACPm /-/-R221C/YKL 1/OARl OSSC

Cytosolic fatty acid synthesis YNR016C 6.4.1.2 ACC 1 acetyl-CoA ACCOA + ATP + C02 accl carboxylase (ACC) / biotin carboxylase<-> MALCOA + ADP
+ PI

6.3.4.14 YKLI82w 4.2.1.61; fasl fatty-acyl-CoAsynthase,betaMALCOA+ACP<aMALACP+COAfasl_1 chain 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 4.2.1.61; fasl fatty-acyl-CoAsynthase,betaACCOA+ACP<->ACACP+COAfasl chain 2 YKL182w -1.3.1.9;2.

3.1.38;2.

3.1.39;3.

1.2.14;2.

3.1.86 YER061C 2.3.1.41 CEMI 3-Oxoacyl-[acyl-carrier-protein]synthaseMALACPm+ACACPm->ACPm+C02m+ceml 30ACPm YGR037C1Y 6.4.1.2; ACB 1/A b-Ketoacyl-ACPACACP + 4 MALACP + c100sn synthase (C10,0), fatty acyl CoA 8 NADPH a g NADP
+

NR016C/YK 6.3.4.1;4 CCI/fasl/ synthaseC100ACP+4C02+4ACP

L182W/YPL 2.3.1.85; FAS2/

231w 1.1.1.100 ;2.3.1.41;

4.2.1.61 YGR037C1Y 6.4.1.2; ACB 1IA b-Ketoacyl-ACPACACP + 5 MALACP + c120sn synthase (CI2,0), fatty acyl CoA 10 NADPH a 10 NADP
+

NR016C/YK 6.3.4.1;4 CCl/fasl/ synthaseC120ACP+SC02+SACP

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 I/A b-Ketoacyl-ACPACACP + 6 MALACP + c140sn synthase (C14,0) 12 NADPH a 12 NADP
+

NR016CIYK 6.3.4.1;4 CCl/fasl/ C140ACP+6C02+6ACP

L182W/YPL 2.3.1.85; FAS2/

231w 1.L1.100 ;2.3.1.41;

4.2.1.61 YGR037C/Y 6.4.1.2; ACB1/A b-Ketoacyl-ACPsynthaseI(C14,1)ACACP+6MALACP+11NADPHaIINADP+c141sy NR016C/YK 6.3.4.1;4 CCl/fasl/ C141ACP+6C02+6ACP

L182W/YPL 2.3.1.85; FAS2/ , 23Iw 1.LL100 ;2.3.1.41;

4.2.1.61 YGR037C/Y 6.4.1.2; ACB 1/A b-Ketoacyl-ACPACACP + 7 MALACP + c160sn synthase I (C16,0) 14 NADPH -> 14 NADP
+

NR016C/YK 6.3.4.1;4 CCl/fasl/ C160ACP+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; ACB1/A b-Ketoacyl-ACPsynthaseI(C16,1)ACACP+7MALACP+13NADPH->13NADP+c161sy NR0I6C/YK 6.3.4.1;4 CCl/fasl/ C161ACP+7002 +7ACP

LL82W/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 1IA b-Ketoacyl-ACPACACP + 8 MALACP + c180sy synthase I (C18,0) 16 NADPH -> 16 NADP
+

NR016C/YK 6.3.4.1; 4 CCllfasll C 180ACP + 8 C02 +

L182W/YPL 2.3.1.85; FAS2/

231w 1.1.1.100 ;2.3.1.41;

4.2.1.61 YGR037C/Y 6.4. L2; ACB 1/A b-Ketoacyl-ACPACACP + 8 MALACP + c181 synthase I (C18,1) 15 NADPH a 15 NADP sy +

NR016C/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 YGR037CIY 6.4.1.2; ACB 1/A b-Ketoacyl-ACPACACP + 8 MALACP + c182sy synthase I (C18,2) 14 NADPH -> 14 NADP
+

NR016C/YK 6.3.4.1;4 CCl/fasl/ C182ACP+8C02+8ACP

LI82W/YPL 2.3.1.85; FAS2/

231w 1.L1.100 ;2.3.1.41;

4.2.1.61 YKL182W 4.2.1.61 fasl 3-hydroxypalmitoyl-[acyl-carrier3HPACP <-> 2HDACP
fasl_3 protein] dehydratase YKL182W 1.3.1.9 fasl Enoyl-ACPreductaseAACP+NAD<a 23DAACP+NADHfasl~4 Fatty acid degradation YGL205W/Y 1.3.3.6/2. POXI/FO FattyC140+ATP+7COA +7FADm+7NAD->AMPc140dg acid degradation KR009C/YIL 3.1.18 X2/POT3 + PPI + 7 FADH2m +
7 NADH + 7 ACCOA

YGL205 W/Y 1.3.3.6/2. POX1/FO Fatty0160 + ATP + 8 COA c160dg acid degradation + 8 FADm + 8 NAD
a pMp KR009C/YIL 3.1.18 X2/POT3 + PPI+ g FADH2m +
8 NADH + 8 ACCOA

YGL205W/Y1.3.3.6/2.Fatty acid degradationC180+ATP+9COA +9FADm+9NAD->AMPc180dg KR009C/YIL3.1.18 +PPI+9FADH2m+9NADH+9ACCOA

PhospholipidBlosynthesls - - Glycerol-3-phosphatencyliransferaseGL3P+0.017C100ACP+0.062C120ACP+0.1Gatl_1 C140ACP + 0.27 C160ACP
+ 0.169 C161ACP
+

0.055 C180ACP+0.235 CI81ACP+0.093 C182ACP -> AGL3P+ACP

- - Glycerol-3-phosphateacyltransferaseGL3P+0.017C100ACP+0.062C120ACP+0.1Gat2_1 C140ACP + 0.27 C160ACP
+ 0.169 C161ACP
+

0.055 C180ACP+0.235 C181ACP+0.093 C182ACP -> AGL3P
+ ACP

- - Glycerol-3-phosphateacyltransferaseT3P2+0.017C100ACP+0.062CI20ACP+0.1Gatl C 140ACP + 0.27 C160ACP
+ 0.169 C 161ACP
+

0.055 C180ACP+0.235 C181ACP+0.093 C182ACP -> AT3P2 + ACP

- Glycerol-3-phosphateacyltransferaseT3P2+0.017C100ACP+0.062C120ACP+0.1Gat2_2 C 140ACP + 0.27 C
I60ACP + 0.169 C
161ACP +

0.055 C1SOACP+0.235 C181ACP+0.093 C 182ACP -> AT3P2 + ACP

- -AcyldihydroxyacetonephosphatereductaseAT3P2+NADPHapGL3P+NADPADHAPR

YDL052C2.3.1.511-Acylglycerol-3-phosphateacyltransferaseAGL3P+0.017C100ACP+0.062C120ACP+0.100 SLC1 slcl C 140ACP + 0.270 C160ACP + 0.169 C 161ACP +

0.055 CI80ACP+0.235 C181ACP+0.093 C182ACP -> PA+ACP

- 23.1.51 1-Acylglycerol-3-phosphateacyltransfemseAGL3P+0.017C100ACP+0.062C120ACP+0.100 - AGAT

C140ACP + 0.270 C160ACP
+ 0.169 C161ACP
+

0.055 CISOACP+0.235 CI81ACP+0.093 C 182ACP -> PA +
ACP

YBR029C2.7.7.41CDP-DiacylglycerolsynthetasePAm+CTPm<->CDPDGm+PPImcdsla YBR029C2.7.7.41CDP-DiacylglycerolsynthetasePA+CTP<->CDPDG+PPI cdslb CDSI

YER026C2.7.8.8 phosphatidylserinesynthaseCDPDG+SER<aCMP+PS chola chol YER026C2.7.8.8 PhosphatidylserinesynthaseCDPDGm+SERm<aCMPm+PSmcholb chol YGR170W4.1.1.65phosphatidylserine PS a PE+ C02 psd2 PSD2 decarboxylase located in vacuole or Golgi YNL169C4.1.1.65PhosphatidylserineDecarboxylaselPSmaPEm+C02m psdl PSDI

YGR157W2.1.1.17PhosphatidylethanolamineN-methyltransferaseSAM+PEaSAH+PMME
cho2 YJR073C2.1.1.16Methylene-fatty-acyl-phospholipidsynthase.SAM+pMME->SAH+PDME
opi3 YJR073C2.1.1.16Phosphatidyl-N-methylethanolamineN-PDME+SAM->PC+SAH opi3 methyltransferase YLR133W2.7.1.32Cholinekinase ATP+CHO->ADP+PCHO ekil YGR202C2.7.7.15CholinephosphatecytidylyltransferasePCHO+CTPaCDPCHO+PPI pctl PCTI

YNL130C2.7.8.2 Diacy(glycerolcholinephosphotransferaseCDPCHO+DAGLYapC+CMP cptl CPTI

YDRI47W2.7.1.82Ethanolamine kinase ATP + ETHM a ADP ekil EKII + pETHM

YGR007W2.7.7.14PhosphoethanolaminecytidylyltransferasePETHM+CTP->CDPETN+PPIectl MUQl YHR1232.7.8.1 Ethanolaminephosphotransferase.CDPETN + DAGLY <-> eptl W EPTI CMP + pE

YJL153C5.5.1.4 myo-Inositol-1-phosphatesynthaseG6PaMIIP inol inol YHR046C3.1.3.25myo-Inositol-1(or4)-monophosphataseMIIPaMYOI+pI impal INMl YPRI13W2.7.8.11phosphatidylinositolsynthaseCDPDG+MYOI->CMP+pINSpisl YJR066W2.7.1.137I-Phosphatidylinositol3-kinaseATP+PINS->ADP+PINSP torl torl YKL203C2.7.11371-Phosphatidylinositol3-kinaseATP+PINS->ADP+PINSP tort tort YLR240W2.7.1.1371-PhosphatidylinositolATP + PINS a pDP vps34 vps34 3-kinase + PINSP

YNL267W2.7.1.67Phosphatidylinositol ATP + PINS -> ADP pikl PIKI 4-kinase (PI 4-kinase),+ PINS4P
generates PtdIns 4-P

YLR305C2.7.1.67Phosphatidylinositol ATP + PINS -> ADP sst4 STT4 4-kinase + pINS4P

YFR019W2.7.1.68PROBABLEPHOSPHAT)DYLINOSITOL-4-PINS4P+ATP->D45PI+ADPfabl PHOSPHATE 5-KINASE, 1-phosphatidylinositol-4-phosphate kinase YDR208W2.7.1.68Phosphatidylinositol-4-phosphates-kinase;required mss4 MSS4 for PINS4P+ATP->D45PI+ADP

proper organization of the actin cytoskeleton YPL268W3.1.4.111-phosphatidylinositol-4,5-bisphosphateD45PIaTPI+DAGLY plcl plcl phosphodiesterase YCL004W2.7.8.8 CDP-diacylglycerol-serine0-phosphatidyltransferaseCDPDGm+GL3Pm<->CMPm+PGPmpgsl PGSI

- 3.1.3.27Phosphatidylglycerol PGPm -> PIm + PGm pgpa phosphate phosphatase A

YDL142C2.7.8.5 Cardiolipinsynthase CDPDGm+pGm->CMPm+CLmcrdl YDR284CDPPI diacylglycerol pyrophosphatePA -> DAGLY + PI dppl phosphatase YDR503CLPP 1 lipid phosphate phosphataseDGPP -> PA + PI lppl Sphingoglycolipid Metabolism YDR062W2.3.1.50SerineC-palmitoyltransferasePALCOA+SERaCOA+DHSPH+C02Icb2 YMR296C2.3.1.50SerineC-palmitoyltransferasePALCOA+SER->COA+DHSPH+C02lcbl LCBl YBR265w1.1.1.1023-DehydrosphinganinereductaseDHSPH+NADPH->SPH+NADPtscl0 YDR297WSUR2 SYRINGOMYCINRESPONSEPROTEIN2SPH +02+NADPHapSPH+NADPsur2 - Ceramide synthase PSPH + C260COA a csyna CER2 + COA

- Ceramide synthase PSPH + C240COA -> csynb CER2 + COA

YMR272CSCS7 Ceramide hydroxylase scs7 that hydroxylates the C-26 fatty- CER2 +NADPH+ 02 -> CER3 +NADP

acyl moiety of inositol-phosphorylceramide YKL004WAURI B'S synthase, AUREOBASIDINCER3 + PINS -> IPC aurl A RESISTANCE

PROTEIN

YBR036C CSG2Protein required for IPC + GDPMAN a MIPC csg2 synthesis of the mannosylnted sphingolipids YPL057C SUR1Protein required for IPC + GDPMAN -> MIPCsurl synthesis of the mannosylated sphingolipids YDR072C2:::IPT1MIP2Csynthase,MANNOSYLMIPC+PINS->MIP2C iptl DIPHOSPHORYLINOSITOL
CERAMIDE

SYNTHASE

YOR171C LCB4Long chain base kinase,SPH +ATP -> DHSP lcb4-1 involved in sphingolipid+ ADP

' metabolism YLR260W LCBSLong chain base kinase,SPH +ATP a DHSP + lcb5_1 involved in sphingolipidADP

metabolism YOR171C LCB4Lang chain base kinase,PSPH +ATP -> PHSP lcb4 involved in sphingolipid+ ADP 2 -metabolism YLR260W LCBSLong chain base kinase,PSPH + ATP -> PHSP lcb5 involved in sphingolipid+ ADP 2 -metabolism YJL134W LCB3Sphingoid base-phosphateDHSP a SPH+PI lcb3 phosphatase, putative regulator of sphingolipid metabolism and stress response YKR053C YSR3Sphingoid base-phosphateDHSP a SPH+PI ysr3 phosphatase, putative regulator of sphingolipid metabolism and stress response YDR294C DPLIDihydrosphingosine-I-phosphateDHSP a PETHM+ C16A dpll lyase Sterolthesis biosyn YMLt26C4.1.3.5HMGS3-hydroxy-3-methylglutarylcoenzymeAsynthaseH3MCOA+COA<->ACCOA+AACCOAhmgs YLR450W1.1.1.34hmg23-hydroxy-3-methylglutaryl-coenzymeA(HMG-CoA)MVL+COA+2NADP<->H3MCOA+2NADPHhmg2 reductase isozyme YML075C1.1.1.34hmgl3-hydroxy-3-methylglutaryl-coenzymeMVL+COA+2 NADP <-> hmgl A (HMG-CoA) H3MCOA+2NADPH

reductase isozyme YMR208W2.7.1.36ergl2mevalonatekinase ATP+MVL->ADP+PMVL ergl2-1 YMR208W2.7.1.36ergl2mevalonatekinase CTP+MVL->CDP+PMVL ergl2 YMR208W2.7.1.36ergl2mevalonatekinase GTP+MVLaGDP+PMVL ergl2 YMR208W2.7.1.36ergl2mevalonatekinase UTP+MVL->UDP+PMVL ergl2 YMR220W2.7.4.2ERGS48kDnPhosphomevalonatekinaseATP+PMVL->ADP+PPMVL erg8 YNR043W4.1.1.33MVDIDiphosphomevalonatedecarboxylaseATP+PPMVL->ADP+PI+IPPP+C02mvdl YPL117C5.3.3.2idilIsopentenyl diphosphate:dimethylallylIPPP <-> DMPP
idil diphosphate isomerase (IPP isomerase) YJL167W2.5.1.1ERG20prenyltransferase DMPP+IPPPaGPP+PPI erg20_1 YJLI67W2.5.1.10ERG20Famesyl diphosphate GPP +IPPP a FPP+PPI erg20 synthetase (FPP synthetase) 2 YHR190W2.5.1.21ERG9Squnlenesynthase. 2FPP+NADPH->NADP+SQLerg9 YGR175C1.14.99.7ERGISqualenemonooxygenaseSQL+02+NADP->S23E+NADPHergl YHR072W5.4.99.7ERG72,3-oxidosqualene-lanosterolS23E a LNST erg?
cyclase YHR007c1.14.14.1argilcytochromeP4501anostero114a-demethylaseLNST+RFP+02->IGST+OFPergll_I

YNL280c1.-.-:ERG24C-l4 sterolreductase IGST+NADPH->DMZYMST+NADPerg24 YGR060w1: ERG25C-4 sterol methyl 3 02 + DMZYMST -> erg25_1 : oxidase IMZYMST
:

YGLOOic5.3.3.1ERG26C-3steroldehydrogenase(C-4decarboxylase)IMZYMST->IIMZYMST+C02erg26_1 YLR100C YLRI00C-3sterolketoreductaseIIMZYMST+NADPH->MZYMST+NADPargil -C

YGR060w1: ERG25C-4 sterol methyl 3 02 +MZYMST -> IZYMSTerg25 : oxidase 2 :

YGLOOIc5.3.3.1ERG26C-3 sterol dehydrogenaseIZYMST -> IIZYMST+ erg26 (C-4 decarboxylase) C02 2 YLRIOOC YLR100C-3 sterol keto reductaseIIZYMST+NADPH-> ZYMST+NADPergl l -C

YML008c2.1.1.41erg6S-adenosyl-methioninedelta-24-sterol-c-ZYMST+SAMaFEST+SAH
erg6 methyltransferase YMR202W ERG2C-8 sterol isomerase FEST a EPST erg2 YLR056wI: ERG3GS sterol desaturase EPST+ 02 +NADPH -> erg3 : NADP + ERTROL
:

YMROISc1.14.14:ERGSC-22 sterol desaturaseERTROL + 02 + NADPH ergs -> NADP + ERTEOL

YGL012w1:::ERG4sterolC-24reductase ERTEOL+NADPH->ERGOST+NADPerg4 LNST + 3 02 + 4 NADPHunkrxn3 +NAD -> MZYMST +

C02 + 4 NADP + NADH

MZYMST+302+4NADPH+NAD->ZYMST+unkrxn4 C02 + 4 NADP + NADH

5.3.3.5 Cholestenol delta-isomeraseZYMST+ SAM -> ERGOST+cdisoa SAH

Nucleotidetabolism Me Histidine Biosynthesis YOL0612.7.6.1PRSSribose-phosphate pyrophosphokinaseRSP + ATP <-> PRPP prs5 W + AMP

YBL068W2.7.6.1PRS4ribose-phosphatepyrophosphokinase4RSP+ATP<->PRPP+AMP prs4 YER099C2.7.6.1PRS2ribose-phosphate pyrophosphokinaseRSP + ATP <-> PRPP prs2 2 +AMP

YHLO11C2.7.6.1PRS3ribose-phosphate pyrophosphokinaseRSP+ ATP <-> PRPP prs3 3 + AMP

YKLLSIW2.7.6.1PRS1ribose-phosphatepyrophosphokinaseRSP+ATP<->PRPP+pMP prsl YIR027C3.5.2.5dollallantoinase ATN <a pTT doll YIR029W3.5.3.4dal2allantoicase ATT<->UGC+UREA dal2 YIR032C3.5.3.19dal3ureidoglycolate hydrolaseUGC <-> GLX + 2 NH3 dal3 + C02 Purina metabolism YJLOOSW4.6.1.1CYRIadenylatecyclase ATP->cAMP+PPI cyrl YDR454C2.7.4.8GUKIguanylatekinase GMP+ATP<->GDP+pDp gukl l YDR454C2.7.4.8GUKIguanylatekinnse DGMP+ATP<->DGDP+ADP gukl YDR454C2.7.4.8GUKIguanylatekinase GMP+DATP<->GDP+DADP gukl_3 YMR300C2.4.2.14nde4phosphoribosylpyrophosphatePRPP + GLN -> PPI+ ade4 amidotransferase GLU + PRAM

YGL234W6.3.4.13ade5,7glycinamide ribotide PRAM + ATP + GLY ade5 synthetase and ntrtinoimidnzole<a ADP + PI+ GAR

ribotide synthetase YDR408C2.1.2.2ade8glycinamideribotidetransformylaseGAR+FTHF->THF+FGAR nde8 YGR061C6.3.5.3ade65'-phosphoribosylformylglycinnmidinesynthetaseFGAR+ATP+GLNaGLU+ADP+PI+FGAMnde6 YGL234W6.3.3.1nde5,7Phosphoribosylfortnylglycinamidecyclo-lipaseFGAM+ATP->ADP+PI+AIRade7 YOR128C4.1.1.21ade2phosphoribosylamino-imidazole-cnrboxylnseCAIR<->AIR+C02 nde2 YARO15W6.3.2.6adelphosphoribosylaminoimidazolesuccinocarbozamideCAIR+ATP+ASP<->ADP+PI+SAICARadel synthetase YLR359W4.3.2.2ADE135'-Phosphoribosyl-4-(N-succinocarboxamide)-5-SAICAR<aFUM+AICAR ndel3-1 nminoimidazole lyase YLR028C2.1.2.3ADE165-aminoimidazole-4-carboxamideribonucleotideAICAR+FTHF<->THF+PRFICAadel6-1 (AICAR) transfonnylaseVIMP
cyclohydrolase YMR120C2.L2.3ADE175-aminoimidazole-4-carboxamideribonucleotideAICAR+FTHF<->THF+PRFICAadel7_1 (AICAR) transformylaseVIMP
cyclohydrolase YLR028C3.5.4.10ADE165-aminoimidaaofe-4-carboxamideribonucleotidePRFICA<->IMP
adel6-2 -(AICAR) transfortnylaseVIMP
cyclohydrolase YMR120C2.1.2.3ADE17IMP cyclohydrolase PRFICA <a IMP adel7_2 YNL220W6.3.4.4adel2adenylosuccinatesynthetaseIMP+GTP+ASP->GDP+PI+ASUCadel2 YLR359W4.3.2.2ADE13AdenylosuccinateLyaseASUC<->FUM+AMP adel3_2 YAR073W1.1.1.205fun63putativeinosine-5'-monophosphatedehydrogenaseIMP+NADaNADH+XMP fun63 YHR216W1.1.1.205pur5purineexcretion IMP+NAD->NADH+XMP pur5 YML056C1.1.1.205IMD4probableinosine-5'-monophosphatedehydrogenaseIMP+NAD->NADH+XMP prm5 (IMP

YLR432W1.1.1.205IMD3probable inosine-5'-monophosphateIMP +NAD a NADH + prm4 dehydrogenase XMP

(IMP

YAR0751.1.1.205YAR075Protein with strong IMP +NAD a NADH +XMPprm6 W similarity to inosine-5'-W monophosphate dehydrogenase, frameshified from YAR073W, possible pseudogene YMR2176.3.5.2,GUAlGMP synthase XMP + ATP + GLN -> goal W GLU + AMP + PPI
+ GMP

6.3.4.1 YML035C3.5.4.6amdlAMPdeaminnse AMP->IMP+NH3 amdl YGL248W3.1.4.17PDEI3',5'-Cyclio-nucleotidecAMP -> AMP pdel phosphodiesterase, low affinity YOR360C3.1.4.17pde23',5'-Cyclic-nucleotideCAMP -> AMP pde2_1 phosphodiesterase, high affinity YOR360C3.1.4.17pde2 cdAMP -> DAMP pde2 YOR360C3.1.4.17pde2 cIMP a IMP pde2 YOR360C3.1.4.17pde2 cGMP -> GMP pde2 YOR360C3.1.4.17pde2 cCMP a CMP pde2_5 YDR530C53 APA25',5"'-P-I,P-4-tetraphosphatephosphorylaseIIADP+ATP->PI+ATRP
npa2 YCLOSOC. apal5',5"'-P-1,P-4-tetraphosphatephosphorylaseIIADP+GTP->PI+ATRP
opal-1 .
.
2.7.7.53 YCLOSOC2.7.7.53apal5',5"'-P-1,P-4-tetraphosphateGDP + GTP a pI+ GTRPapal phosphorylase II 3 -Pyrlmidine metabolism YJLI30C2.1.3.2ura2Aspartate-carbamoyltransfernseCAP + ASP -> CAASP um2_1 + PI

YLR420W3.5.2.3ura4dihydrooratase CAASP <-> DOROA um4 YKL2t6W1.3.3.1oraldihydroorotatedehydrogenaseDOROA+02<aH202+OROA ural_1 YKL216W1.3.3.1PYRDDihydroorotatedehydrogenaseDOROA+Qm<aQH2m+OROA oral YML106W2.4.2.10URASOrotatephosphoribosyltransfernselOROA+PRPP<.>PPI+OMP um5 YMR271C2.4.2.10URA10Orotatephosphoribosyltransferase2OROA+PRPP<->PPI+OMP ural0 YEL0214.1.1.23ura3orotidine-5'-phosphateOMP -> C02 +UMP ura3 W decarboxylase YKL024C2.7.4.14URA6Nucleoside-phosphate ATP + UMP <-> ADP npk kinase + UDP

YHR128W2.4.2.9furlUPRTase,UmcilphosphoribosyltransfernseURA+PRPPaUMP+PPI
furl YPR062W3.5.4.1FCYIcytosinedeaminase CYTS->URA+NH3 fcyl - 2.7.1.21 Thymidine (deoxyuridine)DU + ATP -> DUMP tdkl kinase + ADP

2.7.1.21 Thymidine (deoxyuridine)DT+ ATP -> ADP + tdk2 kinase DTMP

YNR012W2.7.1.48URKIUridinekinase URI+GTP->UMP+GDP urkl_1 YNR012W2.7.1.48URKICytodinekinase CYTD+GTPaGDP+CMP urkl YNR012W2.7.1.48URKIUridine kinase, convertsURI+ ATP -> ADP +UMPurkl ATP and uridine to 3 ADP and -UMP

YLR209C2.4.2.4PNP1Protein with similarityDU + PI <-> URA+ deoal to human purine nucleosideDRIP

phosphorylase, Thymidine (deoxyuridine) phosphorylase, Purine nucleotide phosphorylase YLR209C2.4.2.4PNPIProtein with similarityDT + PI <a THY + deoa2 to human purine nucleosideDR1P

phosphorylase, Thymidine (deoxyuridine) phosphorylese YLR245C3.5.4.5CDD1Cytidinedeaminase CYTD->URI+NH3 cddl_1 YLR245C3.5.4.5CDDICytidinedeaminase DCaNH3+DU cddl YJR0572.7.4.9cdc8dTMP kinase DTMP +ATP <-> ADP cdc8 W + DTDP

YDR353W1.6.4.5TRRIThioredoxinreductase OTHIO+NADPHaNADP+RTHIOttrl YHR106W1.6.4.5TRR2mitochondrialthioredoxinreductaseOTHIOm+NADPHm->NADPm+RTHIOmtrr2 YBR252W3.6.1.23DUTIdUTPpyrophosphatnse(dUTPnse)DUTP->PPI+DUMP dull YOR074C2.1.1.45cdc21Thymidylatesynthase DUMP+METTHF->DHF+DTMPcdc21 _ 2.7.4.14 Cytidylate kinase DCMP +ATP <-> ADP cmkal + DCDP

- 2.7.4.14 Cytidylate kinase CMP +ATP <-> ADP cmka2 + CDP

YHRI44C3.5.4.12DCDIdCMPdeaminase DCMP4>DUMP+NH3 dcdl YBL039C6.3.4.2URA7CTP synthase, highly UTP + GLN + ATP -> um7 homologus to URA8 GLU + CTP + pDP I
CTP + PI -synthase YJR103W6.3.4.2URA8CTPsynthase UTP+GLN+ATP->GLU+CTP+ADP+PIum8 YBL039C6.3.4.2URA7CTP synthase, highly ATP + UTP + NH3 -> um7 homologus to URAB ADP + PI+ CTP 2 CTP

synthase YJR103W6.3.4.2UItA8CTPsynthase ATP+UTP+NH3->ADP+PI+CTPum8 YNL292W4.2.1.70PUS4Pseudouridine synthaseURA+RSP <a PURISP pus4 YPL212C4.2.1.70PUS1intranuclear protein URA+RSP <-> PURISPpus) which exhibits a nucleotide-specific intron-dependent tRNA
pseudouridine synthase activity YGL063W4.2.1.70PUS2pseudouridine synthaseURA + RSP <-> PURISPpus2 YFL0014.2.1.70deglSimilar to rRNA methyltransferaseURA+RSP <-> PURISPdegl W (Caenorhabditis elegans) and hypothetical 28K protein (alkaline endoglucnnnse gene 5' region) from Bacillus sp.

Salvage Pathways YML022W2.4.2.7APTIAdeninephosphoribosyltransferaseAD+PRPP->PPI+AMP apt) YDR441C2.4.2.7APT2similar to adenine AD + PRPP a PPI+AMPaP~
phosphoribosyltransferase YNL141W3.5.4.4AAHIadenine aminohydrolaseADN-> INS+NH3 aahla (adenine deaminase) YNLI41W3.5.4.4AAHIadenineaminohydrolase(adeninedeaminase)DA->DIN+NH3 aahlb YLR2D9C2.4.2.1PNPIPurinenucleotidephosphorylase,XanthosineDIN+PI<->HYXN+DR1Pxapal phosphorylase YLR209C2.4.2.1PNPIXanthosine phosphorylase,DA+ pI <-> AD +DR1Pxapa2 Purine nucleotide phosphorylase YLR209C2.4.2.1PNPIXanthosinephosphorylaseDG+PI<aGN+DRIP xapa3 YLR209C2.4.2.1PNPIXanthosine phosphorylase,HYXN + R1P <-> xapa4 Purine nucleotide INS + PI

phosphorylase YLR209C2.4.2.1PNPlXanthosine phosphorylase,AD+ RIP <a pI+ADN xapa5 Purine nucleotide phosphorylase YLR209C2.4.2.1PNPIXanthosinephosphorylase,PurinenucleotideGN+R1P<->pI+GSN
xapa6 phosphorylase YLR209C2.4.2.1PNP1Xanthosinephosphorylase,PurinenucleotideXAN+R1P<->PI+XTSINExapa7 phosphorylase YJR1332.4.2.22XPTIXanthine-guanine phosphoribosyltransferaseXAN + PRPP a XMP
gptl W + PPI

YDR400W3.2.2.1urhlPurinenucleosidase GSN->GN+RIB pm21 YDR400W3.2.2.1urhlPurinenucleosidase ADN->AD+RIB purl) YJR105W2.7.1.20YJR105Adenosinekinase ADN+ATP->AMP+ADP prm2 W
YDR226W2.7.4.3adklcytosolic adenylate ATP+AMP <-> 2 ADP adkl_1 kinase YDR226W2.7.4.3adklcytosolic adenylate GTP + AMP <-> pDP adkl kinase + GDP 2 YDR226W2.7.4.3adklcytosolic adenylate ITP + AMP <-> ADP adkl kinase + IDP 3 YER170W2.7.4.3ADK2Adenylate kinase (mitochondria)ATPm+AMPm <-> 2 adk2-1 GTP:AMP ADPm phosphotransferase) YER170W2.7.4.3adk2Adenyfate kinase (mitochondria)GTPm+AMPm <-> ADPm+adk2 GTP:AMP GDPm 2 -phosphotransferase) YER170W2.7.4.3adk2Adenylate kinase (mitochondria)ITPm +AMPm <-> adk2 GTP:AMP ADPm+IDPm 3 -phosphotransferase) YGR180C1.17.4.1RNR4ribonucleotide reductase, small subunit (alt), beta chain YIL066C1.17.4.1RNR3Ribonucleotide reductaseADP + RTHIO -> rnr3 (ribonucleoside-diphosphateDADP + OTHIO

reductase) large subunit, alpha chain YJL026W1.17.4.1rnr2small subunit of ribonucleotide reductase, beta chain YKL067W2.7.4.6YNKlNucleoside-diphosphatekinaseUDP+ATP<->UTP+ADP ynkl YKL067W2.7.4.6YNKINucleoside-diphosphatekinaseCDP+ATP<.>CTP+ADP ynkl YKL067W2.7.4.6YNKINucleoside-diphosphatekinaseDGDP+ATP<->DGTP+ADPynkl YKL067W2.7.4.6YNKlNucleoside-diphosphateDUDP + ATP <a DUTPynkl kinese + ADP 4 YKL067W2.7.4.6YNKINucleoside-diphosphatekinaseDCDP+ATP<->DCTP+ADPynkl YKL067W2.7.4.6YNKINucleoside-diphosphatekinaseDTDP+ATP<->DTTP+ADPynkl YKL067W2.7.4.6YNKINucleoside-diphosphatekinaseDADP+ATP<->DATP+ADPynkl_7 YKL067W2.7.4.6YNKINucleoside diphosphateGDP + ATP <-> GTP ynkl kinase + ADP 8 YKL067W2.7.4.6YNKINucleosidediphosphatekinaseIDP+ATP<->ITP+IDP ynkl - 2.7.4.11 Adenylate kinase, DAMP + ATP <-> dampk dAMP kinase DADP + ADP

YNL141W3.5.4.2AAH1Adeninedeaminase AD->NH3+HYXN Yicp - 2.7.1.73 lnosinekinase B~IS+ATP->IMP+ADP gskl - 2.7.1.73 Guanosine kinase GSN + ATP -> GMP gsk2 + ADP

YDR399W2.4.2.8HPTIHypoxanthinephosphoribosyltransferaseHYXN+PRPP->PPI+IMPhptl YDR399W2.4.2.8HPTIHypoxanthinephosphoribosyltransferaseGN+PRPP->PPI+GMP hptl 2.4.2.3 Uridinephasphorylase URI+PI<,>URA+R1P udp YKL024C2.1.4:URA6Uridylatekinase UMP+ATP<->UDP+ADP pyrhl YKL024C2.1.4:URA6Uridylate kinase DUMP + ATP <-> pyrh2 DUDP + ADP

3.2.2.10 CMP glycosylase CMP -> CYTS + RSP cmpg YHR144C3.5.4.13DCD1dCTPdeaminase 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.L3.5 5'-Nucleotidase RCMP->DC+PI usha5 3.1.3.5 5'-Nucleotidase CMP a CYTD + PI usha6 - 3.1.3.5 5'-Nucleotidase AMPaPI+ADN usha7 3.1.3.5 5'-Nucleotidase GMP->PI+GSN ushn8 - 3.1.3.5 5'-Nucleotidase IMP -> PI+ INS usha9 - 3.13.5 5'-Nucleotidase XMP->PI+XTSINE ushal2 - 3.1.3.5 5'-Nucleotidase UMPaPI+URI ushall YER070W1.17.4.1 Ribonucleoside-diphosphatereductaseADP+RTHIO->DADP+OTHIOmrl_1 RNRI

YER070W1.17.4.1RNR1Itibonucleoside-diphosphatereductnseGDP+RTHIO->DGDP+OTHIOmrl_2 YER070W1.17.4.1RNRIRibonueleoside-diphosphatereductaseCDP+RTHIOaDCDP+OTHIOmrl_3 YER070W1.17.4.1RNRIRibonucleoside-diphosphateUDP + RTHIO -> OTHIOmrl-4 reductase + DUDP

- 1.17.4,2 Ribonucleoside-triphosphateATP + RTHIO -> DATP nrddl reductase + OTHIO

- 1.17.4.2 Ribonucleoside-triphasphateGTP + RTHIO -> DGTP nrdd2 reductase + OTHIO

- 1.17.4.2 Itibonucleoside-triphosphateCTP + RTHIO a DCTP nrdd3 reductase + OTHIO

- 1.17.4.2 Ribonucleoside-triphosphateUTP + RTHIO -> OTHIOnrdd4 reductnse + DUTP

3.6.1: Nucleoside triphosphataseGTP -> GSN + 3 PI muttl 3.6.1: Nucleoside triphosphataseDGTP a DG + 3 PI muit2 YML035C3.2.2.4AMDLAMPdeaminase AMPaAD+RSP amn YBR284W3.2.2.4YBR284Protein with similarityAMP a pD + RSP amnl to AMP deaminase W

YJL070C3.2.2.4YJL070CProtein with similarityAMP -> AD + RSP amn2 to AMP deaminase Amino Acid Metabolism Glutamate (Amtnosugars Metabolism met) YMR250W4.1.1.15GADIGlutamatedecarboxylaseBGLU->GABA+C02 btn2 YGR019W2.6.1.19ugalAminobutyrateaminotransaminase2GABA+AKGaSUCCSAL+GLUugal YBR006w1.2.1.16YBR006Succinatesemialdehydedehydrogenase-NADPSUCCSAL+NADP->SUCC+NADPHgabda w YKL104C2.6.1.16GFAlGlutamine fmctose-6-phosphateF6P + GLN -> GLU gfal amidotmnsferase + GA6P

(glucoseamine-6-phosphate synthase) YFL017C2.3.1.4GNAIGlucosamine-phosphnteN-acetyltransferaseACCOA+GA6P<aCOA+NAGA6Pgoal YELOSSW5.4.2.3PCMIPhosphoacetylglucosamineNAGA1P <->NAGA6P pcmla Mutase YDL103C2.7.7.23QRIIN-Acetylglucosamine-1-phosphate-uridyltransfemseUTP+NAGA1P<->UDPNAG+PPIqril YBR023C2.4.1.16chs3chitin synthase 3 UDPNAG a CHIT+ UDP chs3 YBR038W2.4.1.16CHS2chitin synthase 2 UDPNAG a CHIT+UDP chs2 YNLl92W2.4.1.16CHS1chitin synthase 2 UDPNAG a CHIT+UDP chsl YHR037W1.5.1.12puttdelta-1-pyrroline-5-carboxylatedehydrogenaseGLUGSALm+NADPm->NADPHm+GLUmput2-1 PSCm+NADm->NADHm+GLUmputt YDL171C1.4.1.14GLTIGlutamatesynthase(NADH)AKG+GLN+NADH->NAD+2GLUgltl YDL215C1.4.1.4GDH2glutamatedehydrogenaseGLU+NAD->AKG+NH3+NADHgdh2 YAL062W1.4.1.4GDH3NADP-linked glutamateAKG +NH3 +NADPH <-> gdh3 dehydrogenase GLU+NADP

YOR375C1.4.1.4GDHINADP-specific glutamateAKG + NH3 +NADPH gdhl dehydrogenase <-> GLU +NADP

YPR035W6.3.1.2glnlglutaminesynthetase GLU+NH3+ATP->GLN+pDP+PIglnl YEL058W5.4.2.3PCMIPhosphoglucosaminemutaseGA6P<->GA1P pemlb - 3.5.1.2 Glutaminase A GLN a GLU+NH3 glnasea - 3.5.1.2 GlutaminaseB GLN->GLU+NH3 glnaseb Glucosamine - 5.3.1.10 Glucosamine-6-phosphatedeaminaseGA6P->F6P+NH3 nagb Arabinose YBR149W1.1.1.117ARA1D-arabinosel-dehydrogenase(NAD(P)+),ARAB+NADapRpBLAC+NADHaral_1 YBR149W1.1.1.117ARAID-arabinose 1-dehydrogenase(NAD(P)+),ARAB+NADP->ARABLAC+NADPHoral-2 Xylose YGR194C2.7.1.17XKS1Xylulakinase XUL+ATPaXSP+ADP xksl Mannitol 1.1.1.17 Mannitol-I-phosphates-dehydrogenaseMNT6P+NAD<aF6P+NADH mtld AlanineAspartate m and Metabolis YKL106W2.6.1.1AATIAsparatetransaminase OAm+GLUm<->ASPm+AKGmaatl_1 YLR027C2.6.1.1AAT2Asparate transaminaseOA+ GLU <-> ASP +AKGaat2_1 YAR035W2.3.1.7YATICamitine0-acetyltransferaseCOAm+ACARm->ACCOAm+CARmyatl YML042W2.3.1.7CAT2Camitine0-acetyltransferaseACCOA+CAR->COA+ACAR cat2 YDRI11C2.6.1.2YDRIputative alanine transaminasePYR+GLU <a pKG +pLA alab l l C

YLR089C2.6.1.2YLR089alanine aminotmnsferase,PYRm+GLUm <-> AKGm cfx2 mitochondrial precursor+ALAm C (glutamic--YPR145W6.3.5.4ASNIasparaginesynthetase ASP+ATP+GLN->GLU+ASN+AMP+PPIasnl YGR124W6.3.5.4ASN2asparaginesynthetase ASP+pTP+GLN->GLU+ASN+AMP+PPIasn2 YLL062C2.1.1.10MHTIPutative cobalamin-dependentSAM+ HCYS -> SAH mhtl homocysteine S- +MET

methyltransferase, Homocysteine S-methyltransferase YPL273W2.1.1.10SAM4Putativecobalamin-dependenthomocysteineS-SAM+HCYS->SAH+MET
sam4 methyltransferase Asparaglne YCR024c6.1.1.22YCR024c ATPm+ASPm+TRNAm->AMPm+PPIm+mas asn-tRNAsynthetase,mitochondrial ASPTRNAm YHR019C6.1.1.23DED81asn-tRNA synthetase ATP + ASP + TRNA ded81 a pMP + PPI + ASPTRNA

YLR155C3.5.1.1ASP3-1Asparaginase, extracellularASN -> ASP + NH3 asp3 YLRI57C3.5.1.1ASP3-2Asparaginase,extraeellularASN->ASP+NH3 asp3 YLR158C3.5.1.1ASP3-3Asparaginase,extracellularASN->ASP+NH3 asp3 YLR160C3.5.1.1ASP3-4Asparaginase,extracellularASN->ASP+NH3 asp3 YDR321W3.5.1.1nsplAsparaginase ASN->ASP+NH3 aspl Glycine,ne reonine seri and metabolism th YER081W1.1.1.95seraPhosphoglyceratedehydrogenase3PG+NADaNADH+PHP sera YIL074C1.1.1.95ser33Phosphoglycemtedehydrogenase3PG+NADaNADH+PHP ser33 YOR184W2.6.1.52seraphosphoserine transaminasePHP + GLU -> AKG serl + 3PSER 1 -YGR208W3.L3.3ser2phosphoserinephosphntase3PSER->PI+SER ser2 YBR263W2.1.2.1SHMlGlycinehydroxymethyltransfernseTHFm+SERm<->GLYm+METTHFmshml YLR058C2.1.2.1SHM2Glycine hydroxymethyltransferaseTHF + SER <a GLY shm2 + METTHF

YFL030W2.6.1.44YFL030Putative alanine glyoxylateALA+ GL7C <-> PYR agt aminotransferase + GLY
(serine W pyruvate aminotransferase) YDR019C2.1.2.10GCV glycine cleavage T GLYm + THFm +NADm gcvl-1 1 protein (T subunit a METTHFm +NADHm of glycine decarboxylase complex+C02+NH3 YDR019C2.1.2.10GCV glyeine cleavage T GLY + THF+ NAD -> gcvl 1 protein (T subunit METTHF +NADH+ C02 2 of glycine + -decarboxylase complexNH3 YER052C2.7.2.4hom3Aspartate kinase, ASP + ATP -> ADP hom3 Aspartate kinase + BASP
I, II, III

YDRI58W1.2.1.11hom2asparticbetasemi-aldehydedehydrogenase,AspartateBASP+NADPH->NADP+PI+ASPSAhom2 semialdehyde dehydrogenase YJR139C1.1.1.3hom6HomoserinedehydrogenaseIASPSA+NADHaNAD+HSER hom6-1 YJR139C1.1.1.3hom6HomoserinedehydrogenaseIASPSA+NADPHaNADP+HSERhom6 YHR025W2.7.1.39thrlhomoserinekinase HSER+ATP->ADP+PHSER ihrl YCR053W4.2.99.2thr4threoninesynthase PHSERaPI+THR thr4_1 YGR155W4.2.1.22CYS4Cystathioninebeta-synthaseSER+HCYS->LLCT cys4 YEL046C4.1.2.5GLYlThreonineAldolase GLY+ACAL->THR glyl YMR189W1.4.4.2GCV2Glycine decarboxylaseGLYm+LIPOm <-> SAPm+C02mgcv2 complex (P-subunit), glycine synthase (P-subunit), Glycine cleavage system (P-subunit) YCL064C4.2.1.16chalthreonine deaminase THR a NH3 + OBUT chal_1 YER086W4.2.1.16ilvlL-Serinedehydratase THRmaNH3m+OBUTm ilvl YCL064C4.2.1.13chalcatabolic serine (threonine)SER a PYR+NH3 chal-2 dehydratase YIL167W4.2.1.13YIL167catabolic serine (threonine)SER a PYR+NH3 sdll dehydratase W
1.1.L103 ThreoninedehydrogenaseTHR+NAD->GLY+AC+NADHtdhlc Methionine metabolism YFR055W4.4.L8YFR055Cystathionine-b-lyaseLLCT->HCYS+PYR+NH3 metc W

YER043C3.3.1.1SAHlputative S-adenosyl-L-homocysteineSAH a HCYS +ADN sahl hydrolase YER091C2.1.1.14methvitamin B 12-(cobalamin)-independentHCYS + MTHPTGLU ->
meth isozyme of THPTGLU + MET

methionine synthase (also called NS-methyltetrahydrofolate homocysteine methyltransferase or 5-methyltetrahydropteroyl triglutamate homocysteine methyltransferase) - 2.1.1,13 Methioninesynthase HCYS+MTHFaTHF+MET met6_2 YAL012W4.4.1.1cys3cystathionine gamma-lyaseLLCT a CYS + NH3 cys3 + OBUT

YNL277W2.3.1.31met2homoserine0-traps-acetylaseACCOA+HSER<->COA+OAHSERmet2 YLR303W4.2.99.10METI7O-Acetylhomoserine(thiol)-lyaseOAHSER+METH->MET+AC
metl7_1 YLR303W4.2.99.8MET17O-Acetylhomoserine(thiol)-lyaseOAHSER+H2S->AC+HCYS metl7 YLR303W4.2.99.8,metl7O-acetylhomoserinesulfhydrylase(OAHSHLase);OAHSER+H2S->AC+HCYS metl7 4.2.99.10 converts O-acetylhomoserine into homocysteine YML082W4.2.99.9YML082putativecystathioninegamma-synthaseOSLHSER<->SUCC+OBUT+NH4metl7h W

YDR502C2.5.1.6sam2S-adenosylmethioninesynthetaseMET+ATPaPPI+PI+SAM sam2 YLRISOW2.5.1.6samlS-adenosylmethioninesynthetaseMET+ATP->PPI+PI+SAM semi YLR172C2.1.1.98DPHSDiphthine synthase SAM+ CALH -> SAH dph5 + DPTH

Cystelnesynthesis Bio YJROIOW2.7.7.4met3ATPsulfurylase SLF+ATP->PPI+APS met3 YKLOO1C2.7.1.25metl4adenylylsulfatekinaseAPS+ATP->ADP+PAPS metl4 YFR030W1.8.1.2metl0sulfite reductase H2S03 +3 NADPH <a metl0 H2S+3 NADP

- 2.3.1.30 Serinetransacetylase SER+ACCOA->COA+ASER cysl YGR012W4.2.99.8YGR012putative cysteine ASER+ H2S a AC + soil synthase (O-acetylserineCYS l W sulthydrylase) (O-YOL064C3.1.3.7MET223 - 5 Bisphosphate PAP -> AMP + PI met22 nucleotidase YPRI67C1.8.99.4MET16PAPSReductase PAPS+RTHIOa0THI0+H2S03+PAPmetl6 YCL050C2.7.7.5apaldiadenosine 5',5"'-P I ADP + SLF <-> PI+ apal 1,P4-tetraphosphate APS 2 phosphorylase -Branchedhain C Amino Acid Metabolism (Valine, Leucine and Isoleucfne) YHR208W2.6.1.42BATIBranched chain amino OICAPm+GLUm<->AKGm+LEUmbatl_1 acidaminotransferase YHR208W2.6.1.42BATIBranched chain amino OMVALm+GLUm <a pKGm+ILEmbatl_2 acid aminotransferase YJR148W2.6.1.42BAT2branched-chain amino bat2-1 acid transaminase, highly similar OMVAL
+ GLU <-> AKG + ILE

to mammalian ECA39, which is regulated by the oncogene myc YJR148W2.6.1.42BAT2Branched chain amino OIVAL+GLU <-> AKG bat2_2 acid aminotransferase+ VAL

YJR1482.6.1.42BAT2branched-chain amino bat2 W acid transaminase, 3 highly similar OICAP -+ GLU <a pKG + LEU

to mammalian ECA39, which is regulated by the oncogene myc YMR108W4.1.3.18ilv2Acetolactate synthase,OBUTm + PYRm a ABUTmilv2-1 large subunit + C02m YCL009C4.1.3.18ILV6Acetolactate synthase, small subunit YMR108W4.1.3.18ilv2Acetolactate synthase,2 PYRm-> C02m+ACLACmilv2 large subunit 2 -YCL009C4.1.3.18ILV6Acetolactate synthase, small subunit YLR355C1.1.1.86ilv5Keto-acidreductoisomernseACLACm+NADPHm->NADPm+DHVALmilv5_1 YLR355C1.1.1.86ilv5Keto-acidreductoisomeraseABUTm+NADPHm->NADPm+DHMVAmilv5 YJR016C4.2.1.9ilv3Dihydroxy acid dehydrataseDHVALm -> OIVALm ilv3_1 YJR016C4.2.1.9ilv3Dihydroxy acid dehydrataseDHMVAm -> OMVALm ilv3_2 YNL104C4.1.3.12LEU4alpha-isopropylma(ateACCOAm + OIVALm a leu4 synthase (2-IsopropylmalateCOAm + IPPMALm Synthase) YGL009C4.2.1.33leulIsopropylmalate isomeraseCBHCAP <a IPPMAL leul_1 YGL009C4.2.1.33leulisopropylmalate isomerasePPMAL <-> IPPMAL leul_2 YCL018W1.1.1.85leu2beta-IPM (isopropylmalate)IPPMAL +NAD -> NADH leu2 dehydrogenase + OICAP + C02 Lysine biosynthesis/degradation - 4.2.1.79 2-Methylcitrate dehydrataseHCITm <-> HACNm lys3 YDR234W4.2.1.36lys4Homonconitate hydratnseHICITm <-> HACNm lys4 YIL094C1.1.L155LYS12Homoisocitratedehydrogenase(Strathem:1.1.1.87)HICITm+NADm<->OXAm+C02m+NADHm1ys12 - non-enzymatic OXAm <-> C02m + AKAmlys 12b - 2.6.1.39 2-AminoadipntetransnminaseAKA+GLU<->AMA+AKG omit YBRIISC1.2.1.31lys2L-Aminoadipnte-semialdehydedehydrogenase,largeAMA+NADPH+ATP->AMASA+NADP+AMPlys2-1 subunit + ppI

YGL154C1.2.1.31IysSL-Aminoadipate-semialdehyde dehydragenase, small subunit YBR115C1.2.1.31lys2L-Aminoadipate-semialdehydedehydrogenase,largeAMA+NADH+ATP->AMASA+NAD+AMP+lys2_2 subunit PPI

YGL154C1.2.1.31IysSL-Aminoadipate-semialdehyde dehydrogenase, small subunit YNROSOC1.5.1.10lys9Saccharopine dehydrogenaseGLU+AMASA+NADPH <-> lys9 (NADP+, L-glutamate SACP+NADP

forming) YIR034C1.5.1.7lyslSaccharopinedehydrogenase(NAD+,L-lysineSACP+NAD <->LYS+AKG+NADHlysla forming) YDR037W6.1.1.6krsllysyl-tRNAsynthetase,cytosolicATP+LYS+LTRNA->AMP+PPI+LLTRNAkrsl YNL073W6.1.1.6mskllysyl-tRNAsynthetase,mitochondrialATPm+LYSm+LTRNAm->AMPm+pPLn+mskl LLTRNAm YDR368W1.1.1:YPRlsimilar to aldo-keto reductase Arginine metabolism YMR062C2.3.1.1ECM40Amino-acidN-acetyltransferaseGLUm+ACCOAmaCOAm+NAGLUmecm40 YER069W2.7.2.8arg5AcetylglutamatekinaseNAGLUm+ATPm->ADPm+NAGLUPmarg6 YER069W1.2.1.38arg5N-acetyl-gamma-glutamyl-phosphatereductaseandNAGLUPm+NADPHm->NADPm+PIm+arg5 acetylglutamate kinaseNAGLUSm YOLI40W2.6.1.11arg8AcetylornithineaminotransferaseNAGLUSm+GLUmapKGm+NAORNmarg8 YMR062C2.3.1.35ECM40Glutamate N-acetyltransferaseNAORNm + GLUm -> ecm40 ORNm +NAGLUm 2 YJL130C6.3.5.5ura2carbamoyl-phophate GLN + 2 ATP + C02 ura2 synthetase, aspartate-> GLU + CAP + 2 2 ADP + PI -transcarbamylase, and glutamine amidotransferase YJR109C6.3.5.5CPA2carbamyl phosphate GLN + 2 ATP + C02 cpa2 synthetase, large -> GLU + CAP +2 chain ADP + pI

YOR3036.3.5.5epalCarbamoyl phosphate W synthetase, samll chain, arginine specific YJL088W2.1.3.3arg3Ornithine carbamoyltransferaseORN + CAP -> C1TR+ arg3 PI

YLR438W2.6.1.13callOmithine transaminaseORN + AKG -> GLUGSAL+cart GLU

YOL058W6.3.4.5arglarginosuccinatesynthetaseCITR+ASP+ATP<->AMP+ppI+ARGSUCCargl YHRO18C4.3.2.1arg4argininosuccinate ARGSUCC <-> FUM+ARG nrg4 lyase YKLI84W4.1.1.17spelOrnithinedecarboxylaseORN->PTRSC+C02 spel YOL052C4.1.1.50spotS-adenosylmethionine SAM <-> DSAM+ C02 spe2 decarboxylase YPR069C2.5.1.16SPE3putrescine aminopropyltmnsferasePTRSC + SAM -> SPRMDspe3 (spermidine + SMTA

synthase) YLR146C2.5.1.22SPE4Sperminesynthase DSAM+SPRMD->SMTA+SPRMspe4 YDR242W3.5.1.4AMD2Amidase GBAD->GBAT+NH3 amd2_1 YMR293C3.5.1.4YMR293Probnble Amidase GBAD -> GBAT+NH3 amd C

YPL11IW3.5.3.1carlarginase ARG->ORN+UREA carl YDR341C6.1.1.19YDR341arginyl-tRNAsynthetaseATP+ARG+ATRNA->AMP+PPI+ALTRNAatrna C

YHR091C6.1.1.19MSRIarginyl-tRNAsynthetaseATP+ARG+ATRNA->AMP+PPI+ALTRNAmsrl YHR068W1.5.99.6DYS1deoxyhypusinesynthaseSPRMD+Qm->DAPRP+QH2mdysl Histidine metabolism hi l YEROSSC2.4.2.17hislATPphosphoribosyltransferasePRPP+ATP->PPI+PRBATPs YCL030C3.6.1.31his4phosphoribosyl-AMP PRBATP -> PPI + PRBAMPhis4-I
cyclohydrolese /
phosphoribosyl-ATP pyrophosphohydrolase / histidinol dehydrogenase YCL030C3.5.4.19his4histidinol dehydrogenasePRBAMP -> PRPP his4_2 YIL020C5.3.1.16his6phosphoribosyl-5-amino-1-phosphoribosyl-4-PRFP a pRLP
his6 imidazolecarboxiamide isomerase YOR202W4.2.1.19his3imidazoleglycerol-phosphateDIMGP -> IMACP his3 dehydratase YIL116W2.6.1.9hisshistidinol-phosphateaminotransferaseIMACP+GLUaAKG+HISOLPhiss YFR025C3.1.3.15his2HistidinolphosphataseHISOLP->PI+HISOL his2 YCL030C1.1.1.23his4phosphoribosyl-AMP his4 cyclohydrolase / 3 phosphoribosyl- HISOL
+ 2 NAD -> HIS +

ATP pyrophosphohydrolase / histidinol dehydrogenase YBR248C2.4.2:his?glutamineamidotrnnsferase:cyclasePRLP+GLN->GLU+AICAR+DIMGPhis?

YPR033C6.1.1.21htslhistidyl-tRNAsynthetaseATP+HIS+HTRNA->AMP+PPI+HHTRNAhtsl YBR034C2.1.1:hmtlhnRNParginineN-methyltransferaseSAM+HIS->SAH+MHIS hmtl YCL054W2.1.1:spblputative RNA methyltransferase YMLI 2.1.1:coq5ubiquinone biosynthesis lOC methlytransferase COQS

YOR201C2.1.1:pet56rRNA(guanosine-2'-O-)-methyltransferase YPL266W2.1.1:dimldimethyladenosinetransferase Phenylalanine, tyrosine and tryptophan biosynthesis (Aromatic Amino Actds) YBR249C4.1.2.15AR043-deoxy-D-arabino-heptulosonate aro4 7-phosphate (DAHP) E4P + PEP -> PI +

synthaseisoenzyme YDR035W4.1.2.15AR03DAHPsynthase\;a.k.a.phospho-2-dehydro-3-E4P+PEP->PI+3DDAH7P
aro3 deoxyheptonate aldolase, phenylalanine-inhibited\;

phospho-2-keto-3-deoxyheptonnte aldolase\; 2-dehydro-3-deoxyphosphoheptonate aldolase\;3-deoxy-D-arabine-heptulosonate-7-phosphate synthase YDR127W4.6.1,3arolpentafunetionalarompolypeptide(contains;3-3DDAH7P->DQT+PI
arol-1 dehydroquinate synthase,3-dehydroquinate dehydratase (3-dehydroquinase), shikimate 5-dehydrogenase, shikimate kinase, and epsp synthase) YDR127W4.2.1.10arol3-DehydroquinatedehydrataseDQT->DHSK arol YDR127W1.1.1.25arolShikimatedehydrogennseDHSK+NADPH->SME+NADParol YDRI27W2.7.1.71arolShikimatekinaseI,II SME+ATP->ADP+SMESP arot YDR127W2.5.1.19nrol3-Phosphoshikimate-1-carboxyvinyltransfernseSMESP+PEP->3PSME+PI nrol YGL148W4.6.1.4aro2Chorismatesynthase 3PSMEaPI+CHOR aro2 YPR060C5.4.99.5aro7Chorismate mutase CHOR -> PHEN aro7 YNL316C4.2.1.51pha2prephenate dehydratasePHEN a C02 + PHPYR pha2 YHR1372.6.1:AR09putative aromatic PHPYR + GLU <-> AKG aro9_1 W amino acid aminotransferase+ PHE
II

YBR166C1.3.1.13tyrlPrephenatedehydrogenase(NADP+)PHEN+NADP->4HPP+C02+NADPHtyrl YGL202W2.6.1:AR08aromatic amino acid 4HPP + GLU -> AKG aro8 aminotransferase + TYR
I

YHR1372.6.1:AR09aromatic amino acid 4HPP + GLU a AKG aro9_2 W aminotransferase + TYR
II

- 1.3.1.12 Prephanate dehydrogenasePHEN + NAD -> 4HPP tyra2 + C02 +NADH

YER090W4.1.3.27Rp2 Anihranilatesynthase CHOR+GLN->GLU+PYR+ANtrp2_1 YKL211C4.1.3.27trp3Anthranilate synthaseCHOR+ GLN -> GLU trp3_1 + PYR+AN

YDR354W2.4.2.18trp4anthranilate phosphoribosylAN + PRPP a pill+NPRANirp4 transferase YDR007W5.3.1.24trilln-(5'-phosphoribosyl)-anthranilateisomeraseNPRAN->CPADSP
inPl YKL2114.1.1.48trp3Indoleglycerol phosphateCPADSP -> C02 + IGP trp3 C synthase 2 YGL026C4.2.1.20trp5tryptophan synthetaseIGP + SER -> T3P1 trp5 + TRP

YDR256CLl1.1.6CTAIcatalase A 2 H202 -> 02 ctal YGR0881.11.1.6CTTIcytoplasmic catalase 2 H202 -> 02 cttl W T

YKLI06W2.6.1.1AATIAsparate aminotransferase4HPP + GLU <-> pKG aatl + TYR 2 YLR027C2.6.1.1AAT2Asparate aminotransferase4HPP + GLU <-> AKG aat2 + TYR 2 YMR170C1.2.1.5ALD2CytosolicaldeyhdedehydrogenaseACAL+NAD->NADH+AC ald2 YMR169C1.2.1.5ALD3strong similarity ACAL+NAD->NADH+AC ald3 toaldehydedehydrogenase YOR374W1.2.1.3ALD4mitochondrial aldehydeACALm + NADm a NADHmald4_1 dehydrogenase + ACm YOR374W1.2.1.3ALD4mitochondrialaldehydedehydrogenaseACALm+NADPm->NADPHm+ACmald4_2 YER073W1.2.1.3ALDSmitochondrinlAldehydeDehydrogenaseACALm+NADPm->NADPHm+ACmald5_I

YPL061W1.2.1.3ALD6CytosolicAldehydeDehydrogenaseACAL+NADP->NADPH+AC ald6 YJR0781.13.11.1YJR078Protein with similarityTRP + 02 -> FKYN tdo2 W to indoleamine 2,3-1 W dioxygenases, which catalyze conversion of tryptophan and other indole derivatives into kynurenines, Tryptophan 2,3-dioxygenase - 3.5.1.9 Kynurenine formamidaseFKYN -> FOR+KYN kfor ~

YLR231C3.7.1.3YLR231probable kynureninaseKYN -> ALA + AN kynu_1 (L-kynurenine hydrolase) C

YBL098W1.14.13.9 Kynurenine3-hydroxylase,NADPH-dependentflavinKYN+NADPH+02->HKYN+NADPkmo W monooxygenase that catalyzes the hydroxylation of kynurenine to 3-hydroxykynurenine in tryptophan degradation and nicotinic acid synthesis, Kynurenine monooxygenase YLR231C3.7.1.3YLR231probable kynureninaseHKYN a HAN + ALA kynu (L-kynurenine hydrolase) ?
-C
YJR025C1.13.11.6 3-hydroxyanthranilate3,4-dioxygenase(3-HAO)(3-HAN+02->CMUSA bnal BNAI

hydroxyanthranilic acid dioxygenase) (3-hydroxyanthranilatehydroxyanihranilic acid dioxygenase) (3-hydroxyanthranilate oxygenase) - 4.1.1.45 Picolinic acid decarboxylaseCMUSA a C02+AM6SA aaaa - 1.2.1.32 AM6SA+NAD->AMUCO+NADHaaab - L5.1: AMUCO+NADPH->AKA+NADP+NH4aaac - 1.3.11.27 4-Hydroxyphenylpymvate4HPP + 02 a HOMOGEN tyrdega dioxygenase + C02 1.I3.1L5 Homogentisatel,2-dioxygenaseHOMOGEN+02->MACAC tyrdegb - 5.2.1.2 Maleyl-acetoacetate MACAC -> FUACAC tyrdegc isomerase 3.7.1.2 Fumarylacetoacetase FUACAC a FUM + ACTACtrydegd YDR268w6.1.1.2MSWIiryptophanyl-tRNAsynthetase,mitochondrialATPm+TRPm+TRNAm->AMPm+PPIm+mswl TRPTRNAm YDR242W3.5.1.4AMD2putative nmidase PAD a PAC+NH3 amd2 YDR242W3.5.1.4AMD2putative nmidase IAD a 1AC +NH3 amd2 2.6.1.29 Diamine transaminase SPRMD + ACCOA -> spry ASPERMD + COA

- 1.5.3.1 Polyamine oxidase ASPERMD + 02 -> APRUTsprb I + APROA+ H202 - 1.5.3.11 Polyamineoxidase APRUT+02aGABAL+APROA+H202sprc - 2.6.1.29 Diamine transaminase SPRM + ACCOA a pSPRM+sprd COA

- 1.5.3.11 Polyamine oxidase ASPRM + 02 -> ASPERMDspre + APROA + H202 Proltne biosynthesis YDR300C2.7.2.11prolgamma-glutamyl kinnse,GLU+ ATP -> ADP + prol glutamate kinase GLUP

YOR323C1.2.1.41PR02gamma-glutamyl phosphateGLUP + NADH -> NAD pro2 reductase + PI+ GLUGSAL 1 YOR323C1.2.1.41pro2gamma-glutamyl phosphateGLUP + NADPH a NADP pro2 reductnse + PI+ GLUGSAL 2 - spontaneous conversionGLUGSAL <-> PSC gpsl (Strathem) - spontaneous conversionGLUGSALm <a pSCm gps2 (Strathem) YER023W1.5.1.2pro3Pyrroline-5-carboxylatePSC + NADPH -> PRO pro3 reductase + NADP 1 YER023W1.5.1.2pro3Pyrroline-5-carboxylatereductasePHC+NADPH->HPRO+NADPpro3 YER023W1.5.1.2pro3Pyrroline-5-carboxylatereductasePHC+NADH->HPRO+NAD pro3 YLR142W1.5.3:PUTIProlineoxidase PROm+NADmaPSCm+NADHmpro3 -Metabolismf o Other Amino Acids beta-Alanlne metabolism 1.2.1.3 aldehydedehydrogenase,mitochondria))GABALm+NADm->GABAm+NADHmaldl YER0731.2.1.3 mitochondria) AldehydeLACALm + NADm <-> ald5 W ALDS Dehydrogenase LLACm + NADHm 2 -Cyanoamlnoacid metabolism YJL126W3.5.5.1 NITRILASE APROP->ALA+NH3 nit2 YJL126W3.5.5.1 NITRILASE ACYBUT->GLU+NH3 nit2 Proteins,tides Pep and Amlnoacids Metabolism YLR195C2.3.1.97GlycylpeptideN-tetradecanoyltransferaseTCOA+GLPaCOA+TGLP nmtl nmtl YDL040C2.3.1.88Peptide alpha-N-acetyltransferaseACCOA+PEPD->COA+APEPnatl nail YGRI47C2.3.1.88Peptide alpha-N-acetyltransferaseACCOA+ PEPD a COA+ nat2 Glutathlone Biosynthesis YJL101C6.3.2.2 gamma-glutamylcysteinesynthetaseCYS+GLU+ATPaGC+PI+ADPgshl GSHI

YOL049W6.3.2.3 GlutathioneSynthetaseGLY+GC+ATPaRGT+PI+ADPgsh2 YBR244W1.11.1.9Glutathione peroxidase2 RGT+H202 <-> OGT gpx2 YIR037W1.11.1.9Glutathione peroxidase2 RGT+H202 <-> OGT hyrl HYRI

YKL026C1.11.1.9Glutathione peroxidase2 RGT+H202 <-> OGT gpxl GPXI

YPL091W1.6.4.2 GlutathioneoxidoreductaseNADPH+OGT->NADP+RGT girl GLRI

YLR299W2.3.2.2 gamma-glutamyltranspeptidaseRGT+ALAa CGLY+ALAGLYecm38 Metabolism of Complex Carbohydrates Starch and sucrose metabolism YGR032W2.4.1.341,3-beta-Glucan synthaseUDPG-> 13GLUCAN+UDP gsc2 YLR342W2.4.1.341,3-beta-Glucan synihaseUDPG a 13GLUCAN+UDP fksl FKSI

YGR306W2.4.1.34Protein with similarityUDPG-> 13GLUCAN+UDP fks3 FKS3 to Fkslp and Gsc2p YDR261C3.2.1.58Exo-l,3-b-glucanase 13GLUCAN->GLC exg2 exg2 YGR282C3.2.1.58Cell wall endo-beta-1,3-glucanase13GLUCAN -> GLC bgl2 YLR300W3.2.1.58Exo-1,3-beta-glucanase13GLUCANaGLC exgl exgl YOR190W3.2.1.58spomlation-specific 13GLUCAN -> GLC sprl sprl exo-1,3-beta-glucanase Glycoprotein Biosynthesis /
Degradation YMR013C2.7.1.108Dolicholkinase CTP+DOL->CDP+DOLP sec59 sec59 YPR183W2.4.1.83Dolichyl-phosphate GDPMAN+DOLP->GDP+DOLMANPdpml DPM1 beta-D-mannosyltransferase YAL023C2.4.1.109Dolichyl-phosphate-mannose--proteinDOLMANP->DOLP+MANNANpmt2 mannosyltransferase YDL093W2.4.1.109Dolichyl-phosphate-mannose--proteinDOLMANP->DOLP+MANNANpmts PMTS

mannosyltransferase YDL095W2.4.1.109Dolichyl-phosphate-mannose--proteinDOLMANP->DOLP+MANNANpmt) PMTI

mannosyltransferase YGR199W2.4.1.109Dolichyl-phosphate-mannose--proteinDOLMANP->DOLP+MANNANpmt6 mannosyltransferase YJR143C2.4.1.109Dolichyl-phosphate-mannose--proteinDOLMANP->DOLP+MANNANpmt4 mannosyltransferase YOR3212.4.1.109Dolichyl-phosphate-mannose--proteinDOLMANP a DOLP+MANNANpmt3 mannosyltransferase YBRI99W2.4.1.131Glycolipid2-alpha-mannosyltransferaseMAN2PD+2GDPMANa2GDP+2MANPDktr4 YBR205W2.4.1.131Glycolipid2-alpha-mannosyltransferaseMAN2PD+2GDPMAN->2GDP+2MANPDktr3 YDR483W2.4.1.131Glycolipid2-alpha-mannosyltransferaseMAN2PD+2GDPMAN->2GDP+2MANPDkre2 kre2 YJL139C2.4.1.131Glycolipid2-alpha-mannosyltransferaseMAN2PD+2GDPMAN->2GDP+2MANPDyurl yurl YKR0612.4.1.131Glycolipid 2-alpha-mannosyltransferaseMAN2PD + 2 GDPMAN ktrL
W KTR2 -> 2 GDP + 2MANPD

YOR099W2.4.1.131Glycolipid2-alpha-mannosyltransferaseMAN2PD+2GDPMAN->2GDP+2MANPDktrl KTRI

YPL053C2.4.1.131Glycolipid2-alpha-mannosyltransferaseMAN2PD+2GDPMAN->2GDP+2MANPDktr6 Aminosugars metabolism YER062C3.1.3.21DL-glycerol-3-phosphataseGL3P->GL+PI horl YIL053W3.1.3.21DL-glycerol-3-phosphataseGL3P->GL+PI rhr2 YLR307W3.5.1.41ChitinDeacetylase CHIT->CHITO+AC cdal CDAI

YLR308W3.5.1.41ChitinDeacetylase CHIT->CHITO+AC cdn2 MetabolismComplex of Lipids Glycerol (Glycerollpid metabolism) YFL053W2.7.1.29dihydroxyacetonekinaseGLYN+ATPaT3P2+ADP dak2 YML070W2.7.1.29putativedihydroxyacetonekinaseGLYN+ATP->T3P2+ADP dakl DAKI

YDL022W1.1.1.8 glycerol-3-phosphatedehydrogenase(NAD)T3P2+NADH->GL3P+NAD gpdl GPDI

YOL059W1.1.1.8 glycerol-3-phosphate T3P2 +NADH -> GL3P gpd2 GPD2 dehydrogenase (NAD) + NAD

YHL032C2.7.1.30glycerolkinase GL+ATP->GL3P+ADP gull GUT) YIL155C1.1.99.5glycerol-3-phosphate GL3P + FADm a T3P2 gut2 GUT2 dehydrogenase + FADH2m DAGLY + 0.017 C100ACPdaga + 0.062 C120ACP
+

0.100 C140ACP + 0.270 C 160ACP + 0.169 C 161ACP + 0.055 CISOACP + 0.235 C181ACP +

0.093 C182ACP ->
TAGLY + ACP

Metabolism of Cofactors, Vitamins, and Other Substances Thiamine (Vitamin Bl) metabolism YOR143C2.7.6.2 ThiaminpyrophosphokinaseATP+THIAMIN->AMP+TPPthi80 YOR143C2.7.6.2 ThiaminpyrophosphokinaseATP+TPPaAMP+TPPP thi80 - thiC protein AIR-> AHM thic YOLOSSC2.7.1.49Bipartite protein AHM+ ATP -> AHMP thi20 THI20 consisting of N-terminal+ ADP

hydroxymethylpyrimidine phosphate (HMP-P) kinase domain, needed for thiamine biosynthesis, fused to C-terminal PetlBp-like domain of indeterminant function YPL258C2.7.1.49Bipartite protein AHM + ATP -> AHMP thi21 THI21 consisting of N-terminal+ pDP

hydmxymethylpyrimidine phosphate (PIMP-P) kinase domain, needed for thiamine biosynthesis, fused to C-terminal Petl8p-like domain of indeterminant function YPRI212.7.1.49THI22Bipartite protein AHM + ATP -> AHMP thi22 W consisting of N-terminal+ ADP

hydroxymethylpyrimidine phosphate (I-IMP-P) kinase domnin, needed for thiamine biosynthesis, fused to C-terminal Petl8p-like domain of indeterminant function YOLOSSC2.7.4.7THI20HMP-phosphntekinase AHMP+ATP->AHMPP+ADP thid - Hypothetical T3P1 +PYR a DTP unkrxnl - thiGprotein DTP+TYR+CYSaTHZ+HBA+C02thig - thiE protein DTP + TYR+ CYS -> thie THZ + HBA+ C02 - thiFprotein DTP+TYR+CYS->THZ+HBA+C02thif - thiHprotein DTP+TYR+CYSaTHZ+HBA+C02ihih YPL214C2.7.1.50THI6Hydroxyethylthiazole THZ + ATP a THZP thim kinase + ADP

YPL214C2.5.1.3THI6TMP pyrophosphorylase,THZP + AHMPP -> THMPthi6 hydroxyethylthiazole + PPI
kinase - 2.7.4.16 Thiamin phosphate THMP + ATP <-> TPP thil kinase + ADP

3.1.3: (DL)-glycerol-3-phosphataseTHMP -> THIAMIN + unkrxn8 Riboflavinetabolism m YBL033C3.5.4.25riblGTPcycIohydrolaseIl GTPaD6RP5P+FOR+PPI ribl YBR153W3.5.4.26RIB7HTP reductase, secondD6RPSP a p6RP5P +NH3ribdl step in the riboflavin biosynthesis pathway YBR153W1.1.1.193rib?Pyrimidinereductase A6RPSP+NADPH->A6RPSP2+NADPrib?

- Pyrimidine phosphataseA6RPSP2 -> A6RP+PI prm - 3,4 Dihydroxy-2-butanone-4-phosphateRLSP a DB4P + FOR ribb synthase YBR256C2.5.1.9RIBSItiboflavinbiosynthesispathwayenzyme,6,7-dimethyl-DB4P+A6RP->DSRL+PI ribs 8-ribityllumazine synthase, apha chain YOL143C2.5.1.9RIB4Riboflavin biosynthesis pathway enzyme, 6,7-dimethyl-8-ribityllumazine synthase, beta chain YAR0713.1.3.2pholAcid phosphatase FMN -> RIBFLAV + phol W l PI l YDR236C2.7.1.26FMN Riboflavin kinase RB3FLAV + ATP -> fmnl 1 FMN +ADP 1 YDR236C2.7.1.26FMN1Riboflavinkinase RE3FLAVm+ATPm->FMNm+ADPmfmnl YDL045C2.7.7.2FAD1FADsynthetase FMN+ATP->FAD+PPI fadl 2.7.7.2 FADsynthetase FMNm+ATPmaFADm+PPLn fadlb Vitamin (Pyridoxine) Biosynthesis metabolism 2.7.1.35 Pyridoxinekinase PYRDX+ATP->PSP+ADP pdxka - 2.7.1.35 Pyridoxine kinase PDLA+ ATP -> PDLASP pdxkb + ADP

- 2.7.1.35 Pyridoxine kinase PL+ ATP -> PLSP +ADPpdxkc YBR035C1.4.3.5PDX3Pyridoxines'-phosphateoxidasePDLASP+02apL5P+H202+NH3pdx3 YBR035C1.4.3.5PDX3Pyridoxines'-phosphateoxidasePSP+02<apLSP+H202 pdx3 YBR035C1.4.3.5PDX3Pyridoxine 5'-phosphatePYRDX+02 <-> PL+H202pdx3 oxidase 3 YBR035C1.4.3.5PDX3Pyridoxines'-phosphateoxidasePL+02+NH3<aPDLA+H202pdx3 YBR035C1.4.3.5PDX3Pyridoxine 5'-phosphatePDLASP+ 02 -> PLSP+H202pdx3 oxidase +NH3 5 YOR184W2.6.1.52serlHypothetical transaminase/phosphoserineOHB+GLU <a PHT+AKG
serl transaminase c YCR053W4.2.99.2thr4Threoninesynthase PHT->4HLT+PI thr4 3.1.3: Hypothetical Enzyme PDLASP a PDLA + PI hor26 Pantothenate biosynthesis and CoA

- 3 MALCOA -> CHCOA+ biol 2 COA + 2 C02 - 2.3.1.47 8-Amino-7-oxononanoateALA+ CHCOA <a C02 biof synthase + COA + AONA

YNR058W2.6.1.62BI037,8-diamino-pelargonicSAM+AONA <-> SAMOB bio3 acid aminotransferase+DANNA
(DAPA) aminotransferase YNR057C6.3.3.3BI04dethiobiotinsynthetaseC02+DANNA+ATP<->DTB+PI+ADPbio4 YGR286C2.8.1.6BI02Biotin synthase DTB +CYS <-> BT bio2 Folatenthesis biosy YGR267C3.5.4.16fol2GTP cyclohydrolase GTP -> FOR+ AHTD fol2 I

- 3.6.1: Dihydroneopterin triphosphateAHTD a PPI+DHPP ntpa pyrophosphorylase YDR4813.1.3.1pho8Glycerophosphatase, AHTD a DHP + 3 PI pho8 C Alkaline phosphatase;
Nucleoside triphosphatase YDLl00C3.6.1:YDLl00DihydroneopterinmonophosphatedephosphorylaseDHPPaDHP+PI
dhdnpa C

YNL256W4.1.2.25foilDihydroneopterin aldolaseDHP -> AHHMP + GLAL foil YNL256W2.7.6.3foil6-Hydroxymethyl-7,8 foil dihydropterin pyrophosphokinase 2 AHHMP +ATP -> AMP
+ AHHMD

YNR033W4.1.3:ABZIAminodeoxychorismatesynthaseCHOR+GLNaADCHOR+GLU abzl - 4::: AminodeoxychorismatelyaseADCHOR->PYR+PABA pabc YNL256W2.5.1.15fallDihydropteroatesynthasePABA+AHHMD->PPI+DHPTfall_3 YNL256W2.5.1.15FollDihydropteroate synthasePABA+AHHMP -> DHPT fall-4 - 6.3.2.12 DihydrofolatesynthaseDHPT+ATP+GLU->ADP+PI+DHFfait YOR236W1.5.1.3dfrlDihydrofolatereductaseDHFm+NADPHm->NADPm+THFmdfrl_1 YOR236W1.5.1.3dfrlDihydrofolatereductaseDHF+NADPHaNADP+THF dfrl_2 - 6.3.3.2 5-Formyltetrahydrofolatecyclo-lipaseATPm+FTHFm->ADPm+PLn+MTHFmflfa 6.3.3.2 5-Formyltetrahydrofolatecyclo-lipaseATP+FTHF->ADP+PI+MTHFflfb YKLI32C6.3.2.17RMAIProtein with similarityTHF+ATP+GLU<->ADP+PI+THFGrural tofolylpolyglutamatesynthase;

converts tetrahydrofolyl-[Glu(n)]
+ glutamate to tetrahydrofolyl-[Glu(n+1)]

YMR113W6.3.2.17FOL3DihydrofolatesynthetaseTHF+ATP+GLU<->ADP+PI+THFGfol3 YOR2416.3.2.17MET7Folylpolyglutamate THF+ ATP + GLU <-> met?
W synthetase, involved ADP + PI +THFG
in methionine biosynthesis and maintenance of mitochondrial genome One late carbon [MAP:00670[
pool by fo YPL023C1.5.1.20MET12MethylenetetrahydroFolatereductaseMETTHFm+NADPHm->NADPm+MTHFmmetl2 YGL125W1.5.1.20metl3MethylenetetrahydrofolatereductaseMETTHFm+NADPHm->NADPm+MTHFmmetl3 YBR084W1.5.1.5mislthe mitochondria) METTHFm+NADPm <-> misl~1 trifunctional enzyme METHFm+NADPHm tetrahydroflate synthase ' YGR204W1.S.L5ade3the cytoplasmic trifunctional+NADPH ade3,1 enzyme Cl- METTHF+NADP <a METHF

tetrahydrofolate synthase YBR084W6.3.4.3mislthemitochondrialtrifunctionalenzymeCl-THFm+FORm+ATPm->ADPm+PIm+FTHFmmisl-2 tetrahydroflate synthase YGR204W6.3.4.3ade3the cytoplasmic trifunctionalTHF+FOR+ATP a ADP+PI+FTHFade3 enzyme Cl- 2 -tetrahydrofolate synthase YBR084W3.5.4.9mistthe mitochondria) METHFm <-> FTHFm misl trifunctional enzyme 3 Cl- -ietrahydroflate synthase YGR204W3.5.4.9ade3the cytoplasmic trifunctionalMETHF <-> FTHF ade3 enzyme C 1- 3 -tetrahydrofolate synthase YKR080W1.5.115MTD1NAD-dependents,l0-methylenetetrahydrafolateMETTHF+NAD->METHF+NADHmtdl dehydrogenase YBL013W2.1.2.9fmtlMethionyl-tRNATransformylaseFTHFm+MTRNAm->THFm+FMRNAmfmtl CoenzymeBiosynthesis A

YBR176W2.1.2.11ECM31KetopentoatehydroxymethyltransferaseOIVAL+METTHF->AKP+THFecm31 YHR063C1.1.1.169PANSPutative ketopantoateAKP +NADPH -> NADP pane reductase (2-dehydropantoate+ PANT

reductase) involved in coenzyme A synthesis, has similarity to Cbs2p, Ketopantoate reductase YLR355C1.1.1.86ilv5Ketol-acidreductoisomeraseAKPm+NADPHmaNADPm+PANTmilv5 YIL,145C6.3.2.1YIL145CPantoate-b-alanine PANT+ bALA + ATP panca lipase -> AMP + PPI+ PNTO

YDR5312.7.1.33YDR531Putative pnntothenatePNTO + ATP -> ADP coca W kinase involved in + 4PPNT0 coenzyme A

W biosynthesis, Pantothenate kinase - 6.3.2.5 Phosphopantothenate-cysteineligase4PPNT0+CTP+CYS->CMP+PPI+4PPNCYSpclig - 4.1.1.36 Phosphopantothenate-cysteine4PPNCYS -> C02 + pcdcl decarboxylase 4PPNTE

- 2.7.7.3 Phospho-pantethiene 4PPNTE + ATP -> PPI+patrana adenylyltransferase DPCOA

- 2.7.7.3 Phospho-pantethieneadenylyltransferase4PPNTEm+ATPm->PPIm+DPCOAmpatranb - 2.7.1.24 DephosphoCoA kinase DPCOA+ ATP -> ADP dphcoaka + COA

- 2.7.1.24 DephosphoCoAkinase DPCOAm+ATPm->ADPm+COAmdphcoakb - 4.1.1.11 ASPARTATE ALPHA-DECARBOXYLASEASP a C02 + bALA pancb YPL148C2.7.8.7PPT2Acyl carrier-protein COA -> PAP + ACP acps synihase, phosphopantetheine protein nansferase for Acplp NAD
Biosynthesis YGL037C3.5.1.19PNCINicotinamidase NAM<->NAC+NH3 nadh YOR209C2.4.2.11NPTINAPRTase NAC +PRPP a NAMN nptl + PPI

1.4.3: Aspartateoxidase ASP+FADm->FADH2m+ISUCCnadb 1.4.3.16 Quinolate synthase ISUCC + T3P2 -> PI+ nada QA

YFR047C2.4.2.19QPTIQuinolatephosphoribosyltransferaseQA+PRPPaNAMN+C02+PPInadc YLR328W2.7.7.18YLR328Nicotinamidemononucleotide(NMN)NAMN+ATP->PPI+NAAD naddl W adenylyltransferase YHR074W6.3.5.1QNSIDeamido-NADammonialigaseNAAD+ATP+NH3aNAD+AMP+PPInade YJR049c2.7.1.23utrlNAD kinase, POLYPHOSPHATENAD + ATP a NADP nadf KINASE (EC + ADP I
-2.7.4.1) / NAD+ KINASE
(EC 2.7.1.23) YEL041w2.7.1.23YEL041NAD kinase, POLYPHOSPHATENAD + ATP a NADP nadf KINASE (EC + ADP 2 -w 2.7.4.1) / NAD+ KINASE
(EC 2.7.1.23) YPL188w2.7.1.23POSSNAD kinase, POLYPHOSPHATENAD + ATP -> NADP nadf KINASE (EC +ADP 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 + ADPRB3 nadi 2.4.2.1 strong similarity ADN+PI<->AD+RB' nadgl topurine-nucleosidephosphorylases 2.4.2.1 strong similarity GSN + PI <a GN + nadg2 to purine-nucleoside RIP
phosphorylases Ntcotinic is Acid from synthes TRP

YFR047C2.4.2.19QPTIQuinolatephosphoribosyltransferaseQAm+PRPPm->NAMNm+C02m+PPImmnadc YLR328W2.7.7.18YLR328NAMNadenylyltransferaseNAMNm+ATPm->PPIm+NAADmmnaddl W

YLR3282.7.7.18YLR328NAMN adenylyl transferaseNMNm + ATPm a NADm mnadc2 W + PPIm W

YHR074W6.3.5.1QNSIDeamido-NADammonialigaseNAADm+ATPm+NH3m->NADm+AMPm+mnade PPIm YJR049c2.7.1.23utrlNAD kinase, POLYPHOSPHATENADm + ATPm a NADPm mnadf KINASE (EC + ADPm 1 -2.7.4.1) / NAD+ KINASE
(EC 2.7.1.23) YPL188w2.7.1.23POSSNAD kinase, POLYPHOSPHATENADm + ATPm a NADPm mnadF
KINASE (EC + ADPm 2 -2.7.4.1) I NAD+ KINASE
(EC 2.7.1.23) YEL041w2.7.1.23YEL041NAD kinase, POLYPHOSPHATENADm+ ATPm a NADPm mnadf KINASE (EC + ADPm 5 w 2.7.4.1)/NAD+KINASE(EC2.7.1:L3) - 3.1.2: NADP phosphatase NADPm a NADm + PIm mnadphps YLR209C2.4.2.1PNPIstrong similarity ADNm + PIm <-> ADm mnadgl to purine-nucleoside + RB'm phosphorylases YLR209C2.4.2.1PNPIstrong similarity GSNm+PIm <-> GNm+RIPmmnadg2 to purine-nucleoside phosphorylases YGL037C3.5.1.19PNCINicotinamidase NAMm<->NACm+NH3m mnadh YOR209C2.4.2.11NPTINAPRTase NACm+PRPPm->NAMNm+PPImmnptl 3.2.2.5 NADm -> NAMm + ADPRB3mmnadi Uptake Pathways Porphyrin and Chlorophyll Metabolism YDR232W2.3.1.37hem)5-Aminolevulinate SUCCOAm + GLYm ->
synthase ALAVm + COAm + C02m hem) YGL040C4.2.1.24HEM2Aminolewlinate dehydratase2 ALAV -> PBG hem2 YDL205C4.3.1.8HEM3Hydroxymethylbilane 4 PBG -> HMB +4 NH3 hem3 synthase YOR278W4.2.1.75HEM4Uroporphyrinogen-III HMB -> UPRG hem4 synthase YDR047W4.1.1.37HEM12UroporphyrinogendecnrboxylaseUPRGa4C02+CPP heml2 YDR044W1.3.3.3HEM13Coproporphyrinogenoxidase,aerobic02+CPPa2C02+PPHG heml3 YEROl4W1.3.3.4HEM14Protoporphyrinogenoxidase02+PPHGm->PPIXm heml4 YOR176W4.99.1.1HEM15Ferrochelatnse PPDCm->PTHm heml5 YGL245W6.1.1.17YGL245glutamyl-tRNAsynthetase,cytoplasmicGLU+ATP->GTRNA+AMP+PPIunrxnl0 W

YOL033W6.1.1.17MSEI GLUm+ATPm->GTRNAm+AMPm+PPImmsel YKR069W2.1.1.107met)uroporphyrin-BIC-methyltransferaseSAM+UPRG->SAH+PC2 met) Qutnone Biosynthesis YKL211C4.1.3.27trp3anthrnnilate synthaseCHOR a 4HBZ+PYR trp3 Component II and 3 indole-3- -phosphate (multifunctional enzyme) YER090W4.1.3.27trp2anthranilate synthaseCHOR -> 4HBZ + PYR trp2 Component I 2 YPR176C2.5.1:BET2geranylgeranyltransferasetypeIIbetasubunit4HBZ+NPPaN4HBZ+PPI
bet2 YJL031C2.5.1:BET4geranylgeranyltransferase type II alpha subunit YGL155W2.5.1:cdcA3geranylgeranyltransferase type I beta subunit YBR003W2.5.1:COQ1Hexaprenylpyrophosphatesynthetase,catalyzesthe4HBZ+NPP->N4HBZ+PPI coq) first step in coenzyme Q (ubiquinone) biosynthesis pathway YNR041C2.5.1:COQ2pam-hydroxybenzoate--polyprenyltransferase4HBZ+NPP->N4HBZ+PPI
coq2 YPL172C2.5.1:COX10protohemelXfnroesyltransferase,mitochondria)4HBZ+NPP->N4HBZ+PPI coxl0 precursor YDL090C2.5.1:ram)proteinfamesyltransferasebetasubunit4HBZ+NPP->N4HBZ+PPI ram) YKL019W2.5.1:RAM2protein famesyltransferase alpha subunit YBR002C2.5.1:RER2putativedehydrodolichyldiphospatesynthetase4HBZ+NPPaN4HBZ+PPI
rer2 YMR101C2.5.1:SRTIputativedehydrodolichyldiphospatesynthetase4HBZ+NPP->N4HBZ+PPI srtl YDR538W4.1.1:PADIOctaprenyl-hydroxybenzoatedecarboxylaseN4HBZ->C02+2NPPP
pad) -- 1.13.14.- 2-Octaprenylphenol 2NPPP+ 02 -> 2N6H ubib hydroxylase YPL266W2.1.1:DIMI 2N6H+SAMa2NPMP+SAH dim) - 1.14.13: 2-Octaprenyl-6-methoxyphenol2NPMPm + 02m a 2NPMBmubih hydroxylase YML110C2.1.1:COQS2-Octaprenyl-6-methoxy-1,4-benzuquinonemethylase2NPMBm+SAMma2NPMMBm+SAHmcoq5 YGR255C1.14.13:COQ6COQ6 monooxygenase 2NPMMBm + 02m a 2NMHMBmcoq6b YOL096C2.1.1.64COQ33-Dimethylubiquinone 2NMHMBm + SAMm -> ubig 3-methyltransferase QH2m + SAHm Memberane Transport Mitochondiral Membrane Transport Tlrejollowings manner:
diffuse llrrough !he inner milochondiral membrane in a non-carrier-mediated 02 <-> 02m mot C02 <-> C02m mco2 ETH <-> ETHm meth NH3 <-> NH3 m mnh3 MTHN <-> MTHNm mmthn THFm <a THF 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 a ACARm macar-CARm -> CAR mcar ACLAC <a ACLACm maclac ACTAC <a ACTACm mactc SLF-> SLFm+Hm mslf THRm <-> THR mihr AKAm a AKA maka YMR056c AAC1ADPIATPcarcierprotein(MCF)ADP+ATPm+PIaHm+ADPm+ATP+Phnaacl YBL030C pet9ADP/ATPcarcierprotein(MCF)ADP+ATPm+PI->Hm+ADPm+ATP+PImpet9 YBR085w AAC3ADP/ATP carcier proteinADP + ATPm + PI -> aac3 (MCF) Hm + ADPm + ATP
+ Phn YJR077C MIRIphosphatecarcier PI<aHm+PIm mina YER053C YER053similarity to C.elegansPI+ OHm <-> PIm mirld mitochondria) phosphate carcier C

YLR348C DICIdicnrboxylate carrierMAL + SUCCm <.> MALmdicl_1 + SUCC

YLR348C DICldicarboxylatecarrier MAL+PIm<aMALm+PI dicl YLR348C DICIdicarboxylatecarrier SUCC+PIm->SUCCm+PI dicl MALT + PIm <a MALTm mmlt + PI

YKL120W OACIMitochondria) oxaloacetateOA <-> OAm +Hm moab carcier YBR291C CTP1citrate transport CIT+MALm<->CITm+MAL ctpl protein 1 YBR291C CTP1citrate transport CIT+PEPm<aCTTm+PEP ctpl protein 2 YBR291C CTPIcitrate transport CIT+ICITm<->CITm+ICITctpl protein 3 B'PMAL <-> B'PMALm mpmalR

LAC <a LACm + Hm mlac pyrovate carcier PYR <-> PYRm+Hm pyrca glutamate carcier GLU <-> GLUm+Hm gca GLU + OHm a GLUm gcb YORI30C ORTIomithine carcier ORN + Hm <a ORNm ortl YOR100C CRC1 camitinecarrier CARm+ACARaCAR+ACARmcrcl OIVAL <-> OIVALm moival OMVAL <-> OMVALm momval YIL134W FLXI Protein involved in FAD + FMNm -> mfad transport of FAD FADm + FMN
from cytosol into the mitochondria) matrix RIBFLAV <-> RIBFLAVmmribo DTB <-> DTBm mdtb H3MCOA <-> H3MCOAmmmcoa MVL <a MVLm mmvl PA <a PAm mPa 4PPNTE <-> 4PPNTEmmppnt AD <-> ADm mad PRPP <_> pRPPm ~nP~PP

DHF <-> DHFm mdhf ~A <a QAm m9a OPP <_> pPPm mope SAM <-> SAMm msam SAH <a SAHm msah YJR095W SFC1 Mitochondrialmembranesuccinate-fumarateSUCC+FUMmaSUCCm+FUMsfcl transporter, member of the mitochondria) carrier family (MCF) of membrane transporters YPL134C ODCI 2-oxodicarboylatetransporterAKGm+OXA<->AKG+OXAmodcl YOR222W ODC2 2-oxodicarboylate AKGm + OXA <-> odc2 transporter AKG + OXAm MaJate Aspartale Shuttle Included elsewhere Glycerol phasphale shullle T3P2m-> T3P2 mt3p GL3P a GL3Pm mgl3p Plasma nsport Membrane Tra Carbohydrates YHR092c HXT4 moderate- to low-affinityGLCxt -> GLC hxt4 glucose transporter YLR081w GAL2 galactose (and glucose)GLCxt a GLC gal2 permease 3 YOLI56w HXTI low affinity glucose GLCxt -> GLC hxtl I transport protein l YDR536W stll Protein member of GLCxt a GLC stll_1 the hexose transporter Family YHR094c hxtl High-affinity hexose GLCxt a GLC hxtl_1 (glucose) transporter YOL156w HXTI1Glucosepennease GLCxt->GLC hxtll_1 YEL069c HXT13high-affinity hexose GLCxt-> GLC hxtl3_I
transporter YDL245c HXT15Hexose transporter GLCxt-> GLC hxtl5_1 YJR158w HXT16hexose permease GLCxt-> GLC hxtl6_1 YFLOIIw HXTIOhigh-affinity hexose GLCxt-> GLC hxtl0_I
transporter YNR072w HXT17Putative hexose transporterGLCxt-> GLC hxtl7_1 YMR01 HXT2 high affinity hexose GLCXI -> GLC hxt2_1 lw transporter-2 YHR092c hxt4 High-affinity glucoseGLCxt -> GLC hxt4_1 transporter YDR345c hxt3 Low-affinity glucose GLCxt a GLC hxt3_1 transporter YHR096c HXTS 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 YJL219w HXT9 hexose pertnease GLCxt -> GLC hxt9_1 YLR081w gal2 galactosepermease GLACxt+HEXTaGLAC gal2_1 YFLOIIw HXT10high-affinity hexose GLACxt+ HEXT-> hxtl0_4 transporter GLAC

YOL156w HXTIIGlucosepemrease GLACxt+HEXTaGLAC hxtll_4 YNL318c HXT14Member of the hexose GLACxt + HEXT-> hxtl4 transporter family GLAC

YJL219w HXT9 hexose permease GLACxt+ HEXT-> hxt9 YDR536W stl Protein member of GLACxt+ HEXT -> stll_4 l the hexose VansporterGLAC
family YFLOSSw AGP3 Amino acid permease GLUxt+HEXT <-> agp3 for serine, aspartate,GLU 3 and glutamate YDR536W stl Protein member of GLUxt + HEXT <-> stll_2 l the hexose transporterGLU
family YKR039W gap) General amino acid GLUxt+HEXT <-> gap8 permease GLU

YCL025C AGPI Amino acid permease GLUxt + NEXT <-> gap24 For most neutral GLU
amino acids YPL265W DIPS Dicarboxylic amino GLUxt + HEXT <a dipl0 acid permease GLU

YDR536W stf Protein member of GLUxt + HEXT <-> stll_3 l the hexose transporterGLU
family YHR094c hxtl High-affinity hexose FRUxt+ HEXT a hxtl (glucose) transporterFRU 2 YFLO1 HXT10high-nffinity hexose FRUxt+HEXT a FRU hxtl0_2 Iw transporter YOLI56w HXTI1Glucosepecmease FRUxt+HEXT->FRU hxtll -YEL069c HXT13high-affinity hexose FRUxt + HEXT a _2 transporter FRU hxtl3 YDL245c HXTISHexosetransporter FRUxt+HEXT->FRU hxtl5_2 YJRI58w HXT16hexosepermease FRUxt+HEXT->FRU hxtl6_2 YNR072w HXTI7PutativehexosetransporterFRUxt+HEXT->FRU hxtl7_2 YMROIIw HXT2 high affinityhexosetransporter-2FRUxt+HEXT->FRU hxt2_2 YDR345c hxt3 Low-affinity glucose FRUxt+HEXT->FRU hxt3_2 transporter YHR092c hxt4 High-affinity glucoseFRUxt+HEXT a FRU hxt4 transporter 2 YHR096c HXTS hexose transporter FRUxt+HEXT a FRU hxt5 YDR343c HXT6 Hexosetransporter FRUxt+HEXTaFRU hxt6 YDR342c HXT7 Hexosetransporter FRUxt+HEXT->FRU hxt7-2 YJL214w HXT8 hexose permease FRUxt+ HEXT a hxt8 _ YJL219wHXT9 hexosepermease FRUxt+HEXT->FRU hxt9 YHR094chxtl High-affinity hexose MANxt+HEXT a MAN hxtl (glucose) transporter 5 YFLOI HXT10 high-affinity hexose MANxt+HEXT a MAN hxtl0 lw transporter 3 YOL156wHXTI1 Glucosepertnease MANxt+HEXTaMAN hxtll_3 YEL069cHXT13 high-affinity hexose MANxt+HEXT-> MAN hxtl3_3 transporter YDL245cHXT15 Hexose transporter MANxt+HEXT a MAN hxtl5_3 YJR158wHXTI6 hexose permease MANxt+HEXT a MAN hxtl6 YNR072wHXT17 Putntive hexose transporterMANxt+HEXT a MAN hxtl7_3 YMR011wHXT2 high affinityhexosetransporter-2MANxt+HEXT->MAN hxt2 YDR345chxt3 Low-affinity glucose MANxt+HEXT a MAN hxt3_3 transporter YHR092chxt4 High-affinity glucoseMANxt+HEXT-> MAN hxt4_3 transporter YHR096cHXTS hexose transporter MANxt+HEXT a MAN hxt5_3 YDR343cHXT6 Hexosetransporter MANxt+HEXT->MAN hxt6 YDR342cHXT7 Hexosetransporter MANxt+HEXT->MAN hxt7_3 YJL214wHXT8 hexosepermease MANxt+HEXT->MAN hxt8_6 YJL219wHXT9 hexosepermease MANxt+HEXT->MAN hxt9~3 YDR497cITRI myo-inositoltransporterMlxt+HEXT->MI itrl YOL103wITR2 myo-inositoltransporterMIxt+HEXT->MI itr2 Maltase permease MLTxt+ NEXT -> mltup MLT

YILl62W3.2.1.26invertase (sucrose SUCxt-> GLCxt+ suc2 SUC2 hydrolysing enzyme) FRUxt sucrose SUCxt + HEXT -> sucup SUC

YBR298cMAL31 Dicarboxylates MALxt+HEXT <-> ma131 MAL

a-KetoglutaratelmalateMALxt+AKG <-> alcmup translocator MAL+AKGxt a-methylglucoside AMGxt <-> AMG amgup Sorbose SORxt <a SOR sorup Arabinose (low affinity)ARABxt <-> ARAB arbup Fucose FUCxt+ HEXT <-> fucup FUC

GLTLxt + HEXT gltlupb -> GLTL

Glucitol GLTxt + HEXT -> gltup GLT

Glucosamine GLAMxt+ HEXT <-> gaup GLAM

YLL043WFPS1 Glycerol GLxt<->GL glup YKL217WJENI Lactate transport LACxt+HEXT<->LAC lacupl Mannitol MNTxt+HEXT a MNT mntup Melibiose MELIxt+ HEXT a melup N-Acetylglucosamine NAGxt+ NEXT -> nagup NAG

Rhamnose RMNxt + HEXT -> rmnup RMN

Ribose RIBxt + HEXT -> ribup RIB

Trehalose TRExt + HEXT -> treup-1 THE

TRExt->AATRE6P ireup XYLxt <-> XYL xylup Amlno Acids YKR039Wgapl GenemlaininoacidpermeaseALAxt+HEXT<->ALA gapl YPL265WDIPS DicarboxylicaminoacidpermeaseALAxt+HEXT<->ALA dips YCL025CAGPl Amino acid permease ALAxt + HEXT <a gap25 for most neutral ALA
amino acids YOL020WTAT2 Tryptophanpermease ALAxt+HEXT<aALA tats YOR348CPUT4 Proline permease ALAxt+ HEXT <a put4 ALA

YKR039Wgapl General amino acid ARGxt +HEXT <a gap2 permease ARG

YEL063Ccanl Permease for basic ARGxt + HEXT <-> cenl_1 amino acids ARG

YNL270CALP 1 Protein with strong ARGxt + HEXT <-> nlpl similarity to permeasesARG

YKR039Wgapl GenemlaminoacidpermeaseASNxt+HEXT<->pSN gap3 YCL025CAGP 1 Amino acid permease ASNxt+ HEXT <a gap21 for most neutral ASN
amino acids YDR508CGNPl Glutamine permease ASNxt+HEXT <a gnp2 (high affinity) ASN

YPL265DIPS Dicarboxylic amino ASNxt+HEXT <-> dip6 W acid permease ASN

YFLOSSWAGP3 Amino acid permease ASPxt+ HEXT <a agp3 for serine, aspartate,pSP 2 and -glutamate YKR039Wgap 1 Genernl amino acid ASPxt+ HEXT <-> gap4 permease pSP

YPL265WDE'S Dicarboxylic amino ASPxt+ HEXT <-> dip?
acid permease ASP

YKR039Wgapl GenemlaminoacidpermeaseCYSxt+HEXT<->CYS gaps YDR508CGNP 1 Glutamine percnease CYSxt + HEXT <a gnp3 (high affinity) CYS

YBR068CBAP2 Branched chain amino CYSxt + HEXT <-> bap2 acid pennease CYS 1 YDR046CBAP3 Branched chain amino CYSxt+HEXT <-> bap3 acid permease CYS 1 YBR069CVAP l Amino acid permease CYSxt+HEXT <-> vap7 CYS

YOL020WTAT2 Tryptophan permease CYSxt+ HEXT <-> tat?
CYS

YKR039Wgapl General amino acid GLYxt+ HEXT <-> gap6 permease GLY

YOL020WTAT2 Tryptophanpermease GLYxt+HEXT<aGLY mt6 YPL265WDIPS Dicarboxylic amino GLYxt+HEXT <-> dip8 acid permease GLY

YOR348CPUT4 Proline permease GLYxt + HEXT <-> puts GLY

YKR039Wgapl General amino acidpermeaseGLNxt+HEXT<->GLN gap?

YCL025CAGP1 Amino acid permease GLNxt+HEXT <-> gap22 for most neutral GLN
amino acids YDR508CGNP 1 Glutamine permease GLNxt + HEXT <-> gnp (high affinity) GLN 1 YPL265WDIPS Dicarboxylic amino GLNxt+HEXT <a dip9 acid permease GLN

YGRI91WHE'I Histidinepermease HISxt+HEXT<aHIS hipl YKR039Wgapl GenemlaminoacidpermeaseHISxt+HEXT<aHIS gap9 YCL025CAGPI Amino acid permease HISxt+ HEXT <-> gap23 for most neutral HIS
amino acids YBR069CVAPI Amino acid permease HISxt+HEXT <a vap6 HIS

YBR069CTATI Amino acid permease ILExt+ HEXT <-> tall that transports valine,ILE 2 leucine, -isleucine, tyrosine, tryptophan, and ihreonine YKR039Wgapl General amino acid ILExt+HEXT<a ILE gapl0 permease YCL025CAGP Amino acid permease ILExt+ HEXT <-> gap32 1 for most neutral ILE
amino acids YBR068CBAP2 Branched chain amino ILExt * HEXT <-> bap2-L
acid pettnease ILE

YDR04GCBAP3 Branched chain amino ILExt+ HEXT <a bap3 acid permease ILE 2 -YBR069CVAP Amino acid permease ILExt+ HEXT <_> vap3 YBR069CTATI Amino acid permease LEUxt+HEXT <a tall that transports valine,LEU 3 leucine, -isleucine, tyrosine, tryptophan, and threonine YKR039Wgapl GenemlaminoacidpermeaseLEUxt+HEXT<->LEU gapll YCL025CAGP Amino acid permease LEUxt+ HEXT <a gap33 1 for most neutral LEU
amino acids YBR068CBAP2 Branched chain amino LEUxt+HEXT <-> bap2 acid pertnease LEU 3 YDR046CBAP3 Branched chain amino LEUxt+ HEXT <-> bap3 acid permease LEU 3 YBR069CVAP1 Amino acidpermease LEUxt+HEXT<aLEU vap4 YDRSOSCGNP Glutamine permease LEUxt + HEXT <a gnP7 I (high affinity) LEU

YKR039Wgapl GenernlaminoacidpermeaseMETxt+HEXT<->MET gapl3 YCL025CAGP Amino acid permease METxt + HEXT <a gap26 1 for most neutral MET
amino acids YDR508CGNP Glutamine permease METxt+ NEXT <-> gnp4 1 (high affinity) MET

YBR068CBAP2 Branched chain amino METxt+ HEXT <a bap2 acid permease MET 4 YDR046CBAP3 Branched chain amino METxt+ NEXT <a bap3 acid pertnease MET 4 YGRO55 MUP High-affinity methionineMETxt+ HEXT <a mupl W 1 pertnease MET

YHL036WMUP3 Low-affinity methionineMETxt+ NEXT <-> mup3 permease MET

YKR039Wgapl General amino acid PHExt+HEXT <_> gapl4 pertnease pHEN

YCL025CAGPI Amino acid permease PHExt+HEXT <a gap29 For most neutral PHEN
amino acids YOL020WTAT2 Tryptophan permease PHExt+HEXT <-> tat4 PHEN

YBR068CBAP2 Branched chain amino PHExt+HEXT <-> bap2 acid pecmease PHEN 5 YDR046CBAP3 Branched chain amino PHExt+HEXT <-> bap3 acid permease PHEN 5 YKR039Wgapl General amino acid PROxt+HEXT <-> gapl5 permease PRO

YOR348CPUT4 Proline permease PROxt+ HEXT <-> put6 PRO

YBR069CTATI Amino acid permease TRPxt+ HEXT <-> tall that transports valine,TRP 6 leucine, -isleucine, tyrosine, tryptophan, and threonine YKR039 gapl General amino acid TRPxt+ HEXT <a gapl8 W permease TRP

YBR069CVAPI Amino acid permease TRPxt+HEXT <-> vap2 TRP

YOL020WTAT2 Tryptophan permease TRPxt+ NEXT <-> tat3 TRP

YBR068CBAP2 Branched chain amino TRPxt+ HEXT <-> bap2 acid petmease TRP 6 YDR046CBAP3 Branched chain amino TRPxt+HEXT <a bap3 acid permease TRP 6 YBR069CTATI Amino acid permease TYRxt+HEXT <-> tall that transports valine,TYR 7 leucine, -isleucine, tyrosine, tryptophan, and threonine YKR039Wgapl General amino acid TYRxt+HEXT <-> gapl9 permease TYR

YCL025CAGPI Amino acid permease TYRxt+ NEXT <-> gap28 for most neutral TYR
amino acids YBR068CBAP2 Branched chain amino TYRxt + HEXT <a bap2 acid permease TYR 7 YBR069CVAPI Amino acidpermease TYRxt+HEXT<aTYR vapl YOL020WTAT2 Tryptophan permease TYRxt+HEXT <_> tat2 ZyR

YDR046CBAP3 Branched chain amino TYRxt+ HEXT <a bap3-7 acid permease TYR

YKR039Wgapl General amino acidpermeaseVALxt+HEXT<->VAL gap20 YCL025CAGPI Amino acid permease VALxt+HEXT <-> gap31 for most neutral VAL
amino acids YDR046CBAP3 Branched chain amino VALxt+HEXT <-> bap3 acid permease VAL 8 YBR069CVAPI Amino acid permease VALxt+HEXT <-> vap5 VAL

YBR068CBAP2 Branched chain amino VALxt+HEXT <-> bap2 acid permease VAL 8 YFLO55 AGP3 Amino acid permease SERxt+ HEXT <-> agp3 W for serine, aspartate,SER I
and glutamate YCL025CAGP Amino acid permease SERxt+ HEXT <-> gap27 1 for most neutral SER
amino acids YDR508CGNP Glutamine permease SERxt+ HEXT <-> gnp5 1 (high affinity) SER

YKR039Wgapl General amino acid SERxt+HEXT <-> gapl6 permease SER

YPL265WDIPS DicarboxylicaminoacidpermeaseSERxt+HEXT<->SER dipll YBR069CTATI Amino acid permease THRxt+HEXT <_> tall that transports valine,THR 1 leucine, -isleucine, tyrosine, tryptophan, and threonine YCL025CAGP Amino acid permease THRxt+ HEXT <a gap30 1 for most neutral THR
amino acids YKR039Wgapl General amino acidpermeaseTHRxt+HEXT<->THR gapl7 YDR508CGNP1 Glutaminepermease(highaffinity)THRxt+HEXT<aTHR gnp6 YNL268WLYPI Lysine specific permeaseLYSxt+HEXT <-> lypl (high affinity) LYS

YKR039Wgapl General amino acidpermeaseLYSxt+HEXT<->LYS gapl2 YLL061WMMPI Highaf6nityS-methylmethioninepermeaseMMETxt+HEXTaMMET mmpl YPL274WSAM3 High affinity S-adenosylmethionineSAMxt+HEXT a SAM sam3 permease YOR348CPUT4 Prolinepermease GABAxt+HEXT->GABAput?

YDL2IOWuga4 Amino acidpermeasewithhighspecificityforGABAGABAxt+HEXT->GABAuga4 YBR132CAGP2 Plasma membrane camitineCARxt <-> CAR agp2 transporter YGL077CHNMI Cholinepermease CHOxt+HEXT->MET hnml YNR056CBIOS transmembrnne regulatorBIOxt+ NEXT a bio5a of KAPA/DAPA transportBIO

YDL210Wuga4 Amino acid permease ALAVxt+ HEXT -> uga5 with high speciFcity ALAV
for GABA

YKR039Wgapl Genernl amino acid ORNxt+HEXT <-> gaplb pertnease ORN

YEL063Ccanl Permease for basic ORNxt+ HEXT <-> canlb amino acids ORN

Putrescine PTRSCxt + HEXT ptmp a PTRSC

Spermidine & putrescineSPRMDxt + HEXT sprup -> SPRMD 1 YKR093WPTR2 Dipeptide DIPEPxt+HEXT->DIPEPptr2 YKR093WPTR2 Oligopeptide OPEPxt+HEXTaOPEP ptr3 YKR093WPTR2 Peptide PEPTxt+HEXT->PEPTpir4 YBR021W FUR4Uracil URAxt+HEXT->URA uraupl NicotinamidemononucleotidetransporterNMNxt+HEXTaNMN nmnup YER056C FCY2CytosinepurinepermeaseCYTSxt+HEXT->CYTSfcy2-1 YER056C FCY2Adenine ADxt+HEXT->AD fcy2 YER056C FCY2Guanine GNxt+HEXT<->GN fcy2 YER060W FCY21CytosinepurinepermeaseCYTSxt+HEXTaCYTSfcy21_1 YER060W FCY21Adenine ADxt+HEXTaAD fc~21 YER060W FCY21Guanine GNxt+HEXT<aGN fcy21 YER060W-A FCY22Cytosine purine permenseCYTSxt+HEXT a fcy22-1 CYTS

YER060W-A FCY22Adenine ADxt+HEXT->AD fcy22 YER060W-A FCY22Guanine GNxt+HEXT<aGN fcy22 YGLI86C YGL186CytosinepurinepermeaseCYTSxt+HEXTaCYTScytupl C

YGL186C YGLI86Adenine ADxt+HEXT->AD adupl C

YGL186C YGL186Guanine GNxt+HEXT<->GN gnup C

G-system ADNxt + HEXT ncgup a pDN 1 G-system GSNxt + HEXT ncgup3 -> GSN

YBL042C FUIIUridinepermease,G-systemURIxt+HEXT->URI uriup G-system CYTDxt + HEXT ncgup4 a CYTD

G-system (transports INSxt+ HEXT a ncgup5 all nucleosides) INS

G-system XTSINExt + NEXT ncgup6 -> XTSINE

G-system DTxt+ HEXT a ncgup7 DT

G-system DINxt + HEXT ncgup8 -> DIN

G-system DGxt + HEXT -> ncgup9 DG

G-system DAxt + HEXT -> ncgup G-system DCxt + HEXT -> ncgup DC l l G-system DUxt + HEXT -> ncgup Gsystem ADNxt + HEXT nccup -> ADN 1 YBL042C FUIIUridinepermease,C-systemURIXL+HEXT->URI nccup2 C-system CYTDxt + HEXT nccup3 -> CYTD

C-system DTxt + HEXT -> nccup4 DT

Gsystem DAxt+ HEXT a nccup5 DA

C-system DCxt+HEXT a DC nccup6 C-system DUxt + NEXT -> nccup7 DU

Nucleosides and deoxynucleosideADNxt+ HEXT a neap Nucleosides and deoxynucleosideGSNxt+HEXT a ncup2 GSN

YBL042C FUIIUridinepermease,NucleosidesanddeoxynucleosideURIxt+HEXT->URI
ncup3 Nucleosides and deoxynucleosideCYTDxt+ NEXT ncup4 -> CYTD

Nucleosides and deoxynucleosideINSxt+HEXT a ncup5 INS

Nucleosides and deoxynucleosideDTxt + HEXT -> ncup7 DT

Nucleosides and deoxynucleosideDINxt + NEXT ncup8 -> DIN

Nucleosides and deoxynucleosideDGxt+HEXT-> DG ncup9 Nucleosides and deoxynucleosideDAxt+ HEXT -> neap Nucleosides and deoxynucleosideDCxt+HEXT a DC ncupl l Nucleosides and deoxynucleosideDUxt + HEXT -> neap Hypoxanthine HYXNxt+ HEXT hyxnup <-> HYXN

Xanthine XANxt <-> XAN xanup Metabolic By-Products YCR032W BPHIProbable acetic acid ACxt+ HEXT <a acup export pump, Acetate AC
transport Formate transport FORxt <-> FOR forup Ethanol transport ETHxt <-> ETH ethup Succinate transport SUCCxt+ HEXT succup <a SUCC

YKL217W JENIPyruvatelactateprotonsymportPYRxt+HEXT->PYR jenl-1 Other Compounds YHL016C dur3Urea active transportUREAxt+2 HEXT dur3 <-> UREA

YGR121C MEPIAmmonia transport NH3xt<->NH3 mepl YNL142W MEP2Ammonia transport, NH3xt <-> NH3 mep2 low capacity high affinity YPR138C MEP3Ammonia transport, NH3xt <-> NH3 mep3 high capacity low affinity YJLI29C trktPotassium transporterKxt+ HEXT <a trkl of the plasma membrane,K
high affinity, member of the potassium transporter (TRK) family of membrane transporters YBR294W SULISulfate permease SLFxt-> SLF Bull YLR092W SUL2Sulfate permease SLFxt-> SLF sul2 YGRI25W YGRI25Sulfatepermease SLFxt->SLF sulup W

YMLI23C pho84inorganic phosphate pho84 transporter, transmembrane protein Plxt + NEXT
<-> pI

Citrate CITxt + HEXT citup <a CIT

Dicarboxylates FUMxt+ HEXT <-> fumup FUM

Fatty acid transport C140xt a CI40 faupl Fatty acid transport C160xt-> C160 faup2 Fatty acid transport C161xt a 0161 faup3 Fatty acid transport C180xt-> CI80 faup4 Fatty acid transport C 181 xt -> CI faup5 a-Ketoglutarate AKGxt + HEXT <-> akgup AKG

YLRI38WNHAI PulativeNa+/I-I+antiporterNAxt<->NA+HEXT nhal YCR028CFEN2 Pantothenate PNTOxt+HEXT<->PNTOfen2 ATP drain flux for ATP -> ADP + PI atpmt constant maintanence requirements YCR024c-aPMP1 H+-ATPasesubunit,plasmamembraneATPapDP+PI+HEXT pmpl YEL017c-aPMP2 H+-ATPase subunit, ATP a ADP + PI pmp2 plasma membrane + HEXT

YGLOOScPMAI H+-transportingP-typeATPase,majorisoform,plasmaATP->ADP+PI+HEXT
pmal membrane YPL036wPMA2 H+-transporting P-typeATP -> ADP + PI pma2 ATPase, minor isoform,+ HEXT
plasma membrane Glyceraldehyde transportGLALxt <a GLAL glaltx Acetaldehyde transportACALxt <-> ACAL acaltx YLR237WTHI7 Thiamine transport THMxt+HEXT->THIAMINthml protein YOR071CYOR071Probable low affinityTHMxt+ HEXT -> ihm2 thiamine transporter THIAMIN

C

YOR192CYORI92Probable low affinityTHMxt+ HEXT a thm3 thiamine transporter THIAMIN

C

Yflt028Wdal4 ATNxt a pTN dal4 YJRl52Wdal5 ATTxt->ATT dal5 MTHNxt <-> MTHN mthup PAPxt <-> PAP PaPx DTTPxt <a DTTP dttpx THYxt <a THY + ihyx HEXT

GA6Pxt <-> GA6P ga6pup YGR065CVHTI H+/biotin symporter BTxt+ HEXT <-> btup and member of the BT
allantoate permease family of the major facilitator superfamily AONAxt+ NEXT <-> kapaup AONA

DANNAxt+ HEXT dapaup <a DANNA

OGTxt -> OGT ogtup SPRMxt a SPRM sprmup PIMExt a pIME pimeup Oxygen transport 02xt <-> 02 o2tx Carbon dioxide transportC02xt <-> C02 co2tx YORO11WAUS1 ERGOSTxt<aERGOST ergup YOROI AUS Putative sterol transporterZYMSTxt <a ZYMST zymup RFLAVxt + HEXT rflup -> RIBFLAV

[0055] Standard chemical names for the acronyms used to identify the reactants in the reactions of Table 2 are provided in Table 3.

Abbrevtatton Metabolite 13GLUCAN 1,3-beta-D-Glucan 13PDG I3-Phospho-D-2N6H 2-Nonaprenyl-6-hydroxyphenol 2NMHMB ~ 3-Demethylubiqui 2NMHMBm 2NPMBm 12-Nonaprenyl-G-methoxy-1,4-benzoquinoneM
2NPMMBm 2-Nonaprenyl-3-2NPMP 2-Nonaprenyl-methoxyphenc 2NPMPm 2-Nonapreny4 methoxyphenc M

Nonaprenylph of 2PG 2-Phospho-D-glycerate 3DDAH7P 2-Dehydro-3-3PG 3-Phospho-D-glycerate 3PSER 3-Phosphoserine 3PSME 5-O-(I-Carboxyvinyl)-Hydroxybenzoat a 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

4PPNT0 D-4'-Phosphopantoth enate SMTA 5'-Methylthioaden osine 6DGLC D-Gal alpha I->GD-Glucose A6RP 5-Amino-6-ribitylamino-2,4 ( t H, 3 H)-A6RPSP 5-Amtno-6-(5'-phosphoribosyl mino)umcil A6RPSP2 5-Amino-G-(5'-phosphoribityle mino)umcil AACCOA Acetoacetyl-CoA

AACP Acyl-[acyl-carrier-protein]

AATREGP alpha,alpha'-Trehalose phosphate ABUTm 2-Aceto-2-hydroxy butyrateM

pC Acetate ACACP Acyl-[acyl-carrier protein]

ACACPm Acyl-[acyl-ACAL
ACALm ACAR
ACARm ACCOA
ACCOAm ACLAC
ACLACm Acm ACNL
ACOA
ACP
ACPm ACTAC
ACTACm ACYBUT
AD
ADCHOR
Adm ADN
ADNm ADP
ADPm ADPRIB
ADPRIBm AHHMD
AHHMP
AHM
AHMP
pHMPP
AHTD
hydroxymethyl-methylpyrimidin hydroxymethylp yrimidine triphosphnte AICAR 1-(5'-Phosphoribosyl) -5-amino-4-imidazolecarbox AIR
AICA
AKAm AKG
AKGm AKP
AKPm ALA
ALAGLY
ALAm ALAV
ALAVm ALTRNA

AMA
AMASA
AMG
AMP
AMPm AMUCO
AN
AONA
APEP
APROA
APROP
APRUT
APS
ARAB
ARABLAC
ARG
ARGSUCC
ASER
ASN
ASP
ASPERMD
ASPm ASPRM
ASPSA
ASPTRNA
ASPTRNAm ASUC

ATN
ATP
ATPm ATRNA

ATRP P 1,P4-Bis(5'-n denosyl) t etraphosphate A1"f' Allantoate bALA b eta-Alanine BASP 4 -Phospho-L-a spartate bDG6P b eta-D-Glucose 6-phosphate bDGLC beta-D-Glucose BI0 Biotin BT Biotin CIOOACP Decanoyl-[acp]

C120ACP Dodecanoyl-[acyl-carrier protein]

C120ACPm Dodecanoyl-[acyl-carrier protein]M

C140 Myristicacid 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 Hexndecanoyl-[acp]

C160ACPm Hexadecanoyl-[acp]M

C161 1-Hexadecene C161ACP Palmitoyl-[acyl-carrier protein]

C161ACPm Palmitoyl-[acyl-carrier protein]M

C16A C16_aldehydes CI80 Stearate C180ACP Stearoyl-[acyl-carrier protein]

C180ACPm Stearoyl-[acyl-carrier protein]M

C 181 1-Octadecene C181ACP Oleoyl-[acyl-carrier protein]

CI8IACPm Oleoyl-[acyl-carrier protein]M

C182ACP Linolenoyl-[acyl-carrier protein) CI82ACPm Linolenoyl-[acyl-carrier prateinJM

CAASP N-Carbamoyl-L-aspartate CAIR 1-(5-Phospho-CALH
cAMP
CAP
CAR
CARm CBHCAP
CBHCAPm cCMP
cdAMP
CDP
CDPCHO
CDPDG
CDPDGm CDPETN
CERZ

CGLY
cGMP
CHCOA
CHIT
CHITO
CHO
CHOR
cIMP
CTT
CTTm CTTR
CLm CMP
CMPm CMUSA

C02m COA
COAm CPADSP
CPP
CTP
CTPm CYS
CYTD
CYTS

DA
DADP
DAGLY
DAMP
dAMP
DANNA
DAPRP

DATP

DC
DCDP
DCMP
DCTP
DFUC
DG
DGDP
DGMP
DGPP
DGTP
DHF
DHFm DHMVAm DHP
DHPP
DHPT
DHSIC
DHSP
DHSPH
DHVALm DIMGP
DIN
DIPEP

DLIPOm DMPP
DMZYMST
DOL
DOLMANP
DOLP Dolichyl phosphate DOLPP Dehydrodolichol diphosphate DOROA (S)-Dihydroorotate DPCOA Dephospho-CoA

DPCOAm Dephospho-CoAM

DPTH 2-[3-Carboxy-3-DQT

DRSP
DRIB
DSAM
DT
DTB
DTBm DTDP
DTMP
DTP
DTTP
DU
DUDP
DUMP
DUTP

EPM
EPST

ERGOST
ERTEOL
ERTROL
ETH
ETHm ETHM

FAD
FADH2m FADm FALD
FDP
FERIm FEROm FEST
FGAM
FGAR
FGT
FKYN
FMN
FMNm FMRNAm Formylmethiony FOR Formnie FORm FotlnateM

FPP trans,trans-Farnesyl diphosphnte FRU D-Fructose Formyltetrahydr ofofate FTHFm 10-Formyltetrahydr ofolateM

Fumarylacetoac elate FUC beta-D-Fucose FUM Fumarate FUMm FumarateM

G1P D-Glucose phosphate G6P alpha-D-Glucose phosphate GA1P D-Glucosamine 1-phosphate GA6P D-Glucosamine 6-phosphate Aminobutanoate Aminobutyralde hyde GABALm4-Aminobutyralde hydeM

GABAm 4-Aminobutanoate M

GAL1P D-Galactose phosphate GAR 5'-Phosphoribosylg lycinamide GBAD 4-Guanidino-butanamide GBAT 4-Guanidino-butanoate GC gamma-L-Glutamyl-L-cysteine GDP GDP

GDPm GDPM

GDPMANGDPmannose GGL Galactosylglycer of GL Glycerol GL3P sn-Glycerol3-phosphate GL3Pm sn-Glycerol3-phosphateM

GLAC D-Galactose GLACL 1-alpha-D-Galactosyl-myo-inositol GLAL Glycolaldehyde GLAM Glucosamine GLC alpha-D-Glucose GLCN Gluconate GLN L-Glutamine GLP Glycylpeptide GLT L-Glucitol GLU L-Glutamate GLUGSALL-Glutamate semialdehyde GLUGSALmL-Glutamate semialdehydeM

GLUm GlutamateM

GLUP alpha-D-Glutamyl GLX
GLY
GLYCOGEN
GLYm GLYN
GMP
GN
GNm GPP
GSN
GSNm GTP
GTPm GTRNA
GTRNAm GTRP

H2S Hydrogen sulfide H2S03 Sulfite H3MCOA(S)-3-Hydroxy-methylglutaryl-CoA

H3MCOAm(S)-3-Hydroxy-methylglutaryl-CoAM

HACNm But-1-ene-1,2,4-tricarboxylateM

HACOA (3S)-3-Hydroxyacyl-CoA

Hydroxyanthran ilate HBA 4-Hydroxy-benayl alcohol Hydroxybutane-1,2,4-tricarboxylate HCITm 2-Hydroxybutane-1,2,4-tricarboxylateM

HCYS Homocysteine HEXT H+EXT

HHTRNAL-Histidyl-tRNA(His) HIB (S)-3-Hydroxyisobuty rate HIBCOA(S)-3-Hydroxyisobuty ryl-CoA

HICITmHomoisocitrate M

HIS L-Histidine HISOL L-Histidinol HISOLPL-Histidinol phosphate Hydroxykynure nine Hm H+M

HMB Hydroxymethyl bilane HOMOGENHomogentisate HPRO traps-4-HSEIt HTRNA
HYXAN
IAC
IAD
IBCOA
ICIT
ICITm IDP
IDPm IGP
IGST
IIMZYMST
IIZYMST
ILE
ILEm IMACP
IMP
IMZYMST
INAC
INS
IPC
IPPMAL
IPPMALm IPPP
ISUCC
ITCCOAm ITCm ITP
ITPm IVCOA
IZYMST
K
KYN
LAC
LACALm LACm LCCA
LEU
LEUm LGT
LGTm glucosaminyl-1,6-beta-D-glucosamine 1,4'-LIPOm LIPX
LLACm LLCT
LLTRNA
LLTRNAm LNST
LTRNA
LTRNAm LYS
LYSm MAACOA
MACAC
MACOA
MAL
MALACP
MALACPm MALCOA
MALm MALT
MALTm MAN
MAN I P

MANNAN
MBCOA
MCCOA
MCRCOA
MDAP
MELI
MELT
MET
METH
METHF
METHFm METTHF
METTHFm 5,10-Methylenetetrah ydrofolateM

Methylglutacon MHIS
MHVCOA
MI myo-lnosnot MI1P I1L-myo-Inositol 1-phosphate i nositol-P-c eramide MIPC Mannose-i nositol-P-ceramide MK Menaquinone MLT Maltose MMCOA Methylmalonyl-CoA

MMET S-Methylmethioni ne MMS (S)-Methylmalonate semialdehyde MNT D-Mannitol MNT6P D-Mannitoll-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 N4HBZ 3-Nonaprenyl-4-hydroxybenzoat a 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 NAGA1P 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-NAMNm NAORNm NH3m NPP
NPPm NPRAN

02m OA
OACOA
OAHSER
OAm OBUT
OBUTm OFP
OGT
OHB
OHm OICAP
OICAPm 3-Carboxy-4-methyl-2-oxopentanoateM
OIVAL (R)-2-Oxoisovalerate OIVALm (R)-2-OMP Orotidine 5'-phosphate OMVAL 3-Methyl-2-oxobutanoate OMVALm 3-Methyl-2-oxobutanoateM

OPEP Oligopeptide ORN L-Ornithine ORNm L-OmithineM

OROA Orotate OSLHSER O-Succinyl-L-homoserine OSUC Oxalosuccinate OSUCm Oxalosuccinate M

OTHIO Oxidized thioredoxin OTHIOm Oxidized thioredoxinM

OXA Oxaloglutarate OXAm Oxnloglutarate M

PSC (S)-1-Pytroline-5-carboxylate PSCm (S)-1-Pyrroline-5-carboxylateM

PSP Pyridoxine phosphate PA Phosphatidate Aminobenzoate PAC Phenylacetic PAD
PALCOA
PAm PANT
PANTm PAP
PAPS
PBG
PC

PCHO
PDLA
PDLASP
PDME
PE
PEm PEP
PEPD
PEPm PEPT
PETHM
PGm PGPm PHC
PHE
PHEN
PHP
PHPYR
PHSER
PHSP
PHT
PI
PIm PIME
PINS

PINSP
PL
PLSP
PMME
PMVL
PNTO

PPHG
PPHGm PPI
PPIm PPUCm PPMAL
PPMVL
PRAM
PRBAMP
PRBATP
PRFICA
PRFP
PRLP
PRO
PROm PROPCOA
PRPP
PRPPm PS
PSm PSPH
PTHm PTRC
PTRSC
PURISP
PYR
PYRDX
PYRm Q
QA
QAm QH2m Qm RIP
RSP
RADP

RAF
RFP
RGT
RGTm RIB
RIBFLAVm RIBOFLAV
RIPm RLSP
RMN
RTHIO
RTHIOm S

SACP
SAH
SAHm SAICAR
SAM
SAMm SAMOB
SAPm SER
SERm SLF
SLFm SME
SMESP
SOR

SOT
SPH
SPMD
SPRM
SPRMD
SQL
SUC
SUCC
SUCCm SUCCOAm SUCCSAL

amino-2,6-dihydroxypyrimi dine T3P2 IGlycerone phosphate T3P2m Glycerone TAGLY
TCOA
TGLP
THF
THFG
THFm THIAMIN
THMP
THPTGLU
THR
THRm THY
THZ
THZP
TPI
TPP
TPPP
THE

TRNA
TRNAG
TRNAGm TRNAm TRP
TRPm TRPTRNAm TYR
UDP
UDPG

anoyl)glucosami ne UDPG2A IUDP-3-O-(3-hydroxytetradec anoyl)-D-UDPGAL UDY-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 XSP 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. cei"evisiae or that are desired to simulate the activity of the full set of reactions occurring in S. ceT~evisiae. A reaction network data structure that is substantially complete with respect to the metabolic reactions of S.
ce~evisiae 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. ceYevisiae and that do not occur, either naturally or following manipulation, in or by another prokaryotic organism, such as Esche~ichia coli, Haenaophilus i~fluehzae, Bacillus subtilis, Helicobacter pylof~i or in or by another eukaryotic organism, such as Homo Sapiens. Examples of reactions that are unique to S. ce>~evisiae compared at least to Esche~ichia coli, Haemoplailus ihfluehzae, and Helicobczcten pylof-i include those identified in Table 4. It is understood that a S. cef°evisiae 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.
cef~evisiae, for example, when designing or engineering man-made cells or strains.
Table 4. Reactions specific to S. cerevisiae metabolic network glkl 3, hxkl-1, hxk2_l, hxkl_4, hxk2_4, pffcl 3, idhl, idpl_l, idpl 2, idp2_l, idp3_l, idp2 2, idp3 2, lsclR, pycl, pyc2, cyb2, dldl, ncpl, cytr , cyto, atpl, pmal, pma2, pmpl, pmp2, coil, rbkl_2, achl_l, achl_2, sfal_1R, unkrxllR, pdcl, pdc5, pdc6,1ys20, adhlR, adh3R, adh2R, adh4R, adhSR, sfal_2R, psal, pfk26, pfk27, fbp26, gal7R, mell_2, melt 3, mell_4R, mell SR, mell 6R, mell_7R, fsp2b, sorl, gsyl, gsy2, ffcsl, flcs3, gsc2, tpsl, tps3, tsll, tps2, athl, nthl, nth2, fdhl, tfola, tfolb, durlR, dur2, nit2, cyrl, gukl 3R, ade2R, pdel, pde2_l, pde2_2, pde2_3, pde2 4, pde2 5, apa2, apal_l, apal 3, apal_2R, ura2_l, ura4R, ural_1R, uralOR, uraSR, ura3, npkR, furl, fcyl, tdkl, tdk2, urkl_l, urkl_2, urkl 3, deoalR, deoa2R, cddl_l, cddl 2, cdcBR, dutl, cdc2l, cmka2R, dcdlR, ura7 2, ura8_2, deglR, puslR, pus2R, pus4R, ural_2R, aral_l, aral_2, gnalR, pcmlaR, qrilR, chsl, chs2, chs3, put2_l, putt, gltl, gdh2, cat2, yatl, mhtl, sam4, ecm40_2, cpa2, ura2_2, arg3, spe3, spe4, amd, amd2_l, atrna, msrl, rnas, ded8l, hom6_l, cys4, glyl, agtR, gcv2R, sahl, meth, cys3, metl7_l, metl7hR, dph5, met3, metl4, metl7 2, metl7 3,1ys21,1ys20a, lys3R, Iys4R,1ys12R,1ys12bR, amitR, lys2_l, lys2_2, lys9R, lyslaR, krsl, mskl, pro2_l, gpslR, gps2R, pro3 3, pro3 4, pro3_l, pro3 5, dallR, dal2R, dal3R, lus4 3, htsl, hmtl, tyrl, ctal, cttl, ald6, ald4_2, ald5_l, 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 l, gpxlR, gpx2R, hyrlR, ecm38, nit2_l, nit2_2, nmtl, natl, nat2, bgl2, exgl, exg2, sprl, thi80_l, thi80_2, unkrxn8, phol l, finnl_l, fmnl 2, pdx3 2R, pdx3 3R, pdx3 4R, pdx3_l, pdx3_5, biol, foll_4, ftfa, ftfb, fol3R, met7R, rmalR, metl2, metl3, misl 2, ade3_2, mtdl, fmtl, TypeII_l, TypeII 2, TypeII 4, TypeII 3, TypeII 6, TypeII
5, TypeII_9, TypeII_8, TypeII_7, c100sn, c180sy, c182sy, faalR, faa2R, faa3R, faa4R, fox2bR, potl_l, ergl0_1R, ergl0_2R, Gatl_2, Gat2 2, ADHAPR, AGAT, slcl, Gatl-1, Gat2_l, cholaR, cholbR, cho2, opi3_l, opi3_2, ckil, pctl, cptl, ekil, ectl, eptlR, inol, impal, pisl, tort, tort, vps34, pikl, sst4, fabl, mss4, plcl, pgslR, crdl, dppl, lppl, hmgsR, hmglR, hmg2R, ergl2_l, ergl2 2, ergl2 3, ergl2_4, erg8, mvdl, erg9, ergl, erg7, unkrxn3, unkrxn4, cdisoa, ergl l_l, erg24, erg25_l, erg26_l, ergll 2, erg25_2, erg26_2, ergll 3, erg6, erg2, erg3, ergs, erg4, lcbl, lcb2, tscl0, sur2, csyna, csynb, scs7, aurl, csg2, surl, iptl, lcb4_l, lcb5_l, 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, mnadglR, mnadg2R, mnptl, mnadi, heml, bet2, coql, coq2, coxl0, raml, rer2, srtl, mo2R, mco2R, methR, mmthnR, mnh3R, mthfR, mmthfR, mserR, mglyR, mcbhR, moicapR, mproR, mcmpR, macR, macar_, mcar_, maclacR, mactcR, moivalR, momvalR, mnmalRR, mslf, mthrR, maka, aacl, aac3, pet9, mirlaR, mirldR, dicl 2R, dicl_1R, dicl 3, mmltR, moabR, ctpl_1R, ctpl 2R, ctpl 3R, pyrcaR, mlacR, gcaR, gcb, ortlR, crcl, gut2, gpd2, mt3p, mgl3p, mfad, mriboR, mdtbR, mmcoaR, mmvlR, mpaR, mppntR, madR, mprppR, mdhfR, mqaR, moppR, msamR, msahR, sfcl, odclR, odc2R, hxtl_2, hxtl0_2, hxtl l 2, hxtl3_2, hxtl5_2, l~ctl6_2, hxtl7_2, hxt2 2, hxt3_2, hxt4_2, hxt5_2, hxt6_2, hxt7_2, hxt8 5, hxt9_2, sucup, akmupR, sorupR, arbuplR, gltlupb, gal2 3, hxtl_l, hxtl0_l, hxtl l, hxtll_l, hxtl3_l, hxtl5_l, hxtl6_l, hxtl7_l, hxt2_l, hxt3_l,1~t4, hxt4_l, hxt5_l,- hxt6-1, hxt7_l, hxt8_4, hxt9_l, stll_l, gaupR, mmpl, mltup, mntup, nagup, rmnup, ribup, treup 2, treup_l, xylupR, uga5, bap2_1R, bap3_1R, gapSR, gnp3R, tat7R, vap7R, sam3, put7, uga4, dip9R, gap22R, gap7R, gnplR, gap23R, gap9R, hiplR, vap6R, bap2 4R, bap3 4R, gapl3R, gap26R, gnp4R, muplR, mup3R, bap2 SR, bap3 SR, gapl4R, gap29R, tat4R, ptrup, sprupl, ptr2, ptr3, ptr4, mnadd2, fcy2 3R, fcy21_3R, fcy22_3R, gnupR, hyxnupR, nccup3, nccup4, nccup6, nccup7, ncgup4, ncgup7, ncgupl l, ncgupl2, ncup4, ncup7, ncupl l, ncupl2, ethupR, sull, sul2, sulup, citupR, amgupR, atpmt, glaltxR, dal4, dal5, mthupR, papxR, thyxR, ga6pupR, btupR, kapaupR, dapaupR, ogtup, sprmup, pimeup, thml, thm2, thm3, rflup, hnml, ergupR, zymupR, hxtl 5, hxtl0 3, hxtl l 3, hxtl3 3, hxtl5 3, hxtl6 - -3, hxtl7_3, hxt2 3, hxt3 3, hxt4 3, hxt5_3, hxt6 3, hxt7 3, hxt8 6, hxt9 3, itrl, itr2, bio5a, agp2R, dttpxR, ltup [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 genes) 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.

[0060] A gene database of the invention can include a substantially complete collection of genes and/or open reading frames in S. ce~evisiae or a substantially complete collection of the macromolecules encoded by the S. cep°evisiae 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. cef°evisiae 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.
ce~evisiae 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.
ce~evisiae genome such as at least 10%, 15%, 20%, 25%, 50%, 75%, 90% or 95% of the S.
cef~evisiae 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.

[0061] 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. cef~evisiae reactants that are substrates and products of the S. ceYevisiae 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. cenevisiae 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 ce~evisiae 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. ce~evisiae 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 i~ 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 proteins) 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 genes) shown to code for a particular proteins) 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.

[0065] 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 chromosomes) 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 patnway 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.

[0066] 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. Amiotating 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.
ceYevisiae 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. ce~evisiae. 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. cef°evisiae reactants to a plurality of S. ee~evisiae 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. cef~evisiae 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.
ceYevisiae with inputs and outputs for substrates and by-products produced by the metabolic network.

[0073] Returning to the hypothetical reaction network shown in Figure l, 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 (3~ <_ v~ _< a.p : j = 1. . . . n (Eq. 1) where v~ is the metabolic flux vector, (3~ is the minimum flux value and a~ is the maximum flux value. Thus, oc~ can take on a finite value representing a maximum allowable flux through a given reaction or (3~ 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 (31 to negative infinity and a~ to positive infinity as shown for reaction R~ in Figure 3. If reactions proceed only in the forward reaction [3~
is set to zero while a~ is set to positive infinity as shown for reactions Rl, 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 a~ 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 oc~ and (3~ 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 iya silico S. cerevisiae model and methods described herein can be implemented on any conventional host computer system, such as those based on InteI®
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, Learning XML O'Reilly and Associates, Sebastopol, CA (2001).

[0077] 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 colmnn Vgro",~1, to the stoichiometric matrix to represent fluxes to describe the production demands placed on the metabolic system.
Setting this new flux as the obj ective 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=~C~ ~V~
where Z is the obj ective which is represented as a linear combination of metabolic fluxes v;
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. ce~evisiae 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. cef~evisiae 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. ce~evisiae 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. cenevisiae 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 Bioen~. 77:27-36(2002), can be used to analyze the results of a simulation using an ifZ
silico S. cerevisiae model of the invention.

[0088] A physiological function of S. ce~evisiae can also be determined using a reaction map to display a flux distribution. A reaction map of S. ceYevisiae 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. ce~evisiae is shown in Figure 4.

[0089] The invention also provides an apparatus that produces a representation of a S.
ceYevisiae physiological function, wherein the representation is produced by a process including the steps o~ (a) providing a data structure relating a plurality of S. ce~evisiae reactants to a plurality of S. ce~evisiae reactions, wherein each of the S.
cef~evisiae 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. cef°evisiae 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. cef~evisiae 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.
cep°evisiae. 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 a,~ or (3i 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 oc~ or (3~
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 Enaineerin_g 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/CTS91/08694 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 ih 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. eerevisiae. 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 a~ or (3~ 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 enviromnental 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 S 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 Saccharonayces Genome Database (SGD) (Cherry et al., Nucleic Acids Research 26(1): 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. eerevisiae.

Table 5 Amino acid biosynthesis Strathern et al., The Molecular bioloQ;y of the~east Saccharomyces metabolism and eg ne expression 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 phosphor~~lation arid 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 Metabolisrrt 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., su ra (1982)) Transport across the cytoplasrnic nZernbrane 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 nritochondrial membrane Palmieri et al., J Bioene~ Biomembr 32(1): 67:77 (2000);
Palmieri et al., Biochim Bioph s~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 mitochondria) aldehyde dehydrogenase encoded by ALD4 can use both NADH and NADPH as a cofactor (Remize et al. Anpl 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.
ce~evisiae metabolism. S. cerevisiae lacks a gene that encodes the enzyme transhydrogenase.
Insertion of a corresponding gene from Azetobacter vi~elahdii 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 S 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. cenevisiae grown on glucose (Verduyn et al., supra (1991)). However, based on experimental measurements, it has been determined that the net ih 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 mitochondria) transmembrane proton gradient needed to drive metabolite exchange, such as the proton-coupled translocation of pyruvate, across the inner mitochondria) 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 mitochondria) membrane (Westerhoff and van Dam, Thermodynamics and control of biological free-enerQy 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 ifa 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 mitochondria) 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 mitochondria) membrane contributes significantly to the operational P/O
ratio.
EXAMPLE III
Phenotypic phase inane 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, OZ 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 defining 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. Bioen~. 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 (P 1-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] Pl: 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 COz 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 Oz 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. ce~evisiae 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 cer~evisiae, 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. cef°evisiae 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 COz. The calculated RQ along the LO is a constant value of 1.06; the RQ in P 1 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. Bioen~. 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 & Bioen~ 19:69-86 (1977)) and 1.1 (Wang et al., Biotechnol. & Bioen~. 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., A~nl. 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. cef°evisiae 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 ih 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 ATP/gDW, 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 h-~. The computed by-product secretion rates were then compared to the experimental data (Nissen et al. Microbiolo~y 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 ira 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., Microbiolo~y 143(1):

(1997)).

[0135] Optimal growth properties of S. cenevisiae 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 ih 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 ih silico model makes quantitative predictions about the respiratory quotient, glucose uptake, ethanol, CO2, and glycerol secretion rates under aerobic glucose-limited continuous condition (Fig. 10).
EXAMPLE VII
Analysis of deletion of enes 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 fluxes) 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] Ift silico results were compared to experimental results as supplied by the Sacclaaf~omyces 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 ira 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 ira 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 in silicolin in silicol in silicol References:
silicol Gene in vivo in ira vivo ir: vivo (Minimal vivo media) ACOI +/+ -l- (Gangloff et al., 1990) CDC19# +/- +l- (Boles et al., 1998) CITI +l+ +/+ (Kim et al., 1986) CIT2 +/+ +/+ (Kim et al., 1986) CIT3 +l+

DAL7 +/+ +/+ +/+ +/+ (Hartig et al., 1992) ENOI +/+

ENO2$$ +l- +l-FBAI +/- +/-*

(Sedivy and Fraenkel, 1985;

FBPI +l+ +/+ -/-Gancedo and Delgado, 1984) FUMI +l+

GLKI +!+

GNDl"# +l- +l-GND2 +l+

GPMI +/- +/-~~

GPM2 +/+

GPM3 +/+

HXKI +l+

HXK2 +/+

ICLI +/+ +l+ (Smith et al., 1996) (Cupp and McAlister-Henn, IDHI +/+ +/+ 1992) (Cupp and McAlister-Henn, IDH2 +/+ +l+ 1992) IDPI +/+ +/+ (Loftus et al., 1994) IDp2 +/+ +/+ (Loftus et al., 1994) IDP3 +l+

KGDI +/+ +l+ (Repetto and Tzagoloff, 1991) KGD2 +l+ +/+ (Repetto and Tzagoloff, 1991) LPDI +/+

LSCI +/+ +/+ +/+ (Przybyla-Zawislak et al., 1998) LSC2 +l+ +/+ +/+ (Przybyla-Zawislak et al., 1998) MAEI +/+ +/+ +/+ (Boles et al., 1998) (McAlister-Henn and Thompson, MDHI +/+ +l+ +/_ 1987) (McAlister-Henn and Thompson, MDH2 +l+ +/- +/_ 1987) MDH3 +/+

MLSI +/+ +/+ +/+ +/+ (Hartig et al., 1992) OSMI +l+

PCKI +/+

PDCI +l+ +/+ (Flikweert et al., 1996) PDCS +/+ +/+ (Flikweert et al., 1996) PDC6 +/+ +/+ (Flikweert et al., 1996) PFKI +/+ +l+ (Clifton and Fraenkel, 1982) PFK2 +/+ +/+ (Clifton and Fraenkel, 1982) PGII *' +/- +/- (Clifton et al., & 1978) PGKI * +/- +/-PGMl +/+ +l+ (Boles et al., 1994) PGM2 +/+ +/+ (Boles et al., 1994) PYCI +/+ +/+ +/- +/- (Wills and Melham, 1985) PYC2 +l+

(Boles et al., 1998;
McAlister-pyK~ +l+ +l+ +/+ Henn and Thompson, 1987) RKII -l-RPEI +l+

SOLI +l+

SOL2 +/+

SOL3 +/+

SOLO +l+

(Schaaff Gerstenschlager and TALL +/+ +l+ Zimmermann, 1993) TDHI +/+

TDH2 +/+

TDH3 +l+

(Schaff Gerstenschlager and TKLI +/+ +/+ Zimmermann, 1993) TKL2 +/+

TPII i'$ +l-(Schaaff Gerstenschlager and ZWFI +/+ +l+ Zimmermann, 1993) +/- Growth/no growth The isoenyzme n growth on glucose.
Pyk2p is glucose repressed, and cannot sustai * 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 GNDI
accumulates gluconate-6-phosphate, which is toxic to the cell (Schaaff Gerstenschlager and Miosga, 1997).
$$ ENO1 plays central role in gluconeogenesis whereas EN02 is used in glycolysis (Miiller 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 ih silico and compared to published experimental results. In 526 cases (87.8%), the iya 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 iya 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 I~IIPS 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 ih 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 ih silico phenotype was not in agreement with its ira 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 i~ 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 TPII, had a severe impact on the growth rate. Experimentally, a deletion in TPIl 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 networle 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 ih 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 I 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%

[0151] A comprehensive list of all the genes used in the ih 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 as T1V01,, 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 URI BET2 CDC19 CDC21 CDC8 CYRl ED81 DFRI IMI DUTI YSI EN02 ERG10 ERG13 FADI FMNI FOLI FOL2 FOL3 GFAI GPMl EMI EMl~ EMl3 EMI EM2 EM3 EM4 HIPI
TSI IL V3 IL VS SI LCBI LCB2 MSS4 NAT2 NCPI MTl PCMI PET9 GSI IKI PMAI PRO3 ANSI
QRIl RER2 RIBS ECS STT4 THI80 TOR2 TPI TSC10 UGPl URA6 YDR341C YGL245W
False Negative ADE3 ADKI CHOl CH02 DPPI ERG3 ERGO ERGS ERG6INMl METE OP13 PPT2 YNKl True Negative ACCI ADEl3 CDSI DPMI ERGI ERG7 ERG8 ERG9 ERGll ERGl2 ERG20 ERG25 ERG26 ERG27 FBAI
GLNI~GUKI IDll IPPI MVDI PGII PGKI PISl PMI40 PSAI RKII SAHI SEC53 TRRI YDR531 W
True Positive AACI AAC3 AAHI AATI AA T2 ABZI ACOI ACSI ADEI ADE12 ADEl6 ADE17 ADE2 ADE4 ADES

ALPI ASPI ATHI ATPI BAP2 BAP3 BATI BAT2 BGL2 IO~ 103 10 10 NA! CANI CARL CAR2 CDDl CEM! CHAI CHSI CHS2 CHS3 CIT! CIT2 CIT3 CKI! COQl COQ2 CO 3 CO CO COXI
COXIO CPA2 CPT! CRCI CRDI CSG2 CTAI CTPI CTTI CYB2 CYS3 CYS4 DAK! DAK2 DALI

DAL3 DAL4 AL DAL7 DCDI DEGI DIC! DIPS DLD! PHS DPLI DURI DUR3 ECMl7 ECM:31 ECM40 ECT! KI! ENOI PTl ERG2 ERG24; ERR! ERR2 EXGI EXG2 FAAI FAA2 FAA3 FAA4 FABI
F~Sl FBPI FBP26 FCYI FCY2 FKS! FKS3 FLX! MTI OX2 FRDS FUII FUM! FUN63 FURL

GADI GALL GAL10 GAL2 GAL7 GAP! GCYI GCV2 GDH! GDH2 GDH3 GLC3 GLK! GLO! GL02 GLRI GLT! GLYI GNA1 GND! GND2 GNPI GPDI GPD2 GPHI GPM2 GPM3 GPX! GPX2 GSC2 GSH!
GSH2 GSY! GSY2 GUA! GUTI GUT2 EM14 HISI HIS2 HIS3 HIS4 HISS HIS6 HIS7 HMGI
HMG2 MT!
HNM! HOM2 HOM3 HOME HOR2 HPTI HXK! HXK2 HXT! HXTIO HXTll HXT13 HXTI4 HXT15 HXTI7 HXT2 HXT3 HXT4 HXTS HXT6 HXT7 HXTB HXT9 HYRI ICLI ICL2 IDH! IDPl IDP2 IL V2 IlVC71 PTI ITRI ITR2 JENI KGD! KRE2 KTR! KTR2 KTR3 KTR4 KTR6 LCB3 LCB4 LCBS EUI
LEU2 LEU4 PD! PP! LSCI LSCZ LYPI LYSI LYSl2 LYS2 LYS20 LYS21 LYS4 LYS9 MAEI
MAK3 MALl2 MAL3l MAL32 MDH! MDH2 MDH3 MEL! MEPI MEP2 MEP3 ETI METI~Tl~ TI3 MET14 METI6 METl7 MET2 MET22 MET3 MET7 MHTI MIRI MISI MLSI MPI ~'SEl ''ISKl MSRI
''ISWI
MTDl MUP! MUP3 NATI NDHI NDH2 NDII HAI IT2 NPTI NTAI NTHI NTH2 OACI ODCI ODC2 ORT! OSMI PAD! PCK! CTI PDA! PDC! PDCS PDC6 PDEI PDE2 PDX3 PFK! PFK2 PFK2 6 PGMI PGM2 PHA2 PHO8 PHOll PT~08~ LCI PMA2 PMPI PMP2 PMT! PMT2 PMT3 PMT4 PMTS
PMT6 NCI PNPl POSS OTI PPA2 PRM4 PRMS PRM6 PROI PR02 PRS! PRS2 PRS3 PRS4 PRSS
SDl ~,S.D~ PTR2 PURS PUSI PUS2 PUS4 PUT! PUT2 PUT4 PYCI PYC2 PYK2 QPTI RAMI RBKI
RHR2 RIB!

SFAI
SFCI SHMI SHM2 SLC! SOLl SOL2 SOL3 SOL4 ORI SPEI SPE2 SPE3 SPE4 SPR! SRT! STLl SULI SUL2 SUR! SUR2 TALL TATI TATZ TDHI TDH2 TDH3 TH120 TH121 THI22 THI6 THl7 THRI THR4 TKLI TKL2 TORI TPS! TPS2 TPS3 TRK! TRP! TRP2 TRP3 TRP4 TRPS TRR2 TSLI TYRI
UGAI UGA4 URAI URA2'URA3'URA4 URAS URA7 URA8 URA10 URHI URK! UTR! VAPI
VPS34XPTl YATl YSR3 YURl 2WF1 YBL098 YBR006W YBR284W YDLIOOC YDRI11 C YEL041 W YER053C

YFROSSW YGR012W YGR043C YGR125W YGR287CYIL145'G YILI67W 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:
ecrnld, yi1145cd, erg2 d, erg24 d, fall d, ural d, ura2 d, ura3 d and ura4 d 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 Yi1145c catalyses the final step in pantothenate synthesis from [3-alanine and panthoate. In vivo,, ecnild, and yi1145c d mutants require pantothenate for growth (White et al., J Biol Chem 276(14): 10794-10800 (2001)). By supplying pantothenate to the synthetic complete medium ih silico, the model predicted a viable phenotype and the growth rate was similar to ih 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 FASI (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 URAl, 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 GLNI, 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 gl~zld mutants are therefore not viable. However, in silieo, 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 IlvS are both involved in branched amino acid metabolism. ~ne may expect that a deletion of ILV3 or IL VS 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 i1v30 and ilv5~ 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 CH02 or OPl3 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 ih silico model.

[0162] Deletions in the ergosterol biosynthetic pathways of ERG3, ERGO, ERGS
or ERG6 lead ira vivo to viable phenotypes. The former two strains accumulate ergosta-8,22,24 (28)-trien-3-beta-of (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-then-3beta-of (Hata et al., J
Biochem (Tokyo) 94(2): 501-510 (1983)), or zymosterol and smaller amounts of cholesta-5,7,24-trim-3-beta-of and cholesta-5,7,22,24-trim-3-beta-of (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 QIRl. 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 (inml ~, rnet6~, ynkl ~, pho840, psd2~, tps2~) , 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 INMl, 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 inrnl dimp2d 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] Met6d 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 METE 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 Ynklp is the synthesis of nucleoside triphosphates from nucleoside diphosphates. YNK10 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 pho84~ 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 Psdl (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 ih 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., su ra (1998)).
The corresponding genes) 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 (C1-tetrahydrofolate synthase) and ADKI
(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, HTSl, KRSI, YDR41 C, YGL245YT~.
~ However, pathways of tRNA synthesis were not fully included.
~ Heme synthesis was considered in the reconstructed model (HEMl, HEMl2, 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 aurld, lcbld, lcb2d, tscl0d, ded8ld, htsld, krsld, ydr4lcd, ygl~45wd, herald, heml2d, heml3d, hemlSd, 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 andlor 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 irz silico synthetic complete medium or by initially missing information in the model, many false positives may be explained by ira 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 CDCl9, 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, ACS1 or ENO1, 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 su~g~ar metabolism :
biochemistry, genetics, biotechnoloQ;y, and applications Technomic Pub., Lancaster, PA (1997)). A
deletion of GPMI 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, CDC~, CYRl, DIMl, ENO2, FADl, GFA1, GPMl, HIPI, MSS4, PET9, PIKI, PMA1, 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.
Table 9. Reference list for Table 2 Boles,E., Liebetrau,W., Hofinann,M. & Zimmermann,F.K. A family of hexosephosphate mutases in Saccharomyces cerevisiae. Eur. J. Biochem. 220, 83-96 (1994).

Boles,E. Yeast sugar metabolism. Zimmermann,F.K. & Entian,K.-D. (eds.), pp. 81-96(Technomic Publishing CO., INC., Lancaster,1997).
Boles,E., Jong-Gubbels,P. & Pronk,J.T. Identification and characterization of MAEl,the Saccharomyces cerevisiae structural gene encoding mitochondria) malic enzyme.
J.
Bacteriol. 180, 2875-2882 (1998).
Clifton,D., Weinstock,S.B. & Fraenkel,D.G. Glycolysis mutants in Saccha~omyces cerevisiae. Genetics 88, 1-11 (1978).
Clifton,D. 8c Fraenkel,D.G. Mutant studies of yeast phosphofructokinase.
Bioclaenaistry 21, 1935-1942(1982).
Cupp,J.R. & McAlister-Henn,I,. Cloning and Characterization of the gene encoding the IDH1 subunit of NAD(+)-dependent isocitrate dehydrogenase from Saccha~omyces cerevisiae. J.
Biol. ChenZ. 267, 16417-16423 (1992).
Flikweert,M.T. et al. Pyruvate decarboxylase: an indispensable enzyme for growth of Sacchay~omyces cerevisiae on glucose. Yeast 12, 247-257 (1996).
Gancedo,C. & Delgado,M.A. Isolation and characterization of a mutant from Saccharomyces ceYevisiae lacking fructose 1,6-bisphosphatase. Eur. J. Biochem.
139, 651-655 (1984).
Gangloff,S.P., Marguet,D. & Lauquin,G.J. Molecular cloning of the yeast mitochondria) aconitase gene (ACO1) and evidence of a synergistic regulation of expression by glucose plus glutamate. Mol Cell Biol 10, 3551-3561 (1990).
Hartig,A. et al. Differentially regulated malate synthase genes participate in carbon and nitrogen metabolism of S. cerevisiae. Nucleic Acids Res. 20, 5677-5686 (1992).
Heinisch,J.J., Muller,S., Schluter,E., Jacoby,J. & Rodicio,R. Investigation of two yeast genes encoding putative isoenzymes of phosphoglycerate mutase. Yeast 14, 203-213 (1998).
Kim,K.S., Rosenkrantz,M.S. & Guarente,L. Saccharonayces cef°evisiae contains two functional citrate synthase genes. Mol. Cell Biol. 6, 1936-1942 (1986).
Loftus,T.M., HalI,L.V., Anderson,S.L. & McAlister-Henn,L. Isolation, characterization, and disruption of the yeast gene encoding cytosolic NADP-specific isocitrate dehydrogenase.
BiochemistYy 33, 9661-9667 (1994).
McAlister-Heim,L. & Thompson,L.M. Isolation and expression of the gene encoding yeast mitochondria) malate dehydrogenase. J. Bacter~iol. 169, 5157-5166 (1987).
Miiller,S. & Entian,K.-D. Yeast sugar metabolism. Zimmermann,F.K. & Entian,K.-D. (eds.), pp. 157-170 (Technomic Publishing CO.,INC., Lancaster,1997).

Ozcan,S., Freidel,K., Leuker,A. & Ciriacy,M. Glucose uptake and catabolite repression in dominant HTRI mutants of Sacclaaromyces cerevisiae. .I. Bacteriol. 175, 5520-(1993).
Przybyla-Zawislak,B., Dennis,R.A., Zakharkin,S.O. & McCammon,M.T. Genes of succinyl-CoA ligase from Saccharomyces cerevisiae. Eur. J. Bioch.ena. 258, 736-743 (1998).
Repetto,B. & Tzagoloff,A. Ih vivo assembly of yeast mitochondria) alpha-ketoglutarate dehydrogenase complex. Mol. Cell Biol. 11, 3931-3939 (1991).
Schaaff Gerstenschlager,I. & Zimmermaim,F.K. Pentose-phosphate pathway in Saccharonayces cerevisiae: analysis of deletion mutants for transketolase, transaldolase, and glucose 6-phosphate dehydrogenase. Cm°r. Genet. 24, 373-376 (1993).
Schaaff Gerstenschlager,I. & Miosga,T. Yeast sugar metabolism. Zimmermann,F.K.
&
Entian,K.-D. (eds.), pp. 271-284 (Technomic Publishing CO.,INC., Lancaster,1997).
Sedivy,J.M. & Fraenkel,D.G. Fructose bisphosphatase of Saccharomyces cerevisiae.
Cloning, disruption and regulation of the FBP 1 structural gene. J. Mol. Biol.
186; 307-319 (1985).
Smith,V., Chou,K.N., Lashkari,D., Botstein,D. ~ Brown,P.O. Functional analysis of the genes of yeast chromosome V by genetic footprinting. Science 274, 2069-2074 (1996).
Swartz,J. A PURE approach to constructive biology. Nat. Biotechhol. 19, 732-733 (2001).
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236, 782-791 (1985).

[0176] Throughout this application various publications have been referenced.
The disclosures of these publications in their entireties are hereby incorporated by reference in this application in order to more fully describe the state of the art to which this invention pertains.

[0177] Although the invention has been described with reference to the examples provided above, it should be understood that various modifications can be made without departing from the spirit of the invention. Accordingly, the invention is only limited by the claims.

Claims (52)

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;
(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 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.

2. The computer readable medium or media of claim 1, wherein at least one reactant in said plurality of Saccharomyces cerevisiae reactants or at least one reaction in said plurality of Saccharomyces cerevisiae reactions is annotated with an assignment to a subsystem or compartment.

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

4. 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.

5. 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.

6. 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.

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

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

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

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

11. The computer readable medium or media of claim 2, 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.

12. 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.

13. 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;
(b) a constraint set for said plurality of Saccharomyces cerevisiae reactions, and (c) 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 Saccharomyces cerevisiae growth.

14. 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.

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

16. The method of claim 14, 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.

17. The method of claim 14, 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.

18. The method of claim 14, 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.

19. The method of claim 14, wherein said data structure comprises a set of linear algebraic equations.

20. The method of claim 14, wherein said data structure comprises a matrix.

21. The method of claim 14, wherein said flux distribution is determined by linear programming.

22. The method of claim 14, 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.

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

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

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

26. The method of claim 23, 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.

27. The method of claim 14, 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.

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

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

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

31. The method of claim 28, 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.

32. The method of claim 14, 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.

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

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

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

36. The method of claim 33, 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.

37. The method of claim 14, 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.

38. 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;
(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 Saccharomyces cerevisiae growth.

39. 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 a 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;
(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.

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

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

42. The method of claim 39, further comprising annotating a reaction in said data structure.

43. The method of claim 42, 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.

44. The method of claim 39, 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.

45. The method of claim 39, wherein said adding a reaction comprises adding a reaction selected from the group consisting of an infra-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.

46. The method of claim 45, 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.

47. The method of claim 39, 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.

48. The method of claim 39, 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.

49. The method of claim 39, wherein said data structure comprises a set of linear algebraic equations.

50. The method of claim 39, wherein said data structure comprises a matrix.

51. The method of claim 39, wherein said flux distribution is determined by linear programming.

52. 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 a 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;

(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 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.