WO2003036296A9 - 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 cerevisiae is one of the best-studied microorganisms and in addition to its significant industrial importance it serves as a model organism for the study of eukaryotic cells (Winzeler et al. Science 285: 901-906 (1999)). Up to 30% of positionally cloned genes implicated in human disease have yeast homologs.

[0004] The first eukaryotic genome to be sequenced was that of S. cerevisiae, and about 6400 open reading frames (or genes) have been identified in the genome. S. cerevisiae was the subject of the first expression profiling experiments and a compendium of expression profiles for many different mutants and different growth conditions has been established. Furthermore, a protein-protein interaction network has been defined and used to study the interactions between a large number of yeast proteins.

[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. cerevisiae 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. cerevisiae reaction networks, such as its metabolic network, which can be used to simulate many different aspects of the cellular behavior of S. cerevisiae under different conditions. The present invention satisfies this need, and provides related advantages as well.

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

[0009] The invention also provides a method for predicting physiological function of S. cerevisiae, including: (a) providing a data structure relating a plurality of S. cerevisiae to a plurality of S. cerevisiae reactions, wherein each of the S. cerevisiae reactions includes a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating the substrate and the product; (b) providing a constraint set for the plurality of S. cerevisiae reactions; (c) providing an objective function, and (d) determining at least one flux distribution that minimizes or maximizes the objective function when the constraint set is applied to the data structure, thereby predicting a S. cerevisiae physiological function. In one embodiment, at least one of the S. cerevisiae reactions in the data structure is annotated to indicate an associated gene and the method predicts a S. cerevisiae physiological function related to the gene.

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

[0011] Figure 1 shows a schematic representation of a hypothetical metabolic network.

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

[0013] Figure 3 shows mass balance constraints and flux constraints (reversibility constraints) that can be placed on the hypothetical metabolic network shown in Figure 1. (°o, infinity; Yi, uptake rate value)

[0014] Figure 4 shows an exemplary metabolic reaction network in S. cerevisiae.

[0015] Figure 5 shows a method for reconstruction of the metabolic network of S. cerevisiae. Based on the available information from the genome annotation, biochemical pathway databases, biochemistry textbooks and recent publications, a genome-scale metabolic network for S. cerevisiae was designed. Additional physiological constraints were considered and modeled, such as growth, non-growth dependent ATP requirements and biomass composition.

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

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

[0019] Figure 9 shows anaerobic glucose limited continuous culture of S. cerevisiae. Figure 9 shows the utilization of glucose at varying dilution rates in anaerobic chemostat culture. The data-point at the dilution rate of 0.0 is extrapolated from the experimental results. The shaded area or the infeasible region contains a set of stoichiometric constraints that cannot be balanced simultaneously with growth demands. The model produces the optimal glucose uptake rate for a given growth rate on the line of optimal solution (indicated by Model (optimal)). Imposition of additional constraints drives the solution towards a region where more glucose is needed (i.e. region of alternative sub-optimal solution). At the optimal solution, the in 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 in silico. Figure 10A shows biomass yield (Yx), and secretion rates of ethanol (Eth), and glycerol (Gly). Figure 10B shows CO₂ secretion rate (qco₂) and respiratory quotient (RQ; i.e. q_Co₂/qo₂) of the aerobic glucose-limited continuous culture of S. cerevisiae. (exp, experimental).

DETAILED DESCRIPTION OF THE INVENTION

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

[0022] As an example, the S. cerevisiae metabolic model can be used to determine the optimal conditions for fermentation performance, such as for maximizing the yield of a specific industrially important enzyme. The model can also be used to calculate the range of cellular behaviors that S. cerevisiae can display as a function of variations in the activity of one gene or multiple genes. Thus, the model can be used to guide the organismal genetic makeup for a desired application. This ability to make predictions regarding cellular behavior as a consequence of altering specific parameters will increase the speed and efficiency of industrial development of S. cerevisiae strains and conditions for their use.

[0023] The S. cerevisiae metabolic model can also be used to predict or validate the assignment of particular biochemical reactions to the enzyme-encoding genes found in the genome, and to identify the presence of reactions or pathways not indicated by current genomic data. Thus, the model can be used to guide the research and discovery process, potentially leading to the identification of new enzymes, medicines or metabolites of commercial importance.

[0024] The models of the invention are based on a data structure relating a plurality of S. cerevisiae reactants to a plurality of S. cerevisiae reactions, wherein each of the S. cerevisiae reactions includes a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating the substrate and the product.

[0025] As used herein, the term "S. cerevisiae reaction" is intended to mean a conversion that consumes a substrate or forms a product that occurs in or by a viable strain of S. cerevisiae. The term can include a conversion that occurs due to the activity of one or more enzymes that are genetically encoded by a S. cerevisiae genome. The term can also include a conversion that occurs spontaneously in a S. cerevisiae cell. Conversions included in the term include, for example, changes in chemical composition such as those due to nucleophilic or electrophilic addition, nucleophilic or electrophilic substitution, elimination, isomerization, deamination, phosphorylation, methylation, glycolysation, reduction, oxidation or changes in location such as those that occur due to a transport reaction that moves a reactant within the same compartment or from one cellular compartment to another. In the case of a transport reaction, the substrate and product of the reaction can be chemically the same and the substrate and product can be differentiated according to location in a particular cellular compartment. Thus, a reaction that transports a chemically unchanged reactant from a first compartment to a second compartment has as its substrate the reactant in the first compartment and as its product the reactant in the second compartment. It will be understood that when used in reference to an in silico model or data structure, a reaction is intended to be a representation of a chemical conversion that consumes a substrate or produces a product.

[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 in silico model or data structure, a reactant is intended to be a representation of a chemical that is a substrate or a product of a reaction that occurs in or by a viable strain of S. cerevisiae.

[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. cerevisiae reactions or reactants in the range from 2 to the number of naturally occurring reactants or reactions for a particular strain of S. cerevisiae. Thus, the term can include, for example, at least 10, 20, 30, 50, 100, 150, 200, 300, 400, 500, 600 or more reactions or reactants. The number of reactions or reactants can be expressed as a portion of the total number of naturally occurring reactions for a particular strain of S. cerevisiae such as at least 20%, 30%, 50%, 60%, 75%, 90%, 95% or 98% of the total number of naturally occurring reactions that occur in a particular strain of S. cerevisiae.

[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. cerevisiae. The term can include, the rate at which a chemical is consumed or produced by S. cerevisiae, the rate of growth of S. cerevisiae or the rate at which energy or mass flow through a particular subset of reactions.

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

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

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

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

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

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

[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. cerevisiae. The reactants in a reaction network data structure can be designated as either substrates or products of a particular reaction, each with a stoichiometric coefficient assigned to it to describe the chemical conversion taking place in the reaction. Each reaction is also described as occurring in either a reversible or irreversible direction. Reversible reactions can either be represented as one reaction that operates in both the forward and reverse direction or be decomposed into two irreversible reactions, one corresponding to the forward reaction and the other corresponding to the backward reaction.

[0043] Reactions included in a reaction network data structure can include intra-system or exchange reactions. Intra-system reactions are the chemically and electrically balanced interconversions of chemical species and transport processes, which serve to replenish or drain the relative amounts of certain metabolites. These intra-system reactions can be classified as either being transformations or translocations. A transformation is a reaction that contains distinct sets of compounds as substrates and products, while a translocation contains reactants located in different compartments. Thus, a reaction that simply transports a metabolite from the extracellular environment to the cytosol, without changing its chemical composition is solely classified as a translocation, while a reaction such as the phosphotransferase system (PTS) which takes extracellular glucose and converts it into cytosolic glucose-6 -phosphate is a translocation and a transformation.

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

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

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

[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-Naline, 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).

[0048] A demand exchange reaction can be introduced for any metabolite in a model of the invention. Most commonly these reactions are introduced for metabolites that are required to be produced by the cell for the purposes of creating a new cell such as amino acids, nucleotides, phospholipids, and other biomass constituents, or metabolites that are to be produced for alternative purposes. Once these metabolites are identified, a demand exchange reaction that is irreversible and specifies the metabolite as a substrate with a stoichiometric coefficient of unity can be created. With these specifications, if the reaction is active it leads to the net production of the metabolite by the system meeting potential production demands. Examples of processes that can be represented as a demand exchange reaction in a reaction network data structure and analyzed by the methods of the invention include, for example, production or secretion of an individual protein; production or secretion of an individual metabolite such as an amino acid, vitamin, nucleoside, antibiotic or surfactant; production of ATP for extraneous energy requiring processes such as locomotion; or formation of biomass constituents.

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

[0050] A hypothetical reaction network is provided in Figure 1 to exemplify the above- described reactions and their interactions. The reactions can be represented in the exemplary data structure shown in Figure 2 as set forth below. The reaction network, shown in Figure 1, includes intrasystem reactions that occur entirely within the compartment indicated by the shaded oval such as reversible reaction R₂ which acts on reactants B and G and reaction R₃ which converts one equivalent of B to two equivalents of F. The reaction network shown in Figure 1 also contains exchange reactions such as input/output exchange reactions A_xt and E_xt, and the demand exchange reaction, N_gr0wth, which represents growth in response to the one equivalent of D and one equivalent of F. Other intrasystem reactions include Ri which is a translocation and transformation reaction that translocates reactant A into the compartment and transforms it to reactant G and reaction R_ό 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_mn element corresponds to the stoichiometric coefficient of the reactant m in the reaction denoted n. The stoichiometric matrix includes intra-system reactions such as R₂ and R₃ which are related to reactants that participate in the respective reactions according to a stoichiometric coefficient having a sign indicative of whether the reactant is a substrate or product of the reaction and a value correlated with the number of equivalents of the reactant consumed or produced by the reaction. Exchange reactions such as -E_xt and -A_xt 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 (E_cxtemai) such that an exchange reaction (Re) 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_cxtCrnai) is correlated by stoichiometric coefficients of 1 and 0, respectively. Demand reactions such as V_growth can also be included in the stoichiometric matrix being correlated with substrates by an appropriate stoichiometric coefficient. [0052] As set forth in further detail below, a stoichiometric matrix provides a convenient format for representing and analyzing a reaction network because it can be readily manipulated and used to compute network properties, for example, by using linear programming or general convex analysis. A reaction network data structure can take on a variety of formats so long as it is capable of relating reactants and reactions in the manner exemplified above for a stoichiometric matrix and in a manner that can be manipulated to determine an activity of one or more reactions using methods such as those exemplified below. Other examples of reaction network data structures that are useful in the invention include a connected graph, list of chemical reactions or a table of reaction equations.

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

[0054] Depending upon a particular application, a reaction network data structure can include a plurality of S. cerevisiae reactions including any or all of the reactions listed in Table 2. Exemplary reactions that can be included are those that are identified as being required to achieve a desired S. cerevisiae specific growth rate or activity including, for example, reactions identified as ACO1, CDC19, CIT1, DAL7, ENO1, FBA1, FBP1, FUM1, GND1, GPM1, HXK1, ICL1, IDH1, IDH2, IDP1, IDP2, IDP3, KGD1, KGD2, LPD1, LSC1, LSC2, MDH1, MDH2, MDH3, MLS1, PDC1, PFK1, PFK2, PGI1. PGK1, PGM1, PGM2, PYCl, PYC2, PYK2, RKIl, RPEl, SOLI, TALI, TDHl, TDH2, TDH3, TKLl, TPIl, ZWFl 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_1, MNADPHPS, MNADGl, MNADG2, MNADH, MNPT1.

Table 2

Locus # E.C. # Gene Gene Description Reaction Rxn Name

Carbohydrate Metabolism Glycolysls/Glucoπeogenesis

YCL040W 2712 G K1 Gluco inasc GLC + ATP->G6P + ADP glkl 1

YCL040W 2712 G K1 Glucokinase MAN + ATP -> MAN6P + ADP glkl 2

YCL040W 2712 GLK1 Glucokinase bDGLC + ATP -> bDG6P + ADP glkl_3

YFR053C 271 HXK1 Hexokinase I (PI) (also called Hexokmase A) bDG C + ATP -> G6P + ADP hxkl

YFR053C 271 HXK1 Hexokinase I (PI) (also called Hexokinase A) GLC + ATP -> G6P + ADP hxkl_2

YFR053C 271 HX 1 Hexokinase I (PI) (also called Hexokmase A) MAN + ATP -> MAN6P + ADP hxkl_3

YFR053C 271 HXK1 Hexokinase I (PI) (also called Hexokinase A) ATP + FRU -> ADP + F6P hxkl_4

YGL253W 271 HX 2 Hexokinase II (PH) (also called Hexokinase B) bDGLC + ATP -> G6P + ADP hxk2

YGL253W 271 HXK2 Hexokinase II (PIT) (also called Hexokinase B) GLC + ATP -> G6P + ADP hxk2~2

YGL253W 271 HXK2 Hexokmase II (PII) (also called Hexokinase B) MAN + ATP -> MAN6P + ADP hxk2_3

YGL253W 271 HXK2 Hexokinase II (PII) (also called Hexokinase B) ATP + FRU -> ADP + F6P hxk2_4

YBR196C 5319 PG11 Glucose-ό -phosphate isomerase G6P <-> F6P pgιl_l

YBR196C 5319 PGI1 Glucose-6-phosphate isomerase G6P <-> bDG6P pgll_2

YBR196C 5319 PGII Glucose-6 -phosphate isomerase bDG6P <-> F6P pgll_3

Y R205C 27111 PFK2 phosphofructokinase beta subunit F6P + ATP -> FDP + ADP plk2

YGR240C 27111 PFK1 phosphofructokinase alpha subunit F6P + ATP -> FDP + ADP pfkl l

YGR240C 27111 PFK1 phosphofructokinase alpha subunit ATP + TAG6P -> ADP + TAG16P pfkl_2

YGR240C 27111 PFKI phosphofructokinase alpha subunit ATP + S7P->ADP + S17P pfkl_3

YKL060C 41213 FBA1 fructose-bisphosp ate aldolase FDP<->T3P2 + T3P1 fbal_l

YDR050C 5311 TPIl tπosephosphate isomerase T3P2<->T3P1 tpil

YJL052 12112 TDHl Glyceraldehyde-3 -phosphate dehydrogenase 1 T3P1 + PI + NAD <-> NADH + 13PDG tdhl

YJR009C 12112 TDH2 glyceraldehyde 3 -phosphate dehydrogenase T3P1 + PI + NAD <-> NADH + 13PDG tdh2

YGR192C 12112 TDH3 Glyceraldehyde-3 -phosphate dehydrogenase 3 T3P1 + PI + NAD <-> NADH + 13PDG tdh3

YCR012W 2723 PGK1 phosphoglycerate kinase 13PDG + ADP <-> 3PG + ATP Pgkl

YKL152C 5421 GPMl Phosphoglycerate mutase 13PDG <-> 23PDG gp l l

Y L152C 5421 GPMl Phosphoglycerate mutase 3PG <-> 2PG gpml_2

YDL021W 5421 GPM2 Similar to GPMl (phosphoglycerate mutase) 3PG <-> 2PG gpm2

YOL056W 5421 GPM3 phosphoglycerate mutase 3PG <-> 2PG gpm3

YGR254W 4211 ENOl enolase 1 2PG <-> PEP enol

YHR174W 4211 EN02 enolase 2PG <-> PEP eno2

YMR323W 4211 ERR1 Protein with similarity to enolases 2PG <-> PEP eno3

YPL281C 4211 ERR2 enolase related protein 2PG <-> PEP eno4

YOR393W 4211 ERR1 enolase related protein 2PG <-> PEP eno5

YAL038W 27140 CDC 19 Pyruvate kinase PEP + ADP -> PYR + ATP cdcl9

YOR347C 27140 PYK2 Pyruvate kinase, glucose-repressed isoform PEP + ADP -> PYR + ATP pyk2

YER178w 1241 PDA1 pyruvate dehydrogenase (hpoamide) alpha chain PYRm + COA + NAD -> NADHm + C02m + pdal precursor, El component, alpha unit ACCOAm

YBR221C 1241 PDB1 pyruvate dehydrogenase (lipoamide) beta chain precursor, El component, beta unit

YNL071w 23112 LAT1 dihydrolipoamide S-acetyltransferase, E2 component Citrate cycle (TCA cycle) YNR001C 4137 CIT1 Citrate synlhase, Nuclear encoded mitochondria! ACCOAm + OAm -> COAm + CIT citl protein

YCR005C 4137 CIT2 Citrate synthase, non-mitochondnal citrate synthase ACCOA + OA -> COA + CIT cιt2

YPR001 4137 Clt3 Citrate synthase, Mitochondria! isoform of citrate ACCOAm + OAm -> COAm + CITm cιt3 synthase

YLR304C 4213 acol Acomtase, mitochondria! CIT olCITm acol

YJ 200C 4213 YJL200C aconitate hydratase homolog CITm <-> IClTm aco2

YNL037C 11141 IDH1 I so citrate dehydrogenase (NAD+) mi to, subuintl ICITm+ NADm ->C02m + NADHm + AKGm idhl

YOR136W 11141 1DH2 Isocitrate dehydrogenase (NAD+) ito, subumt2

YDL066W 11142 1DP1 Isocitrate dehydrogenase (NADP+) ICITm + NADPm -> NADPHm + OSUCm ιdpl_l

YLR174W 11142 1DP2 Isocitrate dehydrogenase (NADP+) ICIT + NADP->NADPH + OSUC ιdp2_l

YNL009W 11142 IDP3 Isocitrate dehydrogenase (NADP+) 1CIT + NADP -> NADPH + OSUC ldp3_l

YDL066W 11142 1DP1 Isocitrate dehydrogenase (NADP+) OSUCm -> C02m + AKGm 'dpl_2

Y R174W 11142 1DP2 Isocitrate dehydrogenase (NADP+) OSUC -> C02 + AKG ιdp2_2

YN 009W 11142 IDP3 Isocitrate dehydrogenase (NADP+) OSUC -> C02 + AKG ιdp3~2

YI 125W 1242 kgdl alpha-ketoglutarate dehydrogenase complex, El AKGm + NADm + COAm -> C02m + NADHm + kgd 1 a component SUCCOAm

YDR148C 23161 GD2 Dihydrolipoamide S-succinyl trans ferase, E2 component

YGR244C 6214/6 LSC2 Succ mate-Co A ligase (GDP-forming) ATPm + SUCCm + COAm <-> ADPm + Pirn + lsc2 215 SUCCOAm

YOR142W 6214/6 LSC1 succinote-CoA ligase alpha subunit ATPm + ITCm + COAm <-> ADPm + Plm + Iscl 215 ITCCOAm

Electron Transport System, Complex II

YKL141w 1351 SDH3 succinate dehydrogenase cytochrome b SUCCm + FADm <-> FUM + FADH2m sdh3

YKL148c 1351 SDH1 succinate dehydrogenase cytochrome b

YLL041c 1351 SDH2 Succinate dehydrogenase (ubiquinone) iron-sulfur protein subunit

YDR178w 1351 SDH4 succinate dehydrogenase membrane anchor subunit

YLR164w 1351 YLR1 4 strong similarity to SDH4P

YMR118C 1351 YMR118 strong similarity to succinate dehydrogenase c

YJL045w 1 51 YJL045w strong similarity to succinate dehydrogenase flavoprotein

YEL047C 139 1 YEL047c soluble fumarate reductase, cytoplasmic FADH2m + FUM -> SUCC + FADm frdsl

YJR051W 13991 osml Mitochondnal soluble fumarate reductase involved in FADH2m + FUMm -> SUCCm + FADm osml osmotic regulation

YPL262W 4212 FUM1 Fumaratase FUMm <-> MALm fumlj

YPL262W 4212 FUM1 Fumaratase FUM <-> MAL fuml_2

YKL085W 11137 MDH1 mitochondnal malate dehydrogenase MALm + NADm <-> NADHm + OAm mdhl

YDL078C 11137 MDH3 MALATE DEHYDROGENASE, PEROXISOMAL MAL + NAD <-> NADH + OA mdh3

YOL126C 11137 MDH2 malate dehydrogenase, cytoplasmic MAL + N AD <-> NADH + OA mdh2

Anaplerottc Reactions

YER065C 4131 ICL1 isocitrate lyase ICIT -> GLX + SUCC lcll

YPR006C 4131 ICL2 Isocitrate lyase, may be nonfunctional ICIT -> GLX + SUCC ιcl2

YIR031C 41 2 dal7 Malate synthase ACCOA + GLX -> COA + MAL da!7

YNL117W 4132 MLS1 Malate synthase ACCOA + GLX -> COA + MAL mlsl

YKR097W 41149 pckl phosphoenolpyruvate caiboxylkinase OA + ATP -> PEP + C02 + ADP pckl

YLR377C 31311 FBP1 fructose-l ,6-bιsphosphatase FDP -> F6P + PI fbpl

YGL062W 6411 PYCl pyruvate carboxylase PYR + ATP + C02 -> ADP + OA + PI pycl

YBR218C 6411 PYC2 pyruvate carboxylase PYR + ATP + C02 -> ADP + OA + PI pyc2

YKL029C 11138 MAE1 mitochondnal malic enzyme MALm + NADPm -> C02m + NADPHm + PYRm mael

Pentose phosphate cycle

YNL241C 11149 zwfl Glucose-6-phosphute-l -dehydrogenase G6P + NADP <-> D6PGL + NADPH zwfl

YNR034W 31131 SOLI Possible 6-phosphogluconoIactonase D6PGL -> D6PGC soil

YCR073W- 31131 SOL2 Possible 6-phosphoglυconotactonase D6PGL -> D6PGC sol2

A

YHR163W 31131 SOL3 Possible 6-phosphogluconolactoπase D6PGL -> D6PGC so 13

YGR248W 31131 SOL4 Possible 6-phosphogIuconolactonase D6PGL -> D6PGC sol4

YGR256W 1 I 144 GND2 6-phophogluconate dehydrogenase D6PGC + NADP -> NADPH + C02 + RL5P gnd2

YHR183W 11144 GND1 6-phophogluconate dehydrogenase D6PGC + NADP -> NADPH + C02 + RL5P gndl

YJL121C 5131 RPEl nbulose-5-P 3-epιmerase RL5P <-> X5P rpel

YOR095C 5316 RKIl nbose-5-P isomerase RL5P <-> R5P rkil

YBR117C 2211 TKL2 transketolase R5P + X5P<->T3P1 + S7P tk!2_l

YBR117C 2211 TKL2 transketolase X5P + E4P<->F6P + T3P1 tkl2~2

YPR074C 2211 TKLl transketolase R5P + X5P<->T3P1 + S7P tkll

YPR074C 2211 TKLl transketotase X5P + E4P<->F6P+T3P1 tkll_2

YLR354C 2212 TALI transaldolase T3P1 + S7P <-> E4P + F6P tall l

YGR043C 2212 YGR043 transaldolase T3P1 + S7P <-> E4P + F6P tall_2

C

YCR036W 27115 RBKl Ribokinase R1B + ATP->R5P + ADP rbklj

YCR036W 27115 RBKl Ribokinase DRIB + ATP -> DR5P + ADP rbkl_2

YKL127W 5422 pgml phosphoglucomutase RIP<->R5P pgml_l

YKL127W 5422 pgml phosphoglucomutase 1 G1P<->G6P pgml_2

YMR105C 5422 pgm2 phosphoglucomutase R1P<->R5P pgm2_l

YMR105C 5422 Pgm2 Phosphoglucomutase G1P<->G6P pgm2_2

Maπnose

YER003C 5318 PMI40 mannose-6-phosphate isomerase MAN6P <-> F6P pmι40

YFL045C 5428 SEC53 phosphomannomutase MAN6P<-> MANIP sec53

YDL055C 27713 PSA1 mannose-1 -phosphate guanyltransferase, GDP-mannose GTP + MANIP -> PPI + GDPMAN psal pyrophosphorylase

Fructose

YIL107C 271105 i PFK26 6-Phosphofructose-2 -kinase ATP + F6P -> ADP + F26P pfk26

YOL136C 271105 i pfk27 6-phosphofructo-2- ιπase ATP + F6P -> ADP + F26P pfk27

YJL155C 31346 FBP26 Fructose-2,6-bιphosphatase F26P -> F6P + PI fbp26

. 27156 - 1 -Phosphofructokinase (Fructose 1 -phosphate kinase) F1P + ATP->FDP + ADP frc3

Sorbose S c does not metabolize sorbitol, erythπtol, mannitol, xyhtol, bitol, arabimtol, galactinol

YJR159W 11 I 14 SOR1 sorbitol dehydrogenase (L-iditol 2-dehydrogenase) SOT + NAD -> FRU + NADH sorl Calactose metabolism

YBR020W 2716 gall galactokinase GLAC + ATP -> GAL1 P + ADP gall YBR018C 27710 gal7 galactose-1-phosphate undyl transferase UTP + GAL IP <-> PPI + UDPGAL ga!7 YBR019C 5132 gal 10 UDP-glucose 4-cpιmerase UDPGAL <-> UDPG gallO YHL012W 2779 YHL012 UTP--Glucose 1-Phosphate Uπdylyltransferase G 1 P + UTP <-> UDPG + PPI ugpl_2

W

YKL035W 2779 UGP1 Undinephosphoglucose pyrophosphorylase G 1 P + UTP <-> UDPG + PPI ugpl l YBR184W 32122 YBR184 Alpha-galactosidase (melibiase) MELI->GLC + GLAC mell

W

YBR184W 32122 YBR184 Alpha-galactosidase (melibiase) DFUC->GLC + GLAC mell_2

W

YBR184W 32122 YBR184 Alpha-galactosidase (melibiase) RAF->GLAC + SUC mell_3

W

YBR184W 32122 YBR184 Alpha-galactosidase (melibiase) GLACL <-> MYOI + GLAC mell t

W

YBR184W 32122 YBR184 Alpha-galactosidase (melibiase) EPM <-> MAN + GLAC mell_5

W

YBR184W 32122 YBR184 Alpha-galactosidase (melibiase) GGL<->GL + GLAC mell_6

W

YBR184W 32122 YBRI84 Alpha-galactosidase (melibiase) MELT <-> SOT + GLAC mell _7

W

YBR299W 32120 MAL32 Maltase MLT -> 2 GLC mal32a YGR287C 32120 YGR287 putative alpha glucosidase MLT -> 2 GLC mal32b

C

YGR292W 32120 MAL12 Maltase MLT -> 2 GLC mall 2a

YIL172C 32120 YIL172C putative alpha glucosidase MLT -> 2 GLC mall 2b

YJL216C 32120 YJL216C probable alpha-glucosidase (MALTase) MLT -> 2 GLC mall 2c

YJL221C 32120 FSP2 homology to maltase(alpha-D-glucosιdase) MLT -> 2 GLC fsp2a

YJL221C 32120 FSP2 homology to maltase(alpha-D-glucosιdase) 6DGLC -> GLAC + GLC fsp2b

YBR018C 27712 GAL7 UDPglucose— hexose-1 -phosphate undyiyi trans ferase UDPG + G ALI P <-> G 1 P + UDPGAL unkrxlO

Trehalose

YBR126C 24115 TPS1 tτehalose-6-P synthetase, 56 kD synthase subunit of UDPG + G6P -> UDP + TRE6P tpsl trehalose-6-phosphate synthaseVphosphatase complex

YML100W 24115 tsl I trehalose-6-P synthetase, 123 kD regulatory subunit of UDPG + G6P -> UDP + TRE6P tsll trehalose-6-ρhosphate synthaseVphosphatase complex\, homologous to TPS3 gene product

YMR261C 24115 TPS3 trehalose-6-P synthetase, 115 kD regulatory subunit of UDPG + G6P -> UDP + TRE6P tps3 trehalose-6-phosphate synthaseVphosphatase complex

YDR074W 31312 TPS2 Trehalose-6-phosphate phosphatase TRE6P -> TRE + PI tps2

YPR026W 32128 ATHI Acid trehalase TRE -> 2 GLC athl

YBROOIC 32128 NTH2 Neutral trehalase, highly homologous to Nthlp TRE -> 2 GLC πth2

YDR001C 32128 NTH I neutral trehalase TRE -> 2 GLC nthl

Glycogen Metabolism (sucorose and sugar metabolism)

YEL011W 24118 glc3 Branching enzyme, l,4-glucan-6-(l,4-glucano)- GLYCOGEN + PI -> GIP glc3 trans ferase

YPR160W 2411 GPH1 Glycogen phosphorylase GLYCOGEN + PI -> GIP gphl

YFR015C 24111 GSY1 Glycogen synthase (UDP-gluocse-starch UDPG -> UDP + GLYCOGEN gsyl glucosyltrans ferase)

YLR258W 24111 GSY2 Glycogen synthase (UDP-gluocse-starch UDPG -> UDP + GLYCOGEN gsy2 gl ucosy Itransferase)

Pyruvate Metabolism

YAL054C 6211 acsl acetyl-coenzyme A synthetase ATPm + ACm + COAm -> AMPm + PPIm + ACCOAm

YLR153C 6211 ACS2 acetyl-coenzyme A synthetase ATP + AC + COA -> AMP + PPI + ACCOA acs2

YDL168W 1211 SFAI Formaldehyde dehydrogenase/long-chain alcohol FALD + RGT + NAD <-> FGT + NADH sfal dehydrogenase

YJL068C 31212 . S-Formylglutathioπe hydrolase FGT <-> RGT + FOR unkrxll

YGR087C 4111 PDC6 pyruvate decarboxylase PYR -> C02 + ACAL pdc6

YLRI34W 4111 PDC5 pyruvate decarboxylase PYR -> C02 + ACAL pdc5

YLR044C 4111 pdcl pyruvate decarboxylase PYR -> C02 + ACAL pdcl

YBL015W 3121 ACH1 acetyl CoA hydrolase COA + AC -> ACCOA achl l

YBL015W 3121 ACH1 acetyl CoA hydrolase COAm + ACm -> ACCOAm achl_2

YDL131W 41321 LYS21 probable homocitrate synthase, mitochondnal isozyme ACCOA + AKG -> HCIT + COA Iys21 precursor

YDL182W 41321 LYS20 homocitrate synthase, cytosohc isozyme ACCOA + AKG -> HCIT + COA Iys20

YDL182 41321 LYS20 Homocitrate synthase ACCOAm + AKGm -> HCITm + COAm Iys20a

YGL256W 1111 adh4 alcohol dehydrogenase isoenzyme IN ETH + NAD <-> ACAL + NADH adh4

YMR083W 1111 adh3 alcohol dehydrogenase isoenzyme III ETHm + NADm <-> ACALm + NADHm adh3

YMR303C 1111 adh2 alcohol dehydrogenase II ETH + NAD <-> ACAL + NADH adh2

YBR145W 11 11 ADH5 alcohol dehydrogenase isoenzyme V ETH + NAD <-> ACAL + NADH adhS

YOL086C 1111 adhl Alcohol dehydrogenase I ETH + NAD <-> ACAL + NADH adhl

YDL168W 1111 SFAI Alcohol dehydrogenase I ETH + NAD <-> ACAL + NADH sfal_2

Glyoxylate and dlcarboxylate m

Glyoxal Pathway

YML004C 4415 GLOl Lacloytglutathione lyase, glyoxalase I RGT + MTHGXL <-> LGT glol

YDR272W 3126 GL02 Hydroxyacylglutathtone hydrolase LGT -> RGT + LAC glo2

YOR040W 3126 GL04 glyoxalase II (hydroxyacylglutathione hydrolase) LGTm -> RGTm + LAC glo4

Energy Metabolism

Oxidative Phosphorylation

YBR011C 3611 ippl Inorganic pyrophosphatase PPI -> 2 PI ippl

YMR267W 3611 ppa2 mitochondnal inorganic pyrophosphatase PPIm -> 2 Plm ppa2

1221 FDNG Formate dehydrogenase FOR + Qm -> QH2m + C02 +2 HEXT fdng

YML120C 1653 NDI1 ΝADH dehydrogenase (ubiquinone) NADHm + Qm -> QH2m + NADm ndil

YDL085W 1653 NDH2 Mitochondria! ΝADH dehydrogenase that catalyzes the NADH + Qm -> QH2m + NAD ndh2 oxidation of cytosohc ΝADH YMR145C 1653 NDHI Mitochondnal NADH dehydrogenase that catalyzes the NADH + Qm -> QH2m + NAD ndhl oxidation of cytosohc NADH

YHR042W 1624 NCP1 NADPH-ferπhemoprotein reductase NADPH + 2 FERIm -> NADP + 2 FEROm ncpl

YKLUlw 1351 SDH3 succinate dehydrogenase cytochrome b FADH2ιn + Qm <-> FADm + QH2m fad

YKL148c 1351 SDH1 succinate dehydrogenase cytochrome b

YLL041c 1351 SDH2 succinate dehydrogenase cytochrome b

YDR178W 1351 SDH4 succinate dehydrogenase cytochrome b

Electron Transport System Complex III

YEL024W 11022 RIP1 ubiquinol-cytochrome c reductase iron-sulfur subunit 02m + 4 FEROm + 6 Hm -> 4 FERIm cyto

Q0105 11022 CYTB ubiquinol-cytochrome c reductase cytochrome b subunit

YOR065W 11022 CYT1 ubiquinol-cytochrome c reductase cytochrome cl subunit

YBL045C I 1022 COR1 ubiquinol-cytochrome c reductase core subunit 1

YPR191W 11022 QCR1 ubiquinol-cytochrome c reductase core subunit 2

YPR191W 11022 QCR2 ubiquinol-cytochrome c reductase

YFR033C 11022 QCR6 ubiquinol-cytochrome c reductase subunit 6

YDR529C 11022 QCR7 ubiquinol-cytochrome c reductase subunit 7

YJL166W 11022 QCR8 ubiquinol-cytochrome c reductase subunit 8

YGR183C 11022 QCR9 ubiquinol-cytochrome c reductase subunit 9

YHR001W- 11022 QCR10 ubiquinol-cytochrome c reductase subunit 10

A

Electron Transport System Complex IV

Q0045 1931 COX1 cytochrome c oxidase subunit I QH2m + 2 FERIm + 15 Hm -> Qm + 2 FEROm cytr

Q0250 1931 COX2 cytochrome c oxidase subunit I

Q0275 1931 COX3 cytochrome c oxidase subunit I

YDL067C 1931 COX9 cytochrome c oxidase subunit I

YGL187C 1931 COX4 cytochrome c oxidase subunit I

YGL191W 1931 COX13 cytochrome c oxidase subunit I

YHR051W 1931 COX6 cytochrome c oxidase subunit I

YIL111W 1 31 COX5B cytochrome c oxidase subunit I

YLR038C 1 31 COX 12 cytochrome c oxidase subunit I

YLR395C 1931 COX8 cytochrome c oxidase subunit I

YMR256C 1931 COX7 cytochrome c oxidase subunit I

Y L052W 1931 COX5A cytochrome c oxidase subunit I

ATP Synthase

YBL099W 36134 ATP1 FIFO-ATPase complex, FI alpha subunit ADP + Plm -> ATPm + 3 Hm atpl

YPL271W 36134 ATP15 FIFO-ATPase complex, FI epsilon subunit

YDL004W 36134 ATP 16 F-type H+-transportιng ATPase delta chain

Q0085 36134 ATP6 FIFO-ATPase complex, FO A subunit

YBR039W 36134 ATP3 FIFO-ATPase complex, FI gamma subunit

YBR127C 36134 VMA2 H+- ATPase V 1 domain 60 KD subunit, vacuolar

YPL078C 36134 ATP4 FIFO-ATPase complex, FI delta subunit

YDR298C 36134 ATP5 FIFO-ATPase complex, OSCP subunit

YDR377W 36134 ATP17 ATP synthase complex, subunit f

YJR121 W 36 I 34 ATP2 FIFO-ATPase complex, FI beta subunit

YKL016C 36134 ATP7 FIFO-ATPase complex, FO D subunit

YLR295C 36134 ATP14 ATP synthase subunit h

Q0080 36134 ATP8 F-type H+-transportιng ATPase subunit 8

Q0130 36134 ATP9 F-type H+-transportιng ATPase subunit c

YOL077W- 36134 ATP19 ATP synthase k chain, mitochondnal

A

YPR020W 36134 ATP20 subunit G of the dimenc form of mitochondnal F1F0- ATP synthase

YLR447C 36134 VMA6 V-type H+-transportιng ATPase subunit AC39

YGR020C 36134 VMA7 V-type H+-transportιng ATPase subunit F

YKL080W 36134 VMA5 V-type H+-transportιng ATPase subunit C

YDL185W 36134 TFP 1 V-type H+-transportιng ATPase subunit A

YBR127C 36134 VMA2 V-type H+-transportιng ATPase subunit B

YOR332W 36134 VMA4 V-type H+-transportιng ATPase subunit E

YEL027W 36134 CUP5 V-type H+-transportιng ATPase proteohpid subunit

YHR026W 36134 PPA 1 V-type H+-transportιπg ATPase proteohpid subunit

YPL234C 36134 TFP3 V-type H+-transportιng ATPase proteohpid subunit

YMR054W 36134 STV 1 V-type H- ransporting ATPase subunit I

YOR270C 36134 VPH 1 V-type H+-transportιng ATPase subunit I

YEL051 36134 VMA8 V-type H+-transportιng ATPase subunit D

YHR039C-A 36134 VMA10 vacuolar ATP synthase subunit G

YPR036W 36134 VMA 13 V-type H+-transportιng ATPase 54 kD subunit

Electron Transport System. Complex IV

Q0045 1 31 COX1 cytochrome-c oxidase subunit I 4 FEROm + 02m + 6 Hm -> 4 FERIm coxl

Q0275 1931 COX3 Cytochrome-c oxidase subunit 111, mitochondnally- coded Q0250 1931 COX2 cytochrome-c oxidase subunit II

YDL067C 1931 COX9 Cytochrome-c oxidase YGLI87C 1931 COX4 cytochrome-c oxidase chain IV YGLI91W 1931 COX13 cytochrome-c oxidase chain Via YHR051W 1931 COX6 cytochrome-c oxidase subunit VI YLR395C 1 9 3 1 COX8 cytochrome-c oxidase chain VIII

YMR256C 1 9 3 1 COX7 cytochrome-c oxidase, subunit VII

YNL052W 1 9 3 1 COX5A cytochrome-c oxidase chain V A precursor

YML054C 1 1 2 3 cyb2 Lactic acid dehydrogenase 2 FERIm + LLACm -> PYRm + 2 FEROm cyb2

YDL174C 1 1 24 DLD1 mitochondnal enzyme D-lactate femcytochrome c 2 FERIm + LACm -> PYRm + 2 FEROm did 1 oxtdoreductase

Methane metabolism

YPL275W 1 2 1 2 YPL275 putative formate dehydrogenase/putative pseudogene FOR + NAD -> C02 + NADH tfo 1 a W

YPL276W 1 2 1 2 YPL276 putative formate dehydrogenase/putative pseudogene FOR + NAD -> C02 + NADH tfolb W

YOR388C 1 2 1 2 FDH1 Protein with similarity to formate dehydrogenases FOR + NAD -> C02 + NADH fdhl

Nitrogen metabolism

YBR208C 6 3 46 DUR1 urea amidoiyase containing urea carboxylase / ATP + UREA + C02 <-> ADP + PI + UREAC durl allophanate hydrolase

YBR208C 3 5 1 54 DUR1 Allophanate hydrolase UREAC -> 2 NH3 + 2 C02 dur2 YJL126W 3 5 5 1 NIT2 nitnlase ACNL -> INAC + NH3 nιt2

Sulfur metabolism (Cystein biosynthesis maybe) YJR137C 1 8 7 1 ECM17 Sulfite reductase H2S03 + 3 NADPH <-> H2S + 3 NADP ecm 17

Llpld Metabolism

Fatty acid biosynthesis YER015W 6 2 1 3 FAA2 Long-chain-fatty-acid— CoA ligase, Acyl-CoA ATP + LCCA + COA <-> AMP + PPI + ACOA faa2 synthetase

YIL009W 6 2 1 3 FAA3 Long-chaιn-fatty-acιd--CoA ligase, Acyl-CoA ATP + LCCA + COA <-> AMP + PPI + ACOA faa3 synthetase

YOR317W 6 2 1 3 FAA1 Long-chaιn-fatty-acιd--CoA ligase, Acyl-CoA ATP + LCCA + COA <-> AMP + PPI + ACOA faal synthetase

YMR246W 6 2 1 3 FAA4 Acyl-CoA synthase (long-chain fatty acid CoA ligase), ATP + LCCA + COA <-> AMP + PPI + ACOA faa4 contnbutes to activation of imported mynstate

YKR009C 1 1 1 - FOX2 3-Hydroxyacy!-CoA dehydrogenase HACOA + NAD <-> OACOA + NADH fox2b

YIL160C 2 3 1 16 poll 3-Ketoacyl-CoA thiolase OACOA + COA -> ACOA + ACCOA potl l

YPL028W 2 3 1 9 erg 10 Acetyl-CoA C-acetyltransferase, ACETOACETYL- 2 ACCOA <-> COA + AACCOA erglO

COA THIOLASE

YPL028W 2 3 1 9 erg 10 Acetyl-CoA C-acetyltransferase, ACETOACETYL- 2 ACCOAm <-> COAm + AACCOAm erg!0_2

COA THIOLASE (mitoch)

Fatty Acids Metabolism

MItochondrial type II fatty acid synthase

YKLI92C 1 6 5 3 ACP I Acyl earner protein, component of mitochondnal type II NADHm + Qm -> NADm + QH2m ACP 1 fatty acid synthase

YER061C CEM1 Beta-ketoacyl-ACP synthase, mitochondnal (3-oxoacyl-

[Acyl-carπer-protein] synthase)

YOR221C - MCT1 Malonyl CoA acyl earner protein transferase

YKL055C - OAR1 3-Oxoacyl-[acyl-camer-proteιn] reductase

YKL192C/Y 1 6 5 3/- ACP1/CE Type II fatty acid synthase ACACPm + 4 MALACPm + 8 NADPHm -> 8 TypelM

ER061C YO /-/- Ml/MCT NADPm + ClOOACPm + 4 C02m + 4 ACPm

R221C/YKL 1/OARl

055C

YKL192C/Y 1 6 5 3/- ACPI/CE Type II fatty acid synthase ACACPm + 5 MALACPm + 10 NADPHm -> 10 Typell 2

ER061C/YO /-/- Ml/MCT NADPm + Cl 20ACPm + 5 C02m + 5 ACPm

R221C/YKL 1/OARl

055C

YKL192C/Y 1 6 5 3/- ACPI/CE Type II fatty acid synthase ACACPm + 6 MALACPm + 12 NADPHm -> 12 TypeII_3

ER061 C YO /-/- Ml/MCT NADPm + C140ACPm + 6 CO2m + 6 ACPm

R221C/YKL 1/OARl

055C

YKL192C/Y 1 6 5 3/- ACPI/CE Type II fatty acid synthase ACACPm + 6 MALACPm + 1 1 NADPHm -> 1 1 TypeII_4

ER061C YO /-/- M l/MCT NADPm + CI 41ACPm+ 6 C02m + 6 ACPm

R221C/YKL 1/OARl

055C

YKL192C/Y 1 6 5 3/- ACPI/CE Type II fatty acid synthase ACACPm + 7 MALACPm + 14 NADPHm -> 14 TypellJ

ER061 C/YO /-/- M l/MCT NADPm + Cl 60ACPm + 7 C02m + 7 ACPm

R221C/YKL 1/OARl

055C

YKL192C/Y 1 6 5 3/- ACPI/CE Type II fatty acid synthase ACACPm + 7 MALACPm + 13 NADPHm -> 13 TypeII_6

ER061C/YO /-/- Ml/MCT NADPm + Cl βlACPm + 7 C02m + 7 ACPm

R221C/YKL 1/OARl

055C

YKL192C/Y 1 6 5 3/- ACP1/CE Type 11 fatty acid synthase ACACPm + 8 MALACPm + 16 NADPHm -> 16 Typell 7

ER061C YO /-/- Ml/MCT NADPm + C 180ACPm + 8 C02m + 8 ACPm

R221C YKL 1/OARl

055C

YKL192C/Y 1 6 5 3/- ACPI/CE Type II fatty acid synthase ACACPm + 8 MALACPm + 15 NADPHm -> 15 TypellJ

ER061C/YO /-/- Ml/MCT NADPm + Cl 81 ACPm + 8 C02m + 8 ACPm

R221C/YKL 1/OARl

055C

YKL192C/Y 1 6 5 3/- ACPI/CE Type II fatty acid synthase ACACPm + 8 MALACPm + 14 NADPHm -> 14 TypeII_9

ER061C/YO /-/- Ml/MCT NADPm + Cl 82 ACPm + 8 C02m + 8 ACPm

R221C/YKL l/OARI

055C

Cytosolic fatty acid synthesis YNR016C 6412 ACC I acetyl-CoA carboxylase (ACC) / biotin carboxylase ACCOA + ATP + C02 <-> MALCOA + ADP + PI accl

63414

YKL182W 42161, fas I fatly-acyl-CoA synthase, beta chain MALCOA + ACP <-> MALACP + COA fasl 1

1319,2

3138,2

3139,3

1214,2

3186

YPL231w 23185, FAS2 fatty-acyl-CoA synthase, alpha chain

111100

,23141

YKL182W 42161, fatty-acyl-CoA synthase, beta chain ACCOA + ACP <-> ACACP + COA fasl 2

1319,2

3138,2

3139,3

1214,2

3186

YER061C 23141 CEM 1 3-Oxoacyl-[acyl-camer-protein] synthase MALACPm + ACACPm -> ACPm + C02m + cem 1

30ACPm

YGR037C/Y 6412, ACBl/A b-Ketoacyl-ACP synthase (C10.0), fatty acyl CoA ACACP + 4 MALACP + 8 NADPH -> 8 NADP + c 1 OOsn NR016C/YK 6341,4 CCl/fasl/ synthase C100ACP + 4 C02 + 4 ACP L182W/YPL 23185, FAS2/ 231w 111100

,23141, 42161 YGR037C/Y 6412, ACB 1/A b-Ketoacyl-ACP synthase (C 12,0), fatty acyl CoA ACACP + 5 MALACP + 10 NADPH -> 10 NADP + cl20sn NR016C/YK 6341,4 CCl/fasl/ synthase C120ACP + 5 C02 + 5 ACP L182W/YPL 23185, FAS2/ 23 lw 111100

,23141, 42161 YGR037CY 6412, ACBl/A b-Ketoacyl-ACP synthase (C14.0) ACACP + 6 MALACP + 12 NADPH -> 12 NADP + cl40sn NR016CYK 6341,4 CCl/fasl/ C140ACP + 6CO2 + 6 ACP LI82W/YPL 23185, FAS2/ 231w 11 I 100

,23141, 42 I 61 YGR037C/Y 6412, ACBl/A b-Ketoacyl-ACP synthase I (C14.1) ACACP + 6 MALACP + 11 NADPH -> 11 NADP + cl41sy NR0I6C/YK 6341,4 CCl/fasl/ C141ACP + 6C02 + 6ACP L182W/YPL 23185, FAS2/ 231w 111100

.23141, 42161 YGR037CY 6412, ACBl/A b-Ketoacyl-ACP synthase I (C16.0) ACACP + 7 MALACP + 14 NADPH -> 14 NADP π NR016C/YK 6341,4 CCl/fasl/ C160ACP + 7CO2 + 7ACP L182WYPL 23185, FAS2/ 231w 1 I 1100

,23141, 42161 YGR037C/Y 6412, ACBl/A b-Ketoacyl-ACP synthase I (C 16, 1 ) ACACP + 7MALACP+ 13NADPH->13NADP+ clβlsy NR016C/YK 6341,4 CCl/fasl/ C161ACP + 7C02 +7 ACP L182W/YPL 23185, FAS2/ 231w 111100

,23141, 42161 YGR037CY 6412, ACB 1/A b-Ketoacyl-ACP synthase I (C 18,0) ACACP + 8 MALACP + 16 NADPH -> 16 NADP + c!80sy NR016C/YK 6341,4 CCl/fasl/ C180ACP + 8CO2 + 8ACP L182WYPL 23185, FAS2/ 231w 111100

,23141, 42161 YGR037C/Y 6412, ACBl/A b-Ketoacyl-ACP synthase I (C18.1) ACACP + 8 MALACP + 15 NADPH -> 15 NADP + clδlsv NR016C/YK 6341,4 CCl/fasl/ C181ACP + 8 C02 + 8ACP L182W/YPL 23185, FAS2/ 231w 111100

,23141, 42161 YGR037C/Y 6412. ACBl/A b-Ketoacyl-ACP synthase I (C18.2) ACACP + 8 MALACP + 14 NADPH -> 14 NADP + cl82sy NR016C/YK 6341,4 CCl/fasl/ C182ACP + 8C02 + 8ACP L182WYPL 23185, FAS2/ 23 lw 111100

,23141, 42161 YKL182W 42161 fas 1 3-hydroxypalmιtoyl-[acyl-carπer protein] dehydratase 3HPACP <-> 2HDACP fasl YKL182W 1319 fasl Enoyl-ACP reductase AACP + NAD<-> 23DAACP + NADH fasl 4 Fatty acid degradation YGL205WY 1336/2 POX1/FO Fatty acid degradation C140 + ATP + 7COA + 7 FADm + 7 NAD -> AMP cl40dg KR009C/YIL 3118 X2/POT3 + PPI + 7 FADH2m + 7 NADH + 7 ACCOA 160C

YGL205W/Y 1336/2 POX1/FO Fatty acid degradation C160 + ATP + 8 COA + 8 FADm + 8 NAD -> AMP cl60dg KR009C/Y1L 3118 X2/POT3 + PPI + 8 FADH2m + 8 NADH + 8 ACCOA 160C

YGL205W/Y 1 3 3 6/2 POX 1/FO Fatty acid degradation C180 + ATP + 9 COA + 9 FADm + 9 NAD -> AMP cl80dg

KR009C/YIL 3 I 18 X2/POT3 + PPI + 9 FADH2m + 9 NADH + 9 ACCOA

160C

Phospholipid Biosynthesis

Glycerol-3-phosphate acyltransferase GL3P + 0 017 C100ACP + 0 062 C120ACP + 0 1 Gatl 1

C140ACP + 0 27 C160ACP + 0 169 C161 ACP +

0 055 C180ACP + 0 235 C181 ACP + 0 093

C182ACP -> AGL3P + ACP

Glycerol-3-phosphate acyItransferase GL3P + 0 017 C100ACP + 0 062 C120ACP + 0 1 Gat2 1

C140ACP + 0 27 C160ACP + 0 169 C161ACP +

0 055 C180ACP + 0 235 C181 ACP + 0 093

C182ACP -> AGL3P + ACP

Glycerol-3-phosphate acyltransferase T3P2 + 0 017 C100ACP + 0 062 C I20ACP + 0 1 Gatl 2

C 140ACP + 0 27 C160ACP + 0 169 C161ACP +

0 055 Cl 80 ACP + 0 235 C181ACP + 0093

C 182ACP -> AT3P2 + ACP

Glycerol-3-phosphate acyltransferase T3P2 + 0017 C100ACP + 0062 C120ACP + 0 1 Gat2 2

C140ACP + 0 27 C160ACP + 0 169 C161ACP +

0 055 C180ACP + 0 235 C181ACP + 0093

C182ACP -> AT3P2 + ACP

Acyldihydroxyacetonephosphate reductase AT3P2 + NADPH -> AGL3P + NADP ADHAPR YDL052C 2 3 1 51 SLC1 l-Acylglycerol-3-phosphate acyltransferase AGL3P + 0 017 CI00ACP + 0 062 C120ACP + 0 100 slcl

C140ACP + 0 270 C160ACP + 0 169 C161ACP +

0 055 C180ACP + 0 235 C181ACP + 0 093

C182ACP -> PA + ACP

2 3 1 51 1 -Acylglycerol-3-phosphate acyltransferase AGL3P + 0 017 C100ACP + 0 062 C120ACP + 0 100 AGAT

C140ACP + 0 270 C160ACP + 0 169 C161ACP +

0 055 C180ACP + 0 235 C181ACP + 0 093

C182ACP -> PA + ACP

YBR029C 2 7 741 CDS1 CDP-Diacylglycerol synthetase PAm + CTPm <-> CDPDGm + PPIm eds la

YBR029C 2 7 741 CDS1 CDP-Diacylglycerol synthetase PA + CTP <-> CDPDG + PPI eds lb

YER026C 2 7 8 8 chol phosphatidylserine synthase CDPDG + SER <-> CMP + PS chol a

YER026C 2 7 8 8 chol Phosphatidylserine synthase CDPDGm + SERm <-> CMPm + PSm cholb

YGR170W 4 1 1 65 PSD2 phosphatidylserine decarboxylase located in vacuole or PS -> PE + C02 psd2

Golgi

YNL169C 4 1 1 65 PSD 1 Phosphatidylserine Decarboxylase 1 PSm -> PEm + C02m psdl

YGR157W 2 1 1 17 CH02 Phosphatidylethanolamiπe N-methyltransferase SAM + PE -> SAH + PMME cho2

YJR073C 2 1 1 16 OPI3 Methylene-fatty-acyl-phosphohpid synthase SAM + PMME -> SAH + PDME opι3J

YJR073C 2 1 1 16 OPI3 Phosphatidyl-N-methylethanolamine N- PDME + SAM -> PC + SAH opι3_2 methyltransferase

YLR133W 2 7 1 32 CKI1 Choline kinase ATP + CHO -> ADP + PCHO ckil

YGR202C 2 7 7 15 PCTI Cholinephosphate cytidylyltransferase PCHO + CTP -> CDPCHO + PPI pctl

YNL130C 2 7 8 2 CPT1 Diacylglycerol chohnephosphotransferase CDPCHO + DAGLY -> PC + CMP cptl

YDR147W 2 7 1 82 EKI1 Ethanolamine kinase ATP + ETHM -> ADP + PETHM ekil

YGR007W 2 7 7 14 MUQ1 Phosphoethanolamine cytidylyltransferase PETHM + CTP -> CDPETN + PPI ectl

YHR123W 2 7 8 1 EPT1 Ethanolaminephosphotransferase CDPETN + DAGLY <-> CMP + PE eptl

YJL153C 5 5 1 4 ino l myo-Inositol- 1 -phosphate synthase G6P -> MI 1P inol

YHR046C 3 1 3 25 INMI myo-lnositol- 1 (or 4)-monophosphatase MIlP -> MYOI + PI impal

YPRI 13W 2 7 8 1 1 PIS 1 phosphatidylinositol synthase CDPDG + MYOI -> CMP + PINS pisl

YJR066W 2 7 1 137 tori 1-Phosphatιdyhnosιtol 3-kιnase ATP + PINS -> ADP + PINSP tori

YKL203C 2 7 1 137 tor2 1 -Phosphatidylinositol 3-kιnase ATP + PINS -> ADP + PINSP tor2

YLR240W 2 7 1 137 vps34 1 -Phosphatidylinositol 3-kιnase ATP + PINS -> ADP + PINSP vps34

YNL267W 2 7 1 67 P1K1 Phosphatidylinositol 4-kιnase (PI 4-kιnase), generates ATP + PINS -> ADP + PINS4P pikl

Ptdlns 4-P

YLR305C 2 7 1 67 STT4 Phosphatidylinositol 4-kιnase ATP + PINS -> ADP + PINS4P sst4

YFR019W 2 7 1 68 FAB1 PROBABLE PHOSPHAT1DYLINOSITOL-4- PINS4P + ATP -> D45PI + ADP fabl

PHOSPHATE 5-KINASE, l-phosphatιdyhnosιtol-4- phosphate kinase

YDR208W 2 7 1 68 MSS4 Phosphatιdylιnosιtol-4-phosphate 5-kιnase, required for PINS4P + ATP -> D45PI + ADP proper organization of the actin cytoskeleton

YPL268W 3 1 4 1 1 plcl l-phosphatιdylιnosιtol-4,5-bιsphosphate D45PI -> TPI + DAGLY plcl phosphodiesterase

YCL004W 2 7 8 8 PGS1 CDP-diacylglycerol-seπne O-phosphatidyltransferase CDPDGm + GL3Pm <-> CMPm + PGPm pgsl

3 1 3 27 Phosphatidylglycerol phosphate phosphatase A PGPm -> Plm + PGm Pgpa

YDL142C 2 7 8 5 CRD1 Cardiolipin synthase CDPDGm + PGm -> CMPm + CLm crdl YDR284C DPP1 diacylglycerol pyrophosphate phosphatase PA -> DAGLY + PI dppl YDR503C LPP1 lipid phosphate phosphatase DGPP -> PA + PI Ippl

Sphingoglycoliptd Metabolism YDR062W 2 3 1 50 LCB2 Senne C-palmitoyltransferase PALCOA + SER -> COA + DHSPH + C02 Icb2 YMR296C 2 3 1 50 LCB1 Seπne C-palmitoyltransferase PALCOA + SER -> COA + DHSPH + C02 lcbl YBR265w 1 1 1 102 TSCI0 3-Dehydrosphιnganιne reductase DHSPH + NADPH -> SPH + NADP tsclO YDR297W SUR2 SYRINGOMYCIN RESPONSE PROTEIN 2 SPH + 02 + NADPH -> PSPH + NADP sur2

Ceramide synthase PSPH + C260COA -> CER2 + COA csyna

Ceramide synthase PSPH + C240COA -> CER2 + COA csynb

YMR272C SCS7 Ceramide hydroxylase that hydroxylates the C-26 fatty- CER2 + NADPH + 02 -> CER3 + NADP scs7 acyl moiety of inositol-phosphorylceramide YKL004 AUR1 IPS synthase, AUREOBAS1D1N A RESISTANCE CER3 + PINS -> IPC aurl PROTEIN

YBR036C CSG2 Protein required for synthesis of the maπnosylated IPC + GDPMAN ->MIPC csg2 sphingolipids

YPL057C SUR1 Protein required for synthesis of the mannosylated IPC + GDPMAN -> MIPC surl sphingolipids

YDR072C 2 - - - 1PT1 MIP2C synthase, MANNOSYL MIPC + PINS -> MIP2C iptl

D1PHOSPHORYLINOSITOL CERAMIDE

SYNTHASE

YOR171C LCB4 Long chain base kinase, involved in sphingohpid SPH + ATP -> DHSP + ADP lcb4J metabolism

YLR260W LCB5 Long chain base kinase, involved in sphingohpid SPH + ATP -> DHSP + ADP lcb5J metabolism

YOR171C LCB4 Long chain base kinase, involved in sphingohpid PSPH + ATP -> PHSP + ADP lcb4_2 metabolism

YLR260W LCB5 Long chain base kinase, involved in sphingohpid PSPH + ATP -> PHSP + ADP lcb5J metabolism

YJL134W LCB3 Sphingoid base-phosphate phosphatase, putative DHSP -> SPH + PI lcb3 regulator of sphingohpid metabolism and stress response

YKR053C YSR3 Sphingoid base-phosphate phosphatase, putative DHSP -> SPH + PI ysr3 regulator of sphingohpid metabolism and stress response

YDR294C DPL1 Dιhydrosphιngosιne-1-phosphate lyase DHSP -> PETHM + C16A dpi I

Sterol biosynthesis

YML126C 4135 HMGS 3-hydroxy-3-methy!glutaryl coenzyme A synthase H3MCOA + COA <-> ACCOA + AACCOA hmgs

YLR450W 11134 hmg2 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) MVL + COA + 2 NADP <-> H3MCOA + 2 NADPH hmg2 reductase isozyme

YML075C 11134 hmgl 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) MVL + COA + 2 NADP <-> H3MCOA + 2 NADPH hmgl reductase isozyme

YMR208W 27136 erg 12 mevalonate kinase ATP + MVL -> ADP + PMVL ergl2J

YMR208W 27136 erg 12 mevalonate kinase CTP + MVL -> CDP + PMVL ergl 2_2

YMR208W 27136 erg 12 mevalonate kinase GTP + MVL -> GDP + PMVL ergl23

YMR208 27136 erg 12 mevalonate kinase UTP + MVL -> UDP + PMVL ergl2_4

YMR220W 2742 ERG8 48 kDa Phosphomevalonate kinase ATP + PMVL->ADP + PPMVL erg8

YNR043W 41133 MVD1 Diphosphomevalonate decarboxylase ATP + PPMVL -> ADP + PI + IPPP + C02 mvdl

YPL117C 5332 Isopentenyl diphosphate dimethylallyl diphosphate IPPP<->DMPP idil isomerase (IPP isomerase)

YJL167W 2511 ERG20 prenyltransferase DMPP + IPPP -> GPP + PPI erg20_ 1

YJL167W 25110 ERG20 Farnesyl diphosphate synthetase (FPP synthetase) GPP + IPPP -> FPP + PPI erg20

YHR190W 25121 ERG9 Squalene synthase 2 FPP + NADPH -> NADP + SQL erg9

YGR175C 114997 ERG1 Squalene monooxygenase SQL + 02 + NADP -> S23E + NADPH ergl

YHR072W 549 7 ERG7 2,3-oxιdosqualene-lanosterol cyclase S23E->LNST erg7

YHR007c 114141 ergll cytochrome P450 lanosterol 14a-demethylase LNST + RFP + 02->IGST + OFP ergll

YNL280c 1--- ERG24 C-14 sterol reductase IGST + NADPH -> DMZYMST + NADP erg24

YGR060W 1--- ERG25 C-4 sterol methyl oxidase 302 + DMZYMST -> IMZYMST erg25 J

YGLOOlc 5331 ERG26 C-3 sterol dehydrogenase (C-4 decarboxylase) IMZYMST -> IIMZYMST + C02 erg26 1

YLRIOOC YLRIOO C-3 sterol keto reductase I1MZYMST + NADPH -> MZYMST + NADP ergl 1 2 C

YGR060w 1 --- ERG25 C-4 sterol methyl oxidase 302 + MZYMST -> IZYMST «g25_2

YGLOOlc 5331 ERG26 C-3 sterol dehydrogenase (C-4 decarboxylase) IZYMST -> IIZYMST + C02 erg26_2

YLRIOOC YLRIOO C-3 sterol keto reductase IIZYMST + NADPH -> ZYMST + NADP ergl 1 C

YML008C 21141 erg6 S-adenosyl-methιonιnedelta-24-sterol-c- ZYMST + SAM->FEST + SAH ergβ methyltransferase

YMR202W ERG2 C-8 sterol isomerase FEST->EPST erg2

YLR056w 1 - - - ERG3 C-5 sterol desaturase EPST+ 02+ NADPH -> NADP + ERTR0L erg3

YMR015C 11414 - ERG5 C-22 sterol desaturase ERTROL + 02 + NADPH -> NADP + ERTEOL erg5

YGL012w 1--- ERG4 sterol C-24 reductase ERTEOL + NADPH -> ERGOST + NADP erg4

LNST + 302 + 4 NADPH + NAD -> MZYMST + unkrxn3

C02 + 4 NADP + NADH

MZYMST + 302 + 4 NADPH + NAD -> ZYMST + unkrxn4

C02+4NADP+NADH

5335 Cholestenol αelta-isomerase ZYMST + SAM -> ERGOST + SAH cdisoa jcleotlde Metabolism

Histidine Biosynthesis

YOL061W 2761 PRS5 nbose-phosphate pyrophosphokinase R5P + ATP <-> PRPP + AMP prs5

YBL068W 2761 PRS4 πbose-phosphate pyrophosphokinase 4 R5P + ATP<->PRPP + AMP prs4

YER099C 2761 PRS2 nbose-phosphate pyrophosphokinase 2 R5P + ATP <-> PRPP + AMP prs2

YHL011C 2761 PRS3 nbose-phosphate pyrophosphokinase 3 R5 P + ATP <->PRPP + AMP prs3

YKL181W 2761 PRS1 πbose-phosphate pyrophosphokinase R5P + ATP <-> PRPP + AMP prsl

Y1R027C 3525 dall allantoinase ATN<->ATT dall

YIR029W 3534 dal2 allantoicase ATT <-> UG C + U RE A dal2

YIR032C 35319 dal3 ureidoglycolate hydrolase UGC <-> GLX + 2 NH3 + C02 dal3

Purine metabolism

YJL005W 4611 CYR1 adenylate cyclase ATP->cAMP + PPI cyrl

YDR454C 2748 GUK1 guanylate kinase GMP+ATP<->GDP + ADP gukl 1

YDR454C 2748 GUK1 guanylate kinase DGMP + ATP <-> DGDP + ADP gukl

YDR454C 2748 GUK1 guanylate kinase GMP + DATP<->GDP + DADP gukl YMR300C 24214 ade4 phosphoπbosylpyrophosp ate amidotraπsferase PRPP + GLN -> PPI + GLU + PRAM ade4

YGL234W 63413 ade5,7 glycinamide nbotide synthetase and aminoimidazole PRAM + ATP + GLY <-> ADP + PI + GAR ade5 nbotide synthetase

YDR408C 2122 ade8 glycinamide nbotide transformylase GAR + FTHF -> THF + FGAR ade8

YGR061C 6353 ade6 5'-phosphonbosylfonnyl glycinamidine synthetase FGAR + ATP + GLN -> GLU + ADP + PI + FGAM ade6

YGL234W 6331 ade5,7 Phosphoπbosylformylglycinamidecyclo-ligase FGAM + ATP -> ADP + PI + AIR ade7

YOR128C 41121 ade2 phosphon bo sy lam ino-imidazole-carboxylase CAIR <-> AIR + C02 ade2

YAR015W 6326 adel phosphonbosyl amino imidazolesuccinocarbozamide CAIR + ATP + ASP <-> ADP + PI + SAICAR adel synthetase

YLR359W 4322 ADEI3 5'-Phosρhoπbosyl-4-(N-succmocarboxamιde)-5- SAICAR <-> FUM + AICAR ade!3_l aminoimidazole lyase

YLR028C 2123 ADE16 5-amιπoιmιdazole-4-carboxamιde πbonucleotide AICAR + FTHF <-> THF + PRFICA adel6_l

(AICAR) transformylaseVIMP cyclo hydrolase

YMRI20C 2123 ADE17 5-amιnoιmιdazole-4-carboxamιdenbonucleotιde AICAR + FTHF <-> THF + PRFICA adel7_l

(AICAR) transformylaseVIMP cyclohydrolase

YLR028C 35410 ADE16 5-amιnoιmιdazole-4-carboxamιdenbonucleotιde PRFICA<->IMP adel6_2

(AICAR) transformylaseVIMP cyclohydrolase

YMR120C 2123 ADE17 IMP cyclohydrolase PRFICA <-> IMP adel7_2

YNL220 6344 adel 2 adenylosuccinate synthetase IMP + GTP + ASP -> GDP + PI + ASUC adel2

YLR359W 4322 ADE13 Adenylosuccinate Lyase ASUC <-> FUM + AMP adel3_2

YAR073W 111205 fun63 putative ιnosιne-5 '-mono phosphate dehydrogenase IMP + NAD -> NADH + XMP fun63

YHR216W 111205 pur5 puπne excretion IMP + NAD -> NADH + XMP pur5

YML056C 111205 1MD4 probable ιnosιne-5'-moπophosρhate dehydrogenase IMP + NAD -> NADH + XMP prm5

(IMP

YLR432W 111205 IMD3 probable ιnosιne-5'-monoρhosρhate dehydrogenase IMP + NAD -> NADH + XMP prm4

(IMP

YAR075W 111205 YAR075 Protein with strong similarity to ιnosιne-5'- IMP + NAD -> NADH + XMP prm6 W monophosphate dehydrogenase, frameshifted from

YAR073W, possible pseudogene

YMR217W 6352, GUA1 GMP synthase XMP + ATP + GLN -> GLU + AMP + PPI + GMP gual 6341

YML035C 3546 amdl AMP deaminase AMP -> IMP + NH3 amdl

YGL248W 31417 PDE1 3',5'-Cychc-nucleotιde phosphodiesterase, low affinity cAMP -> AMP pdel

YOR360C 31417 pde2 3',5'-Cyclιc-nucleotιde phosphodiesterase, high affinity cAMP -> AMP pde2 1

YOR360C 31417 pde2 cdAMP -> DAMP Pde2

YOR360C 31417 pde2 cIMP -> IMP pde2J

YOR360C 31417 pde2 cGMP -> GMP pde2_4

YOR360C 31417 pdc2 cCMP -> CMP pde2J

YDR530C 27753 APA2 5',5"'-P-l,P-4-tetraphosphate phosphorylase II ADP + ATP -> PI + ATRP apa2

YCL050C 27753 apal 5\5"^p-P-l,P-4-tetraphosphate phosphorylase II ADP + GTP -> PI + ATRP apal_l

YCL050C 27753 apal 5',5"'-P-l,P-4-tetraρhosphate phosphorylase II GDP + GTP -> PI + GTRP apal J

Pyrimidine metabolism

YJL130C 2132 ura2 Aspartate-carbmoyltransferase CAP + ASP -> CAASP + PI ura2J

YLR420W 3523 ura4 dihydrooratase CAASP <-> DOROA ura4

YKL216W 1331 ural dihydroorotate dehydrogenase DOROA + 02 <-> H202 + OROA ural

YKL216W 1331 PYRD Dihydroo rotate dehydrogenase DOROA + Qm <-> QH2m + OROA ural_2

YML106W 24210 URA5 Orotate phosphoπbosyltransferase 1 OROA + PRPP <-> PPI + OMP ura5

YMR271C 24210 URA10 Orotate phosphonbosyltransferase 2 OROA + PRPP <-> PPI + OMP uralO

YEL021W 41123 ur_3 orotidiπe-S'-phosp hate decarboxylase OMP -> C02 + UMP ura3

YKL024C 27414 URA6 Nucleoside-phosphate kinase ATP + UMP <-> ADP + UDP npk

YHRI28W 2429 furl UPRTase, Uracil phosphonbosyltransferase URA + PRPP -> UMP + PPI furl

YPR062W 3541 FCY1 cytosine deaminase CYTS -> URA + NH3 fcyl 27121 Thymidine (deoxyuπdine) kinase DU + ATP -> DUMP + ADP tdkl 27121 Thymidine (deoxyuπdine) kinase DT + ATP -> ADP + DTMP tdk2

YNR012W 27148 URK1 Undine kinase URI + GTP->UMP + GDP urklj

YNR012W 27148 URK1 Cytodiπe kinase CYTD + GTP -> GDP + CMP urklj

YNR012W 27148 URK1 Undine kinase, converts ATP and undine to ADP and URI + ATP -> ADP + UMP urkl

UMP

YLR209C 2424 PNP1 Protein with similarity to human purine nucleoside DU + PI<-> URA + DRIP deo l phosphorylase, Thymidine (deoxyuπdine) phosphorylase, Purine nucleotide phosphorylase

YLR209C 2424 PNP1 Protein with similarity to human purine nucleoside DT + PI<-> TH Y + DR 1 P deoa2 phosphorylase, Thymidine (deoxyuπdiπe) phosphorylase

YLR245C 3545 CDD1 Cytidine deaminase CYTD -> URI + NH3 cddl

YLR245C 3545 CDD1 Cytidine deaminase DC->NH3 + DU cddl_2

YJR057W 2749 cdc8 dTMP kinase DTMP + ATP <-> ADP + DTDP cdc8

YDR353W 1645 TRR1 Thioredoxin reductase OTHIO + NADPH -> NADP + RTHIO trrl

YHR106W 1645 TRR2 mitochondnal thioredoxin reductase OTHlOm + NADPHm -> NADPm + RTHIOm trr2

YBR252W 36123 DUT1 dUTP pyrophosphatase (dUTPase) DUTP -> PPI + DUMP dull

YOR074C 21145 cdc21 Thymidylate synthase DUMP + METTHF -> DHF + DTMP cdc21

- 27414 Cytidylate kinase DCMP + ATP <-> ADP + DCDP crnkal

- 27414 Cytidylate kinase CMP + ATP <-> ADP + CDP cmka2

YHR144C 35412 DCD1 dCMP deaminase DCMP <-> DUMP + NH3 dcdl

YBL039C 6342 URA7 CTP synthase, highly homologus to URA8 CTP UTP + GLN + ATP -> GLU + CTP + ADP + PI ura7J synthase

YJR103W 6342 URA8 CTP synthase UTP + GLN + ATP ■> GLU + CTP + ADP + PI ura8J

YBL039C 6342 URA7 CTP synthase, highly homologus to URA8 CTP ATP + UTP + NH3 -> ADP + PI + CTP ura72 synthase

YJR103W 6342 URA8 CTP synthase ATP + UTP + NH3 -> ADP + PI + CTP ura8 2 YNL292 42170 PUS4 Pseudouridine synthase URA + R5P <-> PURI5P pus4 YPL212C 42170 PUS1 intranuclear protein which exhibits a nucleotide-specific URA + R5P <-> PURI5P pusl mtron-dependent tRNA pseudouridine synthase activity

YGL063W 42170 PUS2 pseudouridine synthase 2 URA + R5P <-> PURI5P pus2

YFL001W 42170 degl Similar to rRNA methyltransferase (Caenorhabditis URA + R5P <-> PURI5P degl elegans) and hypothetical 28K protein (alkaline endoglucanase gene 5' region) from Bacillus sp

Salvage Pathways

YML022W 2427 APT1 Adenine phosphonbosyltransferase AD + PRPP -> PPI + AMP aptl

YDR441C 2427 APT2 similar to adenine phosphonbosyltransferase AD + PRPP -> PPI + AMP apt2

YNL141W 3544 AAH1 adenine aminohydrolase (adenine deaminase) ADN -> INS + NH3 aahla

YNL141W 3544 AAHI adenine aminohydrolase (adenine deaminase) DA->DIN + NH3 aahlb

YLR209C 2421 PNP1 Purine nucleotide phosphorylase, Xanthosme DIN + PI <-> HYXN + DRIP xapal phosphorylase

YLR209C 2421 PNP1 Xanthosme phosphorylase, Purine nucleotide DA+PK->AD + DR1P xapa2 phosphorylase

YLR209C 2421 PNP1 Xanthosme phosphorylase DG + PK->GN + DR1P xapa3

YLR209C 2421 PNP1 Xanthosme phosphorylase, Purine nucleotide HYXN + R1P<->INS + PI xapa4 phosphorylase

YLR209C 2421 PNP1 Xanthosme phosphorylase, Purine nucleotide AD + R1P<->PI + ADN xapa5 phosphorylase

YLR209C 2421 PNP1 Xanthosme phosphorylase, Purine nucleotide GN + R1P<->PI + GSN xapa6 phosphorylase YLR209C 2421 PNP1 Xanthosme phosphorylase, Purine nucleotide XAN + RIP <-> PI + XTSINE xapa7 phosphorylase

YJR133W 24222 XPT1 Xanthine-guamne phosphonbosyltransferase XAN + PRPP -> XMP + PPI gpti

YDR400W 3221 urhl Purine nucleosidase GSN -> GN + RIB pur21

YDR400W 3221 urhl Purine nucleosidase ADN -> AD + RIB purl 1

YJR105W 27120 YJR105 Adenosine kinase ADN + ATP -> AMP + ADP prm2

W

YDR226W 2743 adkl cytosohc adenylate kinase ATP + AMP <-> 2 ADP adkl

YDR226W 2743 adkl cytosohc adenylate kinase GTP + AMP <-> ADP + GDP adkl

YDR226W 2743 adkl cytosohc adenylate kinase ITP + AMP <-> ADP + 1DP adkl 3

YER170W 2743 ADK2 Adenylate kinase (mitochondnal GTP AMP ATPm + AMPm <-> 2 ADPm adk2 1 phosphotransferase)

YER170W 2743 adk2 Adenylate kinase (mitochondnal GTP AMP GTPm + AMPm <-> ADPm + GDPm adk2J phosphotransferase)

YER170W 2743 adk2 Adenylate kinase (mitochondnal GTP AMP ITPm + AMPm <-> ADPm + IDP adk23 phosphotransferase)

YGR180C 11741 RNR4 ribonucleotide reductase, small subunit (alt), beta chain

YIL066C 11741 RNR3 Ribonucleotide reductase (nbonucleoside-diphosphate ADP + RTHIO -> DADP + OTHIO rnr3 reductase) large subunit, alpha chain

YJL026W 11741 rnr2 small subunit of ribonucleotide reductase, beta chain

YKL067W 2746 YNK1 Nucleoside-diphosphate kinase UDP + ATP <-> UTP + ADP ynkl

YKL067W 2746 YNK1 Nucleoside-diphosphate kinase CDP + ATP <-> CTP + ADP ynkl

YKL067 2746 YNK1 Nucleoside-diphosphate kinase DGDP + ATP <-> DGTP + ADP ynklj

YKL067W 2746 YNK1 Nucleoside-diphosphate kinase DUDP + ATP <-> DUTP + ADP ynkl_4

YKL067W 2746 YNK1 Nucleoside-diphosphate kinase DCDP + ATP <-> DCTP + ADP ynklj

YKL067W 2746 YNK1 Nucleoside-diphosphate kinase DTDP + ATP <-> DTTP + ADP ynkl_6

YKL067W 2746 YNK1 Nucleoside-diphosphate kinase DADP + ATP <-> DATP + ADP ynklj

YKL067W 2746 YNK1 Nucleoside diphosphate kinase GDP + ATP <-> GTP + ADP ynklj

YKL067W 2746 YNK1 Nucleoside diphosphate kinase IDP + ATP <-> ITP + IDP ynklj

- 27411 Adenylate kinase, dAMP kinase DAMP + ATP <-> DADP + ADP dampk

YNL141W 3542 AAHI Adenine deaminase AD -> NH3 + HYXN yicp

- 27173 Inosine kinase INS + ATP -> IMP + ADP gskl

- 27173 Guanosine kinase GSN + ATP -> GMP + ADP gsk2

YDR399W 2428 HPT1 Hypoxanthine phosphonbosyltransferase HYXN + PRPP -> PPI + IMP hptl

YDR399W 2428 HPT1 Hypoxanthine phosphonbosyltransferase GN + PRPP -> PPI + GMP hptl

- 2423 Undine phosphorylase URI + PK->URA + R1P udp

YKL024C 214- URA6 Uπdylate kinase UMP + ATP <-> UDP + ADP pyrhl

YKL024C 214- URA6 Undylate kinase DUMP + ATP <-> DUDP + ADP pyrh2

- 32210 CMP glycosylase CMP -> CYTS + R5P cmpg

YHR144C 35413 DCDI dCTP deaminase DCTP -> DUTP + NH3 dcd

- 3135 S'-Nucleotidase DUMP -> DU + PI ushal

- 3135 5'-Nucleotιdase DTMP -> DT + PI usha2

- 3135 5'-Nucleotιdase DAMP -> DA + PI usha3

- 3135 5'-Nucleotιdase DGMP -> DG + PI usha4

- 3135 5-Nucleotιdase DCMP -> DC + PI usha5

. 3135 5-Nucleotιdase CMP -> CYTD + PI usha6

. 3135 5'-Nucleotιdase AMP -> PI + ADN usha7

- 3135 5'-Nucleotιdase GMP->PI + GSN ushaδ

- 3135 5'-Nuclcotιdase IMP -> PI + INS usha9

- 3135 5'-Nucleotιdase XMP -> PI + XTSINE ushal 2

- 3135 S'-Nucleotidase UMP->PI + URI ushal 1

YER070W 11741 RNR1 Ribonucleoside-diphosp hate reductase ADP + RTHIO -> DADP + OTHIO rnrl 1 YER070W 1 174 1 RNRI Riboπucleoside-diphosphate reductase GDP + RTHIO -> DGDP + OTHIO rnrl

YER070W 1 174 1 RNRI Ribonucleoside-diphosphate reductase CDP + RTHIO -> DCDP + OTHIO rnrlj

YER070W 1 174 1 RNRI Ribonucleoside-diphosphate reductase UDP + RTHIO -> OTHIO + DUDP rnrlj 1 174 2 Ribonucleoside-tnphosphate reductase ATP + RTHIO -> DATP + OTHIO nrddl 1 174 2 Ribonucleoside-tnphosphate reductase GTP + RTHIO -> DGTP + OTHIO nrdd2 1 174 2 Ribonucleoside-tnphosphate reductase CTP + RTHIO -> DCTP + OTHIO nrdd3 1 17 4 2 Ribonucleoside-tnphosphate reductase UTP + RTHIO -> OTHIO + DUTP nrdd4 3 6 1 - Nucleoside tπphosphatase GTP -> GSN + 3 PI muttl 3 6 1 - Nucleoside tnphosphatase DGTP -> DG + 3 PI mutt2

YML035C 3 2 24 A AMMDD11 AMP deaminase AMP -> AD + R5P amn

YBR284W 3 2 24 Y YBBRR228844 Protein with similarity to AMP deaminase AMP -> AD + R5P amnl

W

YJL070C 3 2 24 YJL070C Protein with similarity to AMP deaminase AMP -> AD + R5P amn2 Amino Acid Metabolism

Glutamate Metabolism (Amfnosugars met) YMR250W 4 1 1 15 GAD1 Glutamate decarboxylase B GLU -> GABA + C02 btn2 YGR019W 26 1 19 ugal Aminobutyrate aminotransaminase 2 GABA + AKG -> SUCCSAL + GLU ugal YBR006W 1 2 1 16 YBR006 Succinate semialdehyde dehydrogenase -NADP SUCCSAL + NADP -> SUCC + NADPH gabda

YKL104C 26 1 16 GFA1 Glutamine ructose-6-phosphate amidotransferase F6P + GLN -> GLU + G A6P gfa 1

(glucoseamine 6-phosphate synthase)

YFL017C 23 1 4 GNA1 Glucosamine-phosphate N-acetyltransferase ACCOA + GA6P <-> COA + NAGA6P gnal

YEL058W 5 4 2 3 PCM1 Phosphoacetylglucosamine Mutase NAG A 1 P <-> N AG A6P pcm 1 a

YDL103C 2 7 7 23 QRI1 N-Acetylglucosamιne-1-phosphate-uπdyltransferase UTP + NAGA1P <-> UDPNAG + PPI qπl

YBR023C 24 1 16 chs3 chitin synthase 3 UDPNAG -> CHIT + UDP chs3

YBR038W 24 1 16 CHS2 chitin synthase 2 UDPNAG -> CHIT + UDP chs2

YNL192W 24 1 16 CHS1 chitin synthase 2 UDPNAG -> CHIT + UDP chsl

YHR037W 1 5 1 12 put2 delta- l-pyrrolιne-5-carboxylate dehydrogenase GLUGSALm + NADPm -> NADPHm + GLUm put2 1

P5Cm + NADm -> NADHm + GLUm put2

YDL171C 1 4 1 14 GLT1 Glutamate synthase (NADH) AKG + GLN + NADH -> NAD + 2 GLU gltl YDL2I5C 1 4 1 4 GDH2 glutamate dehydrogenase GLU + NAD -> AKG + NH3 + NADH gdh2 YAL062W 1 4 1 4 GDH3 NADP-linked glutamate dehydrogenase AKG + NH3 + NADPH <-> GLU + NADP gdh3 YOR375C 1 4 1 4 GDH1 NADP-speαfic glutamate dehydrogenase AKG + NH3 + NADPH <-> GLU + NADP gdhl YPR035W 63 1 2 glnl glutamine synthetase GLU + NH3 + ATP -> GLN + ADP + PI glnl YEL058W 5 4 2 3 PCM1 Phosphoglucosamine mutase GA6P <-> GA1P pcm lb 3 5 1 2 Glutaminase A GLN -> GLU + NH3 glnasea 3 5 1 2 Glutamiπase B GLN -> GLU + NH3 glnaseb

Glucosamlπe

5 3 1 10 Glucosamιne-6 phosphate deaminase GA6P -> F6P + NH3 nagb

Arabinose

YBR149W 1 1 1 117 ARA1 D-arabinose 1 -dehydrogenase (N AD(P)+) ARAB + NAD -> ARABLAC + NADH aral

YBR149W 1 1 1 117 ARA1 D-arabinose 1 -dehydrogenase (N AD(P)+) ARAB + NADP -> ARABLAC + NADPH aralj

Xylose

YGR194C 2 7 1 17 XKS1 Xylulokinase XUL + ATP -> X5P + ADP xksl

Mannitol

1 1 1 17 Mannιtol-1 -phosphate 5-dehydrogenase MNT6P + NAD <-> F6P + NADH mtld Alanine and Aspartate YKL106W 2 6 1 1 A ATI Asparate transaminase OAm + GLUm <-> ASPm + AKGm aatl 1

YLR027C 2611 AAT2 Asparate transaminase OA + GLU <-> ASP + AKG aat2 J YAR035W 2317 YAT1 Camitiπe O-acetyltransferase COAm + ACARm -> ACCOAm + CARm yatl YML042W 2317 CAT2 Carnitine O-acetyltransferase ACCOA + CAR -> COA + ACAR cat2 YDRU IC 2612 YDR111 putative alanine transaminase PYR + GLU <-> AKG + ALA alab

C

YLR089C 2 6 1 2 YLR089 alanine aminotransferase, mitochondnal precursor PYRm + GLUm <-> AKGm + ALAm cfx2

C (glutamic—

YPR145W 63 5 4 ASN1 asparagine synthetase ASP + ATP + GLN -> GLU + ASN + AMP + PPI asnl

YGR124W 63 5 4 ASN2 asparagine synthetase ASP + ATP + GLN -> GLU + ASN + AMP + PPI asn2

YLL062C 2 1 1 10 MHT1 Putative cobalamin-dependent homocysteine S- SAM + HCYS -> SAH + MET mhtl methyltransferase, Homocysteine S-methyltransferase

YPL273W 2 1 1 10 SAM4 Putative cobalamin-dependent homocysteine S- SAM + HCYS -> SAH + MET sam4 methyltransferase

Asparagine YCR024c 6 1 1 22 YCR024C asn-tRNA synthetase, mitochondnal ATPm + ASPm + TRNAm -> AMPm + PPIm + rnas ASPTRNAm

YHR019C 1 1 23 DED81 asn-tRNA synthetase ATP + ASP + TRNA -> AMP + PPI + ASPTRNA ded81

YLR155C 5 1 I ASP3-1 Asparaginase, extracellular ASN -> ASP + NH3 asp3 J

YLR157C 5 1 1 ASP3-2 Asparaginase, extracellular ASN -> ASP + NH3 asp3 J

YLR158C 5 1 1 ASP3-3 Asparaginase, extracellular ASN -> ASP + NH3 asp3~3

YLR160C 5 1 1 ASP3-4 Asparaginase, extracellular ASN -> ASP + NH3 asp3 J

YDR321W 5 1 1 aspl Asparaginase ASN -> ASP + NH3 aspl

Glycine, serine and threonine metabolism

YER081W 1 1 1 95 ser3 Phosphoglycerate dehydrogenase 3PG + NAD -> NADH + PHP ser3

Y1L074C 1 1 1 95 ser33 Phosphoglycerate dehydrogenase 3PG + NAD -> NADH + PHP ser33

YORI84W 2 6 1 52 serl phosphosenne transaminase PHP + GLU -> AKG + 3PSER serl

YGR208W 3 1 3 3 ser2 phosphosenne phosphatase 3PSER -> PI + SER ser2~

YBR263W 2 1 2 1 SHM1 Glycine hydroxymethyltransferasc THFm + SERm <-> GLYm + METTHFm shm 1

YLR058C 2 1 2 1 SHM2 Glycine hydroxymethyltransferase THF + SER <-> GLY + METTHF shm2 YFL030W 2 6 1 44 YFL030 Putative alanine glyoxylate aminotransferase (seπne ALA + GLX <-> PYR + GLY agt W pyruvate aminotransferase)

YDR019C 2 1 2 10 GCV1 glycine cleavage T protein (T subunit of glycine GLYm + THFm + NADm -> METTHFm + NADHm gcvl 1 decarboxylase complex + C02 + NH3

YDR019C 2 1 2 10 GCV1 glycine cleavage T protein (T subunit of glycine GLY + THF + NAD -> METTHF + NADH + C02 + gcvl 2 decarboxylase complex NH3

YER052C 2 7 2 4 hom3 Aspartate kinase, Aspartate kinase I, II, III ASP + ATP -> ADP + BASP hom3

YDR158W 1 2 1 1 1 hom2 aspartic beta semi-aldehyde dehydrogenase, Aspartate BASP + NADPH -> NADP + PI + ASPSA hom2 semialdehyde dehydrogenase

YJR139C 1 1 1 3 hom6 Homosenne dehydrogenase I ASPSA + NADH -> NAD + HSER hom6_l

YJR139C 1 1 1 3 hom6 Homosenne dehydrogenase I ASPSA + NADPH -> NADP + HSER hom6_2

YHR025W 2 7 1 39 thrl homosenne kinase HSER + ATP -> ADP + PHSER thrl

YCR053 4 2 99 2 thr4 threonine synthase PHSER -> PI + THR thr4_l

YGR155W 4 2 1 22 CYS4 Cystathiomne beta-synthase SER + HCYS -> LLCT cys4

YEL046C 4 1 2 5 GLY1 Threonine Aldolase GLY + ACAL -> THR

YMR189W 1 4 4 2 GCV2 Glycine decarboxylase complex (P-subumt), glycine GLYm + LIPOm <-> SAPm + CQ2m gcv2 synthase (P-subunit), Glycine cleavage system (P- subunit)

YCL064C 4 2 1 16 chal threonine deaminase THR -> NH3 + OBUT chal

YER086W 4 2 1 16 ilvl L-Seπne dehydratase THRm -> NH3m + OBUTm llv 1

YCL064C 4 2 1 13 chal catabo c seπne (threonine) dehydratase SER -> PYR + NH3 chal_2

YIL167W 4 2 1 13 YD.167 catabo c serine (threonine) dehydratase SER -> PYR + NH3 sdll W

- 1 1 1 103 Threonine dehydrogenase THR + NAD -> GLY + AC + NADH tdh 1 c

Methionine metabolism

YFR055W 4 4 1 8 YFR055 Cystathioniπe-b-lyase LLCT -> HCYS + PYR + NH3 mete

W

YER043C 3 3 1 1 SAH1 putative S-adenosyl-L-homocysteine hydrolase SAH -> HCYS + ADN sahl YER091C 2 1 1 14 met6 vitamin B 12-(cobalamιn)-ιndepeπdeπt isozyme of HCYS + MTHPTGLU -> THPTGLU + MET met6 methionine synthase (also called N5- methyltetrahydrofolate homocysteine methyltransferase or 5-methyltetrahydropteroyl trtglutamate homocysteine methyltransferase)

- 2 1 1 13 Methionine synthase HCYS + MTHF -> THF + MET met6_2

YAL012W 44 1 1 cys3 cystathiomne gamma-lyase LLCT -> CYS + NH3 + OBUT cys3

YNL277W 23 1 31 met2 homosenne 0-trans-acetyI se ACCOA + HSER <-> COA + OAHSER met2

YLR303W 42 99 10 MET17 O-Acetylhomoserine (thιol)-lyase OAHSER + METH -> MET + AC metl 7_l

YLR303W 42 99 8 MET17 O-Acetylhomoseπne (thiol)- lyase OAHSER + H2S -> AC + HCYS metl7_2

YLR303W 42 99 8, met 17 O-acetylhomosenne sulfhydrylase (OAH SHLase), OAHSER + H2S -> AC + HCYS met!7_3 4 2 99 10 converts O-acetylhomoseπne into homocysteine

YML082W 4 2 99 YML082 putative cystathiomne gamma-synthase OSLHSER <-> SUCC + OBUT + NH4 metl7h W

YDR502C 2 5 1 6 sam2 S-adenosylmethionine synthetase MET + ATP -> PPI + PI + SAM sam2

YLR180 2 5 1 6 saml S-adenosylmethiomne synthetase MET + ATP -> PPI + PI + SAM sam 1

YLR172C 2 1 1 98 DPH5 Diphthine synthase SAM + CALH -> SAH + DPTH dph5

Cysteine Biosynthesis

YJR010W 27 74 met3 ATP sulfurylase SLF + ATP -> PPI + APS met3

YKL001C 27 1 25 met 14 adenylyl sulfate kinase APS + ATP -> ADP + PAPS metl4

YFR030W 1 8 1 2 met 10 sulfite reductase H2S03 + 3 NADPH <-> H2S + 3 NADP metlO 2 3 1 30 Serine transacetylase SER + ACCOA -> COA + ASER cysl

YGR012W 4 2 998 YGR012 putative cysteine synthase (O-acetylsenne ASER + H2S -> AC + CYS sul 1 1 W sulfhydrylase) (O-

YOL064C 3 1 3 7 MET22 3' - 5' Bisphosphate nucleotidase PAP -> AMP + PI met22

YPR167C 1 8 994 MET16 PAPS Reductase PAPS + RTHIO -> OTHIO + H2S03 + PAP metlβ

YCL050C 2 7 75 apal diadenosme 5',5"'-Pl ,P4-tetraphosphate phosphorylase I ADP + SLF <-> PI + APS apal_2

Branched Chain Amino Acid Me abollsm (Valine, Lcuclne and Isoleuclne)

YHR208W 2 6 1 42 BAT1 Branched chain ammo acid aminotransferase OICAPm + GLUm <-> AKGm + LEUm batl_l

YHR208W 2 6 1 42 BAT1 Branched chain ammo acid aminotransferase OMVALm + GLUm <-> AKGm + ILEm batl_2

YJR148W 2 6 1 42 BAT2 branched-chain ammo acid transaminase, highly similar OMVAL + GLU <-> AKG + ILE bat2 1 to mammalian ECA39, which is regulated by the oncogene myc

YJR148W 2 6 1 42 BAT2 Branched chain ammo acid aminotransferase OIVAL + GLU <-> AKG + VAL bat2_2

YJRI48W 26 1 42 BAT2 braπched-chatn amino acid transaminase, highly similar OICAP + GLU <-> AKG + LEU bat2 3 to mammalian ECA39, which is regulated by the oncogene myc

YMR108W 4 1 3 18 ιlv2 Acetolactate synthase, targe subunit OBUTm + PYRm -> ABUTm + C02m ιlv2_I

YCL009C 4 1 3 18 ILV6 Acetolactate synthase, small subunit

YMR108 4 1 3 18 ιlv2 Acetolactate synthase, large subunit 2 PYRm -> C02m + ACLACm ιlv2_2

YCL009C 4 1 3 18 ILV6 Acetolactate synthase, small subunit

YLR355C 1 1 1 86 ιlv5 Keto-acid reductotsomerase ACLACm + NADPHm -> NADPm + DHVALm ιlv5_l

YLR355C 1 1 1 86 ιlv5 Keto-acid reductotsomerase ABUTm + NADPHm -> NADPm + DHMVAm ιlv5_2

YJR016C 42 1 9 ιlv3 Dihydroxy acid dehydratase DHVALm -> OIVALm ιlv3_l

YJR0I6C 42 1 9 ιlv3 Dihydroxy acid dehydratase DHMVAm -> OMVALm ιlv3~2

YNL104C 4 1 3 12 LEU4 alp ha-isop ropy (malate synthase (2 -Isopropyl malate ACCOAm + OIVALm -> COAm + IPPMALm Ieu4 Synthase)

YGL009C 4 2 1 33 leul Isopropylmalate isomerase CBHCAP <-> IPPMAL leul_l

YGL009C 4 2 1 33 leul isopropylmalate isomerase PPMAL <-> IPPMAL leul_2

YCL018W I 1 1 85 Ieu2 beta-IPM (isopropylmalate) dehydrogenase IPPMAL + NAD -> NADH + OICAP + C02 Ieu2 Lysine biosynthesis/degradation

4.2 1 79 2-Mcthylcιtrate dehydratase HCITm <-> HACNm Iys3

YDR234W 4 2 1 36 lys4 Homoacomtate hydratase HICITm <-> HACNm Iys4

YIL094C I 1 1 155 LYSI 2 Homoisocitrate dehydrogenase (Strathem 1 1 1 87) HICIT + NADm <-> OXA + C02m + NADHm Iysl2 non-enzymatic OXAm <-> C02m + AKAm lysl 2b

2 6 1 39 2-Amιnoadιpate transaminase AKA + GLU <-> AMA + AKG amit

YBR1 15C 1 2 1 31 lys2 L-Aminoadipate-semialdehyde dehydrogenase, large AMA + NADPH + ATP -> AMASA + NADP + AMP lys2 subunit + PPI

YGLI 54C 1 2 1 31 lys5 L-Aminoadipate-semialdehyde dehydrogenase, small subunit

YBR1 15C 1 2 1 31 lys2 L-Aminoadipate-semialdehyde dehydrogenase, large AMA + NADH + ATP -> AMASA + NAD + AMP + lys2 J subunit PPI

YGL154C 1 2 1 31 lys5 L-Aminoadipate-semialdehyde dehydrogenase, small subunit

YNR050C 1 5 1 10 Iys9 Saccharopine dehydrogenase (NADP+, L-glutamate GLU + AMASA + NADPH <-> SACP + NADP lys9 forming)

YIR034C 1 5 1 7 lysl Saccharopine dehydrogenase (NAD+, L-lysme forming) SACP + NAD <-> LYS + AKG + NADH lysla

YDR037W 6 1 1 6 krsl lysyl-tRNA synthetase, cytosohc ATP + LYS + LTRNA -> AMP + PPI + LLTRNA krsi

YNL073W 6 1 1 6 mskl lysyl-tRNA synthetase, mitochondria] ATPm + LYSm + LTRN Am -> AMPm + PPIm + msk 1 LLTRNAm

YDR368W I I I - YPR1 similar to aldo-keto reductase Arginine metabolism

YMR062C 2 3 1 1 ECM40 Amino-acid N-acetyltransferase GLUm + ACCOAm -> COAm + NAGLUm ecm40 1

YER069W 2 7 2 8 arg5 Acetylglutamate kinase NAGLUm + ATPm -> ADPm + NAGLUPm arg6

YER069W I 2 1 38 arg5 N-acetyl-gamma-glutamyl-phosphate reductase and NAGLUPm + NADPHm -> NADPm + Plm + arg5 acetylglutamate kinase NAGLUSm

YOL140W 2 6 1 1 1 Acetylomithiπe aminotransferase NAGLUSm + GLUm -> AKGm + NAORNm arg8

YMR062C 2 3 1 35 ECM40 Glutamate N-acetyltransferase NAORNm + GLUm -> ORNm + NAGLUm ecm40J

YJL130C 6 3 5 5 ura2 carbamoyi-phophate synthetase, aspartate GLN + 2 ATP + C02 -> GLU + CAP + 2 ADP + PI ura2 2 transcarbamylase, and glutamine amidotransferase

YJRI09C 6355 CPA2 carbamyl phosphate synthetase, large chain GLN + 2 ATP + C02 -> GLU + CAP + 2 ADP + PI cpa2 YOR303W 6355 cpal Carbamoyl phosphate synthetase, samll chain, arginine specific

YJL088W 2133 arg3 Ornithine carbamoyltransferase ORN + CAP -> CITR + PI arg3 YLR438W 26113 car2 Ornithine transaminase ORN + AKG -> GLUGSAL + GLU car2 YOL058W 6345 argl arginosuccinate synthetase CITR + ASP + ATP <-> AMP + PPI + ARGSUCC argl YHR018C 4321 arg4 argiπinosuccinate lyase ARGSUCC <-> FUM + ARG arg4 YKL184W 41 I 17 spel Ornithine decarboxylase ORN - PTRSC + C02 spel YOL052C 41 I 50 spe2 S-adenosylmethionine decarboxylase SAM <-> DSAM + C02 spe2 YPR069C 25116 SPE3 putrescine aminopropyltransferase (sper idine PTRSC + SAM -> SPRMD + 5MTA spe3 synthase)

YLR146C 2 5 1 22 SPE4 Spermine synthase DSAM + SPRMD -> 5MTA + SPRM spe4 YDR242W 3 5 1 4 AMD2 Amidase GBAD -> GBAT + NH3 amd2J YMR293C 3 5 1 4 YMR293 Probable Amidase GBAD -> GBAT + NH3 amd

C

YPL1 11W 3 5 3 1 carl arginase ARG -> ORN + UREA carl YDR341C 6 1 1 19 YDR341 argiπyl-tRNA synthetase ATP + ARG + ATRNA -> AMP + PPI + ALTRNA atrna

C

YHR091C 6 1 1 19 MSR 1 arginyl-tRNA synthetase ATP + ARG + ATRNA -> AMP + PPI + ALTRNA msrl YHR068W 1 5 99 6 DYS1 deoxyhypusme synthase SPRMD + Qm -> DAPRP + QH2m dysl Histidine metabolism YER055C 2 4 2 17 hisl ATP phosphonbosyltransferase PRPP + ATP -> PPI + PRBATP hisl YCL030C 3 6 1 31 hιs4 phosphoπbosyl-AMP cyclohydrolase / phosphoπbosyl- PRBATP -> PPI + PRBAMP hιs4J ATP pyrophosphohydrolase / histidinol dehydrogenase

YCL030C 3 5 4 19 hιs4 histidmol dehydrogenase PRBAMP -> PRFP hιs4J Y1L020C 5 3 1 16 hιs6 phosphoπbosyl-5-amιno-l-phosphoπbosyl-4- PRFP -> PRLP hιs6 imidazolecarboxiamide isomerase

YOR202W 4 2 1 19 hιs3 imidazoleglycerol-phosphate dehydratase DIMGP -> IMACP hιs3

YIL1 16W 2 6 1 9 hιs5 histidinol-phosphate aminotransferase IMACP + GLU -> AKG + HISOLP hιs5

YFR025C 3 1 3 15 hιs2 Histidinolphosphatase HISOLP -> PI + HISOL hιs2

YCL030C 1 1 1 23 hιs4 phosphoπbosyl-AMP cyclohydrolase / phosphoπbosyl- HISOL + 2 NAD -> HIS + 2 NADH hιs4 3 ATP pyrophosphohydrolase / histidinol dehydrogenase

YBR248C 2 4 2 - hιs7 glutamine amidotransferase cyclase PRLP + GLN -> GLU + AICAR + DIMGP hιs7 YPR033C 6 1 htsl histidyl-tRNA synthetase ATP + HIS + HTRNA -> AMP + PPI + HHTRNA htsl YBR034C 2 I hmtl hnRNP arginine N-methyltransferase SAM + HIS -> SAH + MHIS hmtl YCL054W 2 1 spbl putative RNA methyltransferase YML1 10C 2 1 coq5 ubiquinone biosynthesis methlytraπsferase COQ5 YOR201C 2 1 pet56 rRNA (guanosιne-2'-0-)-methyltransferase YPL266W 2 1 diml dimethyladenosine transferase

Phenylalanlne, tyrosine and tryptophan biosynthesis (Aromatic Amino Acids) YBR249C 4 1 2 15 AR04 3-deoxy-D-arabιno-heptulosonate 7-phosphate (DAHP) E4P + PEP -> PI + 3DDAH7P aro4 synthase isoenzyme

YDR035W 4 I 2 15 AR03 DAHP synthase\, a k phospho-2-dehydro-3- E4P + PEP -> PI + 3DDAH7P aro3 deoxyhcptonate aldolase, phenylalanιne-ιnhιbιted\, phospho-2-keto-3-deoxyheptonate aldolase\, 2-dehydro- 3-deoxyphosphoheptonate aldolaseV, 3-deoxy-D- arabιne-heptulosonate-7 -phosphate synthase

YDR127W 4 6 1 3 aro l pentafunctional arom polypeptide (contains 3- 3DDAH7P -> DQT + PI dehydroquinate synthase, 3-dehydroquιπate dehydratase

(3-dehydroquιnase), shikimate 5 -dehydrogenase, shikimate kinase, and epsp synthase)

YDR127W 42 1 10 arol 3 -Dehydroquinate dehydratase DQT -> DHSK arol

YDR127W 1 1 1 25 arol Shikimate dehydrogenase DHSK + NADPH -> SME + NADP arol J

YDR127W 27 1 71 arol Shikimate kinase I, II SME + ATP -> ADP + SME5P arol J

YDR127W 25 1 19 arol 3-Phosphoshιkιmate-l-carboxyvιnyltransferase SME5P + PEP -> 3PSME + PI arol J

YGL148W 46 1 4 aro2 Choπsmate synthase 3PSME -> PI + CHOR aro2

YPR060C 54 9 5 aro7 Chonsmate mutase CHOR -> PHEN aro7

YNL316C 42 1 51 pha2 prephenate dehydratase PHEN -> C02 + PHPYR pha2

YHR137W 26 1 - AR09 putative aromatic ammo acid aminotransferase II PHPYR + GLU <-> AKG + PHE aro9 1

YBR166C 1 3 1 13 tyrl Prephenate dehydrogenase (NADP+) PHEN + NADP -> 4HPP + C02 + NADPH tyrl

YGL202W 26 1 - AR08 aromatic ammo acid aminotransferase I 4HPP + GLU -> AKG + TYR aro8

YHR137W 26 1 - AR09 aromatic ammo acid aminotransferase II 4HPP + GLU -> AKG + TYR aro9J

- 1 3 1 12 Prephanate dehydrogenase PHEN + NAD -> 4HPP + C02 + NADH tyra2

YER090W 4 1 3 27 trp2 Anthramlate synthase CHOR + GLN -> GLU + PYR + AN trp2J

YKL211C 4 1 327 trp3 Anthranilate synthase CHOR + GLN -> GLU + PYR + AN trp3J

YDR354W 24 2 18 trp4 anthramlate phosphonbosyl transferase AN + PRPP -> PPI + NPRAN trp4

YDR007W 53 1 24 trpl π-(5'-phosphorιbosyl)-anthranιlate isomerase NPRAN -> CPAD5P trpl

YKL21 1C 4 1 1 48 tη>3 Indoleglycerol phosphate synthase CPAD5P -> C02 + IGP trp3J

YGL026C 42 1 20 trp5 tryptophan synthetase IGP + SER -> T3P1 + TRP trp5

YDR256C 1 1 1 1 6 CTA1 catalase A 2 H202 -> 02 etal

YGR088W 1 11 1 6 cm cytoplasmic catalase T 2 H202 -> 02 cttl

YKL106W 26 1 1 A ATI Asparate aminotransferase 4HPP + GLU <-> AKG + TYR aatl _2

YLR027C 26 1 1 AAT2 Asparate aminotransferase 4HPP + GLU <-> AKG + TYR aat2J

YMR170C 1 2 1 5 ALD2 Cytosohc aldeyhde dehydrogenase ACAL + NAD -> NADH + AC ald2

YMR169C 1 2 1 5 ALD3 strong similarity to aldehyde dehydrogenase ACAL + NAD -> NADH + AC ald3

YOR374W 1 2 1 3 ALD4 mitochondnal aldehyde dehydrogenase ACALm + NADm -> NADHm + ACm ald4J

YOR374W 1 2 1 3 ALD4 mitochondnal aldehyde dehydrogenase ACALm + NADPm -> NADPHm + ACm ald4J

YER073W 1 2 1 3 ALD5 mitochondria! Aldehyde Dehydrogenase ACALm + NADPm -> NADPHm + ACm ald5J

YPL061W 1 2 1 3 ALD6 Cytosohc Aldehyde Dehydrogenase ACAL + NADP -> NADPH + AC ald6

YJR078W 1 13 11 1 YJR078 Protein with similarity to indoleamine 2,3- TRP + 02 -> FKYN tdo2 1 W dioxygenases, which catalyze conversion of tryptophan and other indole derivatives into kynuremnes,

Tryptophan 2,3-dιoxygenase

3 5 1 9 Kynurenine formamidase FKYN -> FOR + KYN kfor

YLR231C 37 1 3 YLR231 probable kynurenmase (L-kynurenine hydrolase) KYN -> ΛLA + AN kynu

C

YBL098W 1 14 13 9 YBL098 Kynurenine 3 -hydroxylase, NADPH-dependent flavin KYN + NADPH + 02 -> HKYN +NADP

W moπooxygenase that catalyzes the hydroxylation of kynurenine to 3-hydrox kynurenine in tryptophan degradation and mcotinic acid synthesis, Kynurenine 3- monooxygeπase

YLR231C 3 7 1 3 YLR231 probable kynurenmase (L-kynurenine hydrolase) HKYN -> HAN + ALA kynuj

C YJR025C 1 13 11 6 BNA1 3-hydroxyanthranιlate 3,4-dιoxygenase (3-HAO) (3- HAN + 02 -> CMUSA bn l hydroxyanthranilic acid dioxygenase) (3- hydroxyanthranilatehydroxyanthranilic acid dioxygenase) (3-hydroxyanthranιlate oxygenase)

4 1 1 45 Picolinic acid decarboxylase CMUSA -> C02 + AM6SA 1 2 1 32 AM6SA + NAD -> AMUCO + NADH aaab 1 5 1 - AMUCO + NADPH -> AKA + NADP + NH4 aaac 1 3 1 1 27 4-Hydroxyphenylpyruvate dioxygenase 4HPP + 02 -> HOMOGEN + C02 tyrdega 1 13 11 5 Homogentisate 1,2-dιoxygenase HOMOGEN + 02 -> MACAC tyrdegb 5 2 1 2 Maleyl-acetoacetate isomerase MACAC -> FUACAC tyrdegc 3 7 1 2 Fumarylacetoacetase FUACAC -> FUM + ACT AC trydegd

YDR268w 6 1 1 2 tryptophanyl-tRNA synthetase, mitochondnal ATPm + TRPm + TRNAm -> AMPm + PPIm + mswl

TRPTRNAm

YDR242W 3 5 1 4 AMD2 putative amidase PAD -> PAC + NH3 amd2J YDR242W 3 5 1 4 AMD2 putative amidase IAD -> IAC + NH3 amd2J 26 1 29 Diamine transaminase SPRMD + ACCOA -> ASPERMD + COA spra 1 5 3 11 Polyamine oxidase ASPERMD + 02 -> APRUT + APROA + H202 sprb 1 5 3 I I Polyamine oxidase APRUT + 02 -> GABAL + APROA + H202 sprc 2 6 1 29 Diamine transaminase SPRM + ACCOA -> ASPRM + COA sprd 1 5 3 1 1 Polyamine oxidase ASPRM + 02 -> ASPERMD + APROA + H202 spre Proline biosynthesis YDR300C 27 2 1 1 prol gamma-glutamyl kinase, glutamate kinase GLU + ATP -> ADP + GLUP prol YOR323C 1 2 1 41 PR02 gamma-glutamyl phosphate reductase GLUP + NADH -> NAD + PI + GLUGSAL pro2J YOR323C 1 2 1 41 pro2 gamma-glutamyl phosphate reductase GLUP + NADPH -> NADP + PI + GLUGSAL pro2J spontaneous conversion (Strathem) GLUGSAL <-> P5C gpsl spontaneous conversion (Strathem) GLUGSALm <-> P5Cm gps2

YER023W 1 5 1 2 pro3 Pyrrol ιne-5 -carboxy late reductase P5C + NADPH -> PRO + NADP pro3J YER023W 1 5 1 2 pro3 Pyrrol i ne-5 -carboxy late reductase PHC + NADPH -> HPRO + NADP pro3J YER023W ! 5 1 2 pro3 Pyrrol ιne-5 -carboxy late reductase PHC + NADH -> HPRO + NAD pro3J YLR142W 1 5 3 - PUT1 Proline oxidase PROm + NADm -> P5Cm + NADHm pro3J Metabolism of Other Amino Acids b ta-Alanlne metabolism 1 2 1 3 aldehyde dehydrogenase, mitochondnal 1 GABALm + NADm -> GABAm + NADHm aldl YER073W 1 2 1 3 ALD5 mitochondnal Aldehyde Dehydrogenase LACALm + NADm <-> LLAC + NADHm ald5J Cyanoamino acid metabolism YJL126W 3 5 5 1 NIT2 NITRILASE APROP -> ALA + NH3 nιt2J YJL126W 3 5 5 1 N1T2 NITRILASE ACYBUT -> GLU + NH3 nιt2J Proteins, Peptides and Amiπoacids Metabolism

YLR195C 2 3 1 97 nmtl Glycylpeptide N-tetradecanoyltransferase TCOA + GLP -> COA + TGLP nmtl YDL040C 2 3 1 88 natl Peptide alpha-N-acetyltraπsferase ACCOA + PEPD -> COA + APEP natl YGR147C 2 3 1 88 NAT2 Peptide alpha-N-acetyltransferase ACCOA + PEPD -> COA + APEP nat2 Glutathlone Biosynthesis

YJL101C 6 3 2 2 GSHI gamma-glutamylcysteine synthetase CYS + GLU + ATP -> GC + PI + ADP gshl YOL049W 63 2 3 GSH2 Glutathione Synthetase GLY + GC + ATP -> RGT + PI + ADP gsh2 YBR244W 1 1 1 1 9 GPX2 Glutathione peroxidase 2 RGT + H202 <-> OGT gP*2 Y1R037W 1 11 1 9 HYR1 Glutathione peroxidase 2 RGT + H202 <-> OGT hyrl YKL026C 1 1 1 1 9 GPX1 Glutathione peroxidase 2 RGT + H202 <-> OGT gpχi YPL09IW 1 6 4 2 GLR1 Glutathione oxidoreductase NADPH + OGT -> NADP + RGT girl YLR299W 2 3 2 2 ECM38 gamma-glutamyltranspeptidase RGT + ALA -> CGLY + ALAGLY ecm38 Metabolism of Complex Carbohydrates Starch and sucrose metabolism YGR032W 2 4 1 34 GSC2 1 ,3-beta-Glucan synthase UDPG -> 13GLUCAN + UDP gsc2

YLR342W 24 I 34 FKS1 1,3-beta-Glucan synthase UDPG -> 13GLUCAN + UDP fksl YGR306W 2 4 1 34 FKS3 Protein with similarity to Fkslp and Gsc2p UDPG -> 13GLUCAN + UDP fks3 YDR261C 3 2 1 58 cxg2 Exo- 1 ,3-b-glucanase 13GLUCAN -> GLC exg2 YGR282C 3 2 1 58 BGL2 Cell wall endo-beta-l,3-glucanase 13GLUCAN -> GLC bgl2 YLR300W 3 2 1 58 exgl Exo-l,3-beta-glucanase 13GLUCAN -> GLC exgl YOR190W 3 2 1 58 sprl sporulation-specific exo-l,3-beta-glucanase 13GLUCAN -> GLC sprl

Glycoprotein Biosynthesis / Degradation YMR013C 2 7 1 108 sec59 Dolichol kinase CTP + DOL -> CDP + DOLP sec59 YPR183W 2 4 1 83 DPMI Dolichyl-phosphate beta-D-mannosyltransferase GDPMAN + DOLP -> GDP + DOLMANP dpml YAL023C 2 4 1 109 PMT2 Dohchyl-phosphate-mannose-protein DOLMANP -> DOLP + MANNAN pmt2 mannosyltransferase

YDL093W 2 4 1 109 PMT5 Dohchyl-phosphate-mannose— protein DOLMANP -> DOLP + MANNAN pmt5 mannosyltransferase

YDL095W 2 4 1 109 PMT1 Dohchyl-phosphate-mannose —protein DOLMANP -> DOLP + MANNAN pmtl mannosyltransferase

YGR199W 2 4 1 109 PMT6 Dohchyl-phosphate-mannose—protein DOLMANP -> DOLP + MANNAN pmt6 mannosyltransferase

YJR143C 2 4 1 109 PMT4 Dohchyl-phosphate-mannose— protein DOLMANP -> DOLP + MANNAN pmt4 mannosyltransferase

YOR321 W 2 4 1 109 PMT3 Dohchyl-phosphate-mannose-protein DOLMANP -> DOLP + MANNAN pmt3 mannosyltransferase

YBR199W 2 4 1 131 KTR4 Glycolipid 2-alpha-mannosyltransferase MAN2PD + 2 GDPMAN -> 2 GDP + 2MANPD ktr4 YBR205W 2 4 1 131 KTR3 Glycolιpιd 2-alpha-mannosyltransferase MAN2PD + 2 GDPMAN -> 2 GDP + 2MANPD ktr3 YDR483W 2 4 1 131 kre2 Glycolipid 2-alpha-mannosyltransferase MAN2PD + 2 GDPMAN -> 2 GDP + 2MANPD kre2 YJL139C 2 4 1 131 yurl Glycolipid 2-alpha-mannosyltransferase MAN2PD + 2 GDPMAN -> 2 GDP + 2MANPD yurl YKR061W 2 4 1 131 KTR2 Glycolipid 2-alpha-mannosyltrαnsferase MAN2PD + 2 GDPMAN -> 2 GDP + 2MANPD ktr2 YOR099W 2 4 1 131 KTR1 Glycolipid 2-alpha-mannosyltransferase MAN2PD + 2 GDPMAN -> 2 GDP + 2MANPD ktrl YPL053C 2 4 1 131 KTR6 Glycolipid 2-alpha-maπnosyltraπsferase MAN2PD + 2 GDPMAN -> 2 GDP + 2MANPD ktrβ Amlnosugars metabolism YER062C 3 1 3 21 HOR2 DL-glycerol-3-phosphatase GL3P -> GL + PI hor2 YIL053W 3 1 3 21 RHR2 DL-glycerol-3-phosphatase GL3P -> GL + PI rhr2 YLR307W 3 5 1 41 CDA1 Chitin Deacetylase CHIT -> CHITO + AC cda] YLR308W 3 5 1 41 CDA2 Chitin Deacetylase CHIT -> CHITO + AC cda2 Metabolism of Complex Lipids

Glycerol (Glycerolipid metabolism)

YFL053W 2 7 1 29 DAK2 dihydroxyacetone kinase GLYN + ATP -> T3P2 + ADP dak2

YML070W 2 7 1 29 DA 1 putative dihydroxyacetone kinase GLYN + ATP -> T3P2 + ADP dak I

YDL022W 1 1 1 8 GPD1 glycerol-3-phosphate dehydrogenase (NAD) T3P2 + NADH -> GL3P + NAD gpdl

YOL059W 1 1 1 8 GPD2 glycerol-3-phosphate dehydrogenase (NAD) T3P2 + NADH -> GL3P + NAD gpd2

YHL032C 2 7 1 30 GUT1 glycerol kinase GL + ATP -> GL3P + ADP gutl

YIL155C 1 1 99 5 GUT2 glycerol-3-phosphate dehydrogenase GL3P + FADm -> T3P2 + FADH2m gut2

DAGLY + 0 017 CIOOACP + 0 062 C120ACP + daga

0 100 C140ACP + 0 270 C160ACP + 0 169

C161 ACP + 0 055 C180ACP + 0 235 C181ACP Λ

0 093 C182ACP -> TAGLY + ACP

Metabolism of Cofactors, Vitamins, and Other Substances Thiamine (Vitamin Bl) metabolism

YOR143C 2 7 6 2 THI80 Thiamin pyrophosphokinase ATP + THIAMIN -> AMP + TPP thι80J YOR143C 2 7 6 2 THI80 Thiamin pyrophosphokinase ATP + TPP -> AMP + TPPP thι80J ttiiC protein AIR -> AHM thic

YOL055C 2 7 1 49 THI20 Bipanite protein consisting of N-terminal AHM + ATP -> AHMP + ADP thι20 hydroxymethylpynmidine phosphate (HMP-P) kinase domain, needed for thiamine biosynthesis, fused to C- termina! Petl 8p-lιke domain of indeterminant function

YPL258C 2 7 1 49 THI21 Bipartite protein consisting of N-terminal AHM + ATP -> AHMP + ADP hydroxymethylpynmidine phosphate (HMP-P) kinase domain, needed for thiamine biosynthesis, fused to C- terminal Petl8p-lιke domain of indeterminant function

YPR121 W 2 7 1 49 THI22 Bipartite protein consisting of N-terminal AHM + ATP -> AHMP + ADP thι22 hydroxymethylpynmidine phosphate (HMP-P) kinase domain, needed for thiamine biosynthesis, fused to C- terminal Petl8p-lιke domain of indeterminant function

YOL055C 2 7 4 7 THI20 HMP-phosphate kinase AHMP + ATP -> AHMPP + ADP thid

Hypothetical T3P1 + PYR -> DTP unkrxn I thiG protein DTP + TYR + CYS -> THZ + HBA + C02 thig thiE protein DTP + TYR + CYS -> THZ + HBA + C02 thie thiF protein DTP + TYR + CYS -> THZ + HBA + C02 thif thiH protein DTP + TYR + CYS -> THZ + HBA + C02 thih

YPL214C 2 7 1 50 THI6 Hydroxyethylthiazole kinase THZ + ATP -> THZP + ADP thim YPL214C 2 5 1 3 THI6 TMP pyrophosphorylase, hydroxyethylthiazole kinase THZP + AHMPP -> THMP + PPI thι6 2 7 4 16 Thiamin phosphate kinase THMP + ATP <-> TPP + ADP thil 3 1 3 - (DL)-glycerol-3-phosphatase 2 THMP -> THIAMIN + PI unkrxnδ Riboflavin metabolism YBL033C 3 5 4 25 πbl GTP cyclohydrolase II GTP -> D6RP5P + FOR + PPI ribl YBR153W 3 5 4 26 R1B7 HTP reductase, second step in the πboflavin D6RP5P -> A6RP5P + NH3 πbdl biosynthesis pathway

YBRI53W 1 1 1 193 πb7 Pyπmidine reductase A6RP5P + NADPH -> A6RP5P2 + NADP πb7

Pynmidine phosphatase A6RP5P2 -> A6RP + PI prm

3,4 Dιhydroxy-2-bυtanone-4-phosphate synthase RL5P -> DB4P + FOR πbb

YBR256C 2 5 1 9 RIB5 Riboflavin biosynthesis pathway enzyme, 6,7-dιmethyl- DB4P + A6RP -> D8RL + PI πb5

8-nbιtyllumazιne synthase, apha chain

YOL143C 2 5 1 9 RIB4 Riboflavin biosynthesis pathway enzyme, 6,7-dιmethyl-

8-πbιtyllumazιne synthase, beta chain

YAR071W 3132 pholl Acid phosphatase FMN -> RIBFLAV + PI phol l YDR236C 27126 FMN1 Riboflavin kinase RIBFLAV + ATP -> FMN + ADP fmnl YDR236C 27126 FMN1 Riboflavin kinase RIBFLAVm + ATPm -> FMNm + ADPm fmnl YDL045C 2772 FAD1 FAD synthetase FMN + ATP -> FAD + PPI fadl 2772 FAD synthetase FMNm + ATPm -> FADm + PPIm fadlb

Vitamin B6 (Pyridoxlne) Biosynthesis metabolism

- 2 7 1 35 Pyπdoxine kinase PYRDX + ATP -> P5P + ADP pdxka

- 2 7 1 35 Pyndoxine kinase PDLA + ATP -> PDLA5P + ADP pdxkb

- 2 7 1 35 Pyndoxine kinase PL + ATP -> PL5P + ADP pdxkc

YBR035C 1 4 3 5 PDX3 Pyndoxine 5'-phosphate oxidase PDLA5P + 02 -> PL5P + H202 + NH3 pdx3J

YBR035C 1 4 3 5 PDX3 Pyndoxine 5'-phosphate oxidase P5P + 02 <-> PL5P + H202 pdx3J

YBR035C 1 4 3 5 PDX3 Pyndoxine 5'-phosphate oxidase PYRDX + 02 <-> PL + H202 pdx3J

YBR035C 1 4 3 5 PDX3 Pyndoxine 5'-phosphate oxidase PL + 02 + NH3 <-> PDLA + H202 pdx3J

YBR035C 1 4 3 5 PDX3 Pyndoxine 5'-phosphate oxidase PDLA5P + 02 -> PL5P + H202 + NH3 pdx3J

YOR184W 2 6 1 52 serl Hypothetical transamiπase/phosphoseπne transaminase OHB + GLU <-> PHT + AKG serl J,

YCR053W 4 2 99 2 thr4 Threonine synthase PHT -> 4HLT + PI thrt

3 1 3 - Hypothetical Enzyme PDLA5P -> PDLA + PI hor2b

Pantothenate and CoA biosynthesis

3 MALCOA -> CHCOA + 2 COA + 2 C02 biol

2 3 1 47 8-Amιno-7-oxoπonanoate synthase ALA + CHCOA <-> C02 + COA + AONA biof YNR058W 2 6 1 62 BI03 7,8-dιamιno-pelargonιc acid aminotransferase (DAPA) SAM + AONA <-> SAMOB + DANNA bιo3 aminotransferase

YNR057C 6 3 3 3 BI04 dethiobiolm synthetase C02 + DANNA + ATP <-> DTB + PI + ADP bιo4 YGR286C 2 8 1 6 BI02 Biotin synthase DTB + CYS <-> BT blo2 Folate biosynthesis YGR267C 3 5 4 16 fol2 GTP cyclohydrolase I GTP -> FOR + AHTD fol2

3 6 1 - Dihydroneopteπn tnphosphate pyrophosphorylase AHTD -> PPI + DHPP ntpa YDR481C 3 1 3 1 pho8 Glycerophosphatase, Alkaline phosphatase, Nucleoside AHTD -> DHP + 3 PI pho8 tnphosphatase

YDL100C 3 6 1 - YDL100 Dihydroneopteπn monophosphate dephosphorylase DHPP -> DHP + PI dhdnpa

C

YNL256W 4 1 2 25 foil Dihydroneoptenn aldolase DHP -> AHHMP + GLAL foll YNL256W 2 7 6 3 foil 6-Hydroxymethyl-7,8 dihydroptenn pyrophosphokinase AHHMP + ATP -> AMP + AHHMD foll YNR033W 4 1 3 - ABZ1 Aminodeoxychoπsmate synthase CHOR + GLN -> ADCHOR + GLU abzl 4 - - - Aminodeoxychorismate lyase ADCHOR -> PYR + PABA pabc

YNL256W 2 5 1 15 foil Dihydropteroate synthase PABA + AHHMD -> PPI + DHPT follj YNL256W 2 5 1 15 foil Dihydropteroate synthase PABA + AHHMP -> DHPT follj 6 3 2 12 Dihydrofolate synthase DHPT + ATP + GLU -> ADP + PI + DHF folc

Y0R236W 1 5 1 3 dfrl Dihydrofolate reductase DHFm + NADPHm -> NADPm + THFm dfrl Y0R236W 1 5 1 3 dfrl Dihydrofolate reductase DHF + NADPH -> NADP + THF dfrlj 6 3 3 2 5-Formyltetrahydrofolate cyclo-lιgase ATPm + FTHFm -> ADPm + Plm + MTHF ftfa 6 3 3 2 5-Formyltetrahydrofolate cyclo-lιgase ATP + FTHF -> ADP + PI + MTHF ftlb

YKL132C 6 3 2 17 Protein with similarity to folylpolyglutamate synthase, THF + ATP + GLU <-> ADP + PI + THFG rmal converts tetrahydrofolyl-[Glu(n)] + glutamate to tetrahydrofolyl-[Glu(n+l )]

YMR113W 6 3 2 17 FOL3 Dihydrofolate synthetase THF + ATP + GLU <-> ADP + PI + THFG fol3 YOR241 W 6 3 2 17 MET7 Folylpolyglutamate synthetase, involved in methionine THF + ATP + GLU <-> ADP + PI + THFG met7 biosynthesis and maintenance of mitochondria] genome

One carbon pool by folate |MAP 006701 YPL023C 1 5 1 20 METI2 Methylene tetrahydrofolate reductase METTHFm ^ NADPHm -> NADPm + MTHFm met 12 YGL125W 1 5 1 20 metl3 Methyleπe tetrahydrofolate reductase METTHFm h- NADPHm -> NADPm + MTHFm metl3 YBR084W 1515 misl the mitochondnal tπfunctional enzyme Cl- METTHFm + NADPm <-> METHFm + NADPHm misl J tetrahydroflate synthase

YGR204W 1515 ade3 the cytoplasmic tnfunctioπal enzyme Cl- METTHF + NADP <->METHF + NADPH ade3J tetrahydro folate synthase

YBR084W 6343 misl the mitochondnal tnfunctional enzyme Cl- THFm + FORm + ATPm -> ADPm + Plm + FTHF misl J tetrahydroflate synthase

YGR204W 6343 ade3 the cytoplasmic tnfunctional enzyme Cl- THF + FOR + ATP -> ADP + PI + FTHF ade3 J tetrahydrofolate synthase

YBR084W 3549 misl the mitochondnal tnfunctional enzyme Cl- METHFm <-> FTH Fm misl J tetrahydroflate synthase

YGR204W 3549 ade3 the cytoplasmic tnfunctional enzyme Cl- METHF <-> FTHF ade3 J tetrahydrofolate synthase

YKR080W 15115 MTD1 NAD-dependent5,10-methylenetetrahydrafolate METTHF + NAD ->METHF + NADH mtdl dehydrogenase

YBL013 2129 fmtl Methioπyl-tRNA Transformylase FTHFm + MTRNAm -> THFm + FMRNAm fmtl

Coenzyme A Biosynthesis

YBR176W 21211 ECM31 Ketopentoate hydroxymethyl transferase OrVAL + METTHF -> AKP + THF ecm31

YHR063C 111169 PANS Putative ketopantoate reductase (2-dehydropantoate 2- AKP + NADPH -> NADP + PANT pane reductase) involved in coenzyme A synthesis, has similarity to Cbs2p, Ketopantoate reductase

YLR355C 11186 ιlv5 Ketol-acid reductotsomerase AKPm + NADPHm -> NADPm + PANTm ιlv5 J YIL145C 6321 YIL145C Pantoate-b-alamne ligase PANT + bALA + ATP -> AMP + PPI + PNTO panca YDR531W 27133 YDR531 Putative pantothenate kinase involved in coenzyme A PNTO + ATP -> ADP + 4PPNTO coaa W biosynthesis, Pantothenate kinase

6325 Phosphopantothenate-cysteme ligase 4PPNTO + CTP + CYS -> CMP + PPI + 4PPNCYS pchg 41136 Phosphopantothenate-cysteine decarboxylase 4PPNCYS -> C02 + 4PPNTE pcdcl

2773 Phospho-pantethiene adenylyltransferase 4PPNTE + ATP -> PPI + DPCOA patrana 2773 Phospho-pantethiene adenylyltransferase 4PPNTEm + ATPm -> PPIm + DPCOAm patranb 27124 DephosphoCoA kinase DPCOA + ATP -> ADP + COA dphcoaka 27124 DephosphoCoA kinase DPCOAm + ATPm -> ADPm + COAm dphcoakb 41111 ASPARTATE ALPHA-DECARBOXYLASE ASP -> C02 + bALA pancb

YPL148C 2787 Acyl carrier-protein synthase, phosphopantetheine COA -> PAP + ACP acps protein transferase for Acplp

NAD Biosynthesis YGL037C 35119 PNC1 Nicotinamidase NAM <-> NAC + NH3 nadh

YOR209C 24211 NPT1 NAPRTase NAC + PRPP -> NAMN + PPI nptl

143- Aspartate oxidase ASP + FADm -> FADH2m + ISUCC nadb

14316 Quinolate synthase ISUCC + T3P2->PI+QA nada

YFR047C 24219 QPT1 Quinotate phosphoπbosyl transferase QA + PRPP -> NAMN + C02 + PPI nadc YLR328W 27718 YLR328 Nicotinamide mononucleotide (NMN) NAMN + ATP -> PPI + NAAD naddl

W adenylyltransferase

YHR074W 6351 QNS1 Deamido-NAD ammonia ligase NAAD + ATP + NH3 -> NAD + AMP + PPI nade YJR049c 27123 utrl NAD kinase, POLYPHOSPHATE KINASE (EC NAD + ATP -> NADP + ADP nadf

2741)/ NAD+ KINASE (EC 27123)

YEL041w 27123 YEL041 NAD kinase, POLYPHOSPHATE KINASE (EC NAD + ATP -> NADP + ADP nadfj w 2741)/ NAD+ KINASE (EC 27123) YPL188w 27123 POS5 NAD kinase, POLYPHOSPHATE KINASE (EC NAD + ATP -> N ADP + ADP nadfj

2741)/ NAD+ KINASE (EC 27123)

312- NADP phosphatase NADP -> NAD + PI nadphps 3225 NAD -> NAM + ADPRIB nadi 2421 strong similarity to puπne-nucleoside phosphorylases ADN + PI <-> AD + RIP nadgl 2421 strong similarity to puπne-nucleoside phosphorylases GSN + PI <-> GN + RIP nadg2 Nlcotinic Acid synthesis from TRP

YFR047C 24219 QPTI Quinolate phosphoπbosyt transferase OAm + PRPPm -> NAMNm + C02m + PPIm mnadc YLR328W 27718 YLR328 NAMN adenylyl transferase NAMNm + ATPm -> PPIm + NAADm mnaddl

W

YLR328W 27718 YLR328 NAMN adenylyl transferase NMNm + ATPm -> NADm + PPIm mnad 2

W

YHR074W 6351 QNSI Deamido-NAD ammonia ligase NAADm + ATPm + NH3m -> NADm + AMPm H mnade

PPIm

YJR049c 27123 utrl NAD kinase, POLYPHOSPHATE KINASE (EC NADm + ATPm -> NADPm + ADPm mnadf

2741)/ NAD+ KINASE (EC 27123)

YPL188w 27123 POS5 NAD kinase, POLYPHOSPHATE KINASE (EC NADm + ATPm -> NADPm + ADPm m nadfj

2741)/ NAD+ KINASE (EC 27123)

YEL041w 27123 YEL041 NAD kinase, POLYPHOSPHATE KINASE (EC NADm + ATPm -> NADPm + ADPm mnadfj

2741)/ NAD+ KINASE (EC 27123)

312- NADP phosphatase NADPm -> NADm + Plm mnadphps

YLR209C 2421 PNP1 strong similarity to punne-nucleoside phosphorylases ADNm + Plm <-> ADm + RIPm mnadgl

YLR209C 2421 PNP1 strong similarity to punne-nucleoside phosphorylases GSNm + Plm <- GNm + RIPm mnadg2

YGL037C 35119 PNC1 Nicotinamidase NAMm <-> NACm + NH3m mnadh

YOR209C 24211 NPT1 NAPRTase NACm + PRPPm -> NAMNm + PPIm mnptl

3225 NADm -> NAMm + ADPRIBm mnadi Uptake Pathways

Porphyrln and Chlorophyll Metabolism

YDR232W 23137 heml 5-Amιnolevulιnate synthase SUCCOAm + GLY -> ALA V + COAm + C02m heml

YGL040C 42124 HEM2 Aminolevulinate dehydratase 2 ALAV -> PBG hem2

YDL205C 4318 HEM3 Hydroxymethylbilane synthase 4 PBG -> HMB + 4 NH3 hem3

YOR278W 42175 HEM4 Uroporphyπnogen-HI synthase HMB -> UPRG hem4 YDR047W 4 1 1 37 HEM12 Uroporphyπnogen decarboxylase UPRG -> 4 C02 + CPP hem 12 YDR044W 1 3 3 3 HEM13 Coproporphyπnogen oxidase, aerobic 02 + CPP -> 2 C02 + PPHG hem 13 YER014W 1 3 3 4 HEM 14 Protoporphyπnogen oxidase 02 + PPHGm -> PPLXm hem 14 YOR176W 4 99 I 1 HEM 15 Ferrochelatase PPlXm -> PTHm hem 15 YGL245W 6 1 1 17 YGL245 glutamyl-tRNA synthetase, cytoplasmic GLU + ATP -> GTRNA + AMP + PPI unrxnlO

W

YOL033W 6 1 1 17 MSE1 GLUm + ATPm -> GTRNAm + AMPm + PPIm msel

YKR069W 2 1 1 107 metl uroporphyπn-III C-methyltransferase SAM + UPRG -> SAH + PC2 metl

Qulnone Biosynthesis

YKL21 1C 4 1 3 27 t 3 anthramlate synthase Component II and ιndole-3- CHOR -> 4HBZ + PYR trp3J phosphate (multifunctional enzyme)

YER090W 4 1 3 27 tφ2 anthramlate synthase Component I CHOR -> 4HBZ + PYR tφ2J

YPR176C 2 5 1 - BET2 geranylgeranyltransferase type II beta subunit 4HBZ + NPP -> N4HBZ H PPI bet2

YJL031C 2 5 1 - BET4 geranylgeranyltransferase type II alpha subunit

YGL155W 2 5 1 - cdc43 geranylgeranyltransferase type I beta subunit

YBR003W 2 5 1 - COQ1 Hexaprenyl pyrophosphate synthetase, catalyzes the 4HBZ + NPP -> N4HBZ + PPI coql first step in coenzyme Q (ubiquinoπe) biosynthesis pathway

YNR041 C 2 5 1 - COQ2 para-hydroxybenzoate— polyprenyltransferase 4HBZ + NPP -> N4HBZ + PPI coq2 YPL172C 2 5 1 - COX 10 protoheme IX farnesyltransferase, mitochondnal 4HBZ + NPP -> N4HBZ + PPI cox 10 precursor

YDL090C 2 5 1 - ram 1 pprrootteeiinn ffaarnrneessyyllttrraannssffeerraassee bbeettaa ssuubbuunniitt 4HBZ + NPP -> N4HBZ + PPI raml

YKL019W 2 5 1 - RAM2 pprrootteeiinn ffaarnrneessyyllttrraannssffeerraassee aallpphhaa ssuubbuunniitt

YBR002C 2 5 1 - RER2 ppuuttaattiivvee ddeehhyyddrrooddoolliicchhyyll ddiipphhoossppaattee ssyynntthheettaassee 4HBZ + NPP -> N4HBZ + PPI rer2

YMR101C 2 5 1 - SRT1 ppuuttaattiivvee ddeehhyyddroroddoolliicchhyyll ddiipphhoossppaattee ssyynntthheettaassee 4HBZ + NPP -> N4HBZ + PPI srtl

YDR538W 4 1 1 - PAD1 OOccttaapprreennyytl--hhyyddroroxxyybbeennzzooaattee ddeeccaarrbbooxxyyllaassee N4HBZ -> C02 + 2NPPP padl

- 1 13 14 - 2 2--OOccttaapprreennyyllpphheennooll hhyyddrrooxxyyllaassee 2NPPP + 02 -> 2N6H ubib

YPL266W 2 1 1 - DIM1 2N6H + SAM -> 2NPMP + SAH dim]

- 1 14 13 - 2 2--OOccttaapprreennyyll--66--mmeetthhooxxyypphheennooll hhyyddroroxxyyllaassee 2NPMPm + 02m -> 2NPMBm ubih

YML1 10C 2 1 1 - COQ5 22--OOccttaapprreennyyll--66--mmeetthhooxxyy--ll,,44--bbeennzzooqquuιιnnoonnee mmeetthhyyllaassee 2NPMBm + SAMm -> 2NPMMBm + SAHm coq5

YGR255C 1 14 13 - COQ6 CCOOQQ66 mmoonnooooxxyyggeeππaassee 2NPMMBm + 02m -> 2NMHMBm coq6b

YOL096C 2 1 1 64 COQ3 33--DDιιmmeetthhyylluubbιιqquuιιnnoonnee 33--mmeetthhyyllttrraannssffeerraassee 2NMHMBm + SAMm -> QH2m + SAHm ubig

Memberaπe Transport

Mitochondiral Membrane Transport

Thefollowings diffuse through the inner mitochondiral membrane in a non-carner-mediated manner 02 <-> 02m mo2 C02 <-> C02m mco2 ETH <-> ETH meth NH3 <-> NH3m mnh3 MTHN <-> MTHN m thn THFm <-> THF mthf METTHFm <-> METTHF mmthf SERm <-> SER mser GLYm <-> GLY mgly CBHCAP <-> CBHCAP mcbh OICAPm <-> OICAP moicap PROm <-> PRO mpro CMPm <-> CMP mcmp ACm <-> AC mac ACAR -> ACARm macar_ CARm -> CAR mcar_ ACLAC <-> ACLACm maclac ACT AC <-> ACTACm mactc SLF -> SLFm + Hm mslf THRm <-> THR mthr AKAm -> AKA maka

YMR056C AAC 1 ADP/ATP earner protein (MCF) ADP + ATPm + PI -> Hm + ADPm + ATP + Plm aacl YBL030C pet9 ADP/ATP earner protein (MCF) ADP + ATPm + PI -> Hm + ADPm + ATP + Plm pet9 YBR085w AAC3 ADP/ATP earner protein (MCF) ADP + ATPm + PI -> Hm + ADPm + ATP + Plm aac3 YJR077C MIR1 phosphate earner PI <-> Hm + Plm mirla YER053C YER053 similarity to C elegans mitochondnal phosphate carrier PI + OHm <-> Plm mirld

C

YLR348C DIC 1 dicarboxylate carrier MAL + SUCCm <-> MALm + SUCC diclj YLR348C D IC 1 dicarboxylate carrier MAL + Plm <-> MALm + PI dicl YLR348C DIC 1 dicarboxylate earner SUCC + Plm -> SUCCm + PI diclj

MALT + Plm <-> MALTm + PI mmlt

YKL120W OAC1 Mitochondnal oxaloacetate carrier OA <-> OAm + Hm moab YBR291 C CTP 1 citrate transport protein CIT + MALm <-> CITm + MAL ctpl 1 YBR29 I C CTP 1 citrate transport protein CIT + PEPm <-> CITm + PEP ctpl 2 YBR291 C CTP 1 citrate transport protein CIT + ICITm <-> CITm + ICIT ctpl 3

IPPMAL <-> IPPMALm mpmalR

LAC <-> LACm + Hm mlac pyruvate earner PYR <-> PYRm + Hm pyrca glutamate carrier GLU <-> GLUm + Hm gca

GLU + OHm -> GLUm gcb

YOR130C ORT1 ornithine carrier ORN + Hm <-> ORNm ortl YOR100C CRC 1 carnitine carrier CARm + ACAR -> CAR + ACARm crcl

OIVAL <-> OIVALm moival

OMVAL <-> OMVALm momval

YIL134W FLX1 Protein involved in transport of FAD from cytosol into FAD + FMNm -> FADm + FMN mfad the mitochondnal matrix

RIBFLAV <-> RIBFLAVm mnbo

DTB <-> DTBm mdtb

H3MCOA <-> H3MCOAm mmcoa

MVL <-> MVLm mmvl

PA <-> PAm mpa

4PPNTE <-> 4PPNTEm mppnt

AD <-> ADm mad

PRPP <-> PRPPm mpφp

DHF <-> DHFm mdhf

QA <-> QAm mqa

OPP <-> OPPm mopp

SAM <-> SAMm msam

SAH <-> SAHm msah

YJR095W SFC 1 Mitochondria! membrane succinate-fumarate SUCC + FUMm -> SUCCm + FUM sfcl transporter, member of the mitochondnal earner family (MCF) of membrane transporters

YPL134C ODC 1 2-oxodιcarbo late transporter AKGm + OXA <-> AKG + OXAm odcl

YOR222W ODC2 2-oxodιcarboylate transporter AKGm + OXA <-> AKG + OXAm odc2

Malate Aspartate Shuttle Included elsewhere Glycerol phosphate shuttle

T3P2m -> T3P2 mt3p GL3P -> GL3Pm mgl3p

Plasma Membrane Transport

Carbohydrates

YHR092c HXT4 moderate- to low-affinity glucose transporter GLCxt -> GLC hxt4

YLROS lw GAL2 galactose (and glucose) permease GLCxt -> GLC ga!2J

YOL156w HXT1 1 low affinity glucose transport protein GLCxt -> GLC hxtl l

YDR536W stl l Protein member of the hexose transporter family GLCxt -> GLC stll 1

YHR094C hxtl High-affinity hexose (glucose) transporter GLCxt -> GLC hxtl

YOL156W HXTU Glucose permease GLCxt -> GLC hxtlϊj

YEL069c HXT13 high-affinity hexose transporter GLCxt -> GLC hxtl3J

YDL245C HXT15 Hexose transporter GLCxt -> GLC hxtlSJ

YJR158W HXT16 hexose permease GLCxt -> GLC hxtl 6 J

YFLOl lw HXT10 high-affinity hexose transporter GLCxt -> GLC hxtlOJ

YNR072w HXT17 Putative hexose transporter GLCxt -> GLC hxtl7J

YMR01 lw HXT2 high affinity hexose transporter-2 GLCxt -> GLC hxt2J

YHR092c hxt4 High-affinity glucose transporter GLCxt -> GLC hxt4 1

YDR345c hxϋ Low-affinity glucose transporter GLCxt -> GLC hxt3J

YHR096C HXT5 hexose transporter GLCxt -> GLC hxt5J

YDR343c HXT6 Hexose transporter GLCxt -> GLC hxtβ

YDR342c HXT7 Hexose transporter GLCxt -> GLC hxt7J

YJL214w HXT8 hexose permease GLCxt -> GLC hxt8J

YJL219W HXT9 hexose permease GLCxt -> GLC hxt9J

YLR081W gal2 galactose permease GLACxt + HEXT -> GLAC ga!2

YFLOl lw HXT10 high-affinity hexose transporter GLACxt + HEXT -> GLAC hxtlOJ

YOL156w HXT1 1 Glucose permease GLACxt + HEXT -> GLAC hxtl l

YNL318C HXT14 Member of the hexose transporter family GLACxt + HEXT -> GLAC hxtl4~

YJL219w HXT9 hexose permease GLACxt + HEXT -> GLAC hxt9J

YDR536W stll Protein member of the hexose transporter family GLACxt + HEXT -> GLAC stll

YFL055w AGP3 Amino acid permease for senne, aspartate, and GLUxt + HEXT <• •> GLU agp3J glutamate

YDR536W stll Protein member of the hexose transporter family GLUxt + HEXT <-> GLU stll

YKR039W gapl General ammo acid permease GLUxt + HEXT <-> GLU gap8

YCL025C AGP1 Am o acid permease for most neutral amino acids GLUxt + HEXT <-> GLU gap24

YPL265W DD>5 Dicarboxylic amino acid permease GLUxt + HEXT <-> GLU diplO

YDR536W stll Protein member of the hexose transporter family GLUxt + HEXT <-> GLU stllj

YHR094C hxtl High-affinity hexose (glucose) transporter FRUxt + HEXT -> FRU hxtl

YFLOl lw HXT10 high-affinity hexose transporter FRUxt + HEXT -> FRU hxtlOJ

YOL156w HXTI 1 Glucose permease FRUxt + HEXT -> FRU hxtl lj

YEL069C HXT13 high-affinity hexose transporter FRUxt + HEXT -> FRU hxt!3J

YDL245c HXT15 Hexose transporter FRUxt + HEXT -> FRU hxtl 5 J

YJR158W HXT16 hexose permease FRUxt + HEXT -> FRU hxtlδj

YNR072w HXT17 Putative hexose transporter FRUxt + HEXT -> FRU hxtl7J

YMROl lw HXT2 high affinity hexose transporter-2 FRUxt + HEXT -> FRU hxt2J

YDR345C hxϋ Low-affinity glucose transporter FRUxt + HEXT -> FRU hxt3J

YHR092c hxt4 High-affinity glucose transporter FRUxt + HEXT -> FRU hxt4 2

YHR096c HXT5 hexose transporter FRUxt + HEXT -> FRU hxt5J

YDR343C HXT6 Hexose transporter FRUxt + HEXT -> FRU hxt6J

YDR342C HXT7 Hexose transporter FRUxt + HEXT -> FRU hxt7J

YJL214w HXT8 hexose permease FRUxt + HEXT -> FRU hxt8 5 YJL219w HXT9 hexose permease FRUxt + HEXT -> FRU hxt9J

YHR094C hxtl High-affinity hexose (glucose) transporter MANxt + HEXT -> MAN hxtlj

YFLOl lw HXT10 high-affinity hexose transporter MANxt + HEXT -> MAN hxtlOJ

YOL156w HXT1 1 Glucose permease MANxt + HEXT -> MAN hxtl lj

YEL069c HXT13 high-affinity hexose transporter MANxt + HEXT -> MAN hxtl3J

YDL245c HXT15 Hexose transporter MANxt + HEXT -> MAN hxtl 5 J

YJR158w HXT16 hexose permease MANxt + HEXT -> MAN hxtlβj

YNR072w HXT17 Putative hexose transporter MANxt + HEXT -> MAN hxtl7J

YMROl lw HXT2 high affinity hexose transporter-2 MANxt + HEXT -> MAN hxt2J

YDR345C hxt3 Low-affinity glucose transporter MANxt + HEXT -> MAN hxt3J

YHR092C hxt4 High-affinity glucose transporter MANxt + HEXT -> MAN hxt4J

YHR096c HXT5 hexose transporter MANxt + HEXT -> MAN hxt5J

YDR343c HXT6 Hexose transporter MANxt + HEXT -> MAN hxtβj

YDR342c HXT7 Hexose transporter MANxt + HEXT -> MAN hxt7J

YJL214w HXT8 hexose permease MANxt + HEXT -> MAN hxt8J

YJL219W HXT9 hexose permease MANxt + HEXT -> MAN hxt9J

YDR497c ITR1 myo-inositol transporter MIxt + HEXT -> MI itrl

YOL103w 1TR2 myo-inosilol transporter MIxt + HEXT -> MI ιtr2

Maltase permease MLTxt + HEXT -> MLT mltup

YIL162W 3 2 1 26 SUC2 lnvertase (sucrose hydrolyzing enzyme) SUCxt -> GLCxt + FRUxt suc2 sucrose SUCxt + HEXT -> SUC sucup

YBR298c MA 31 Dicarboxylates MALxt + HEXT <-> MAL mal31 a-Ketoglutarate/malate translocator MALxt + AKG <-> MAL + AKGxt akmup a-methy]glucosιde AMGxt <-> AMG amgup

Sorbose SORxt <-> SOR sorup

Arabinose (low affinity) ARABxt <-> ARAB arbupl

Fucose FUCxt + HEXT <-> FUC fucup GLTLxt + HEXT -> GLTL gltlupb

Glucitol GLTxt + HEXT -> GLT gltup

Glucosamine GLAMxt + HEXT <-> GLAM gaup

YLL043W FPS1 Glycerol GLxt <-> GL glup YKL217W JEN1 Lactate transport LACxt + HEXT <-> LAC lacupl

Mannitol MNTxt + HEXT -> MNT mntup

Mehbiose MELIxt + HEXT -> MELI melup

N-Acetylglucosamine NAGxt + HEXT -> NAG nagup

Rhamnose RMNxt + HEXT -> RMN rmπup

Ribose RIBxt + HEXT -> RIB nbup

Trehalose TRExt + HEXT -> TRE treup TRExt -> AATRE6P treupj XYLxt <-> XYL xylup

Amino Acids

YKR039W gapl General amino acid permease ALAxt + HEXT <-> ALA gapl 1

YPL265W DIP5 Dicarboxyhc amino acid permease ALAxt + HEXT <-> ALA dιp5

YCL025C AGP 1 Amino acid permease for most neutral amino acids ALAxt + HEXT <-> ALA gap25

YOL020W TAT2 Tryptophan permease ALAxt + HEXT <-> ALA tat5

YOR348C PUT4 Proline permease ALAxt + HEXT <-> ALA put4

YKR039W gapl General amino acid permease ARGxt + ^■ HEXT <-> ARG gap2

YEL063C canl Permease for basic amino acids ARGxt + HEXT <-> ARG canl J

YNL270C ALP1 Protein with strong similarity to permeases ARGxt + HEXT <-> ARG alpl

YKR039W gapl General ammo acid permease ASNxt + HEXT <-> ASN gap3

YCL025C AGP1 Am o acid permease for most neutral amino acids ASNxt + HEXT <-> ASN gap21

YDR508C GNP1 Glutamine permease (high affinity) ASNxt + HEXT <-> ASN gπp2

YPL265W D1P5 Dicarboxyhc am o acid permease ASNxt + HEXT <-> ASN dιp6

YFL055W AGP3 Amino acid permease for senne, aspartate, and ASPxt + HEXT <-> ASP agp3J glutamate

YKR039W gapl General amino acid permease ASPxt + HEXT <-> ASP gap4

YPL265W DIP5 Dicarboxyhc amino acid permease ASPxt + HEXT <-> ASP dιp7

YKR039W gapl General amino acid permease CYSxt + HEXT <-> CYS gap5

YDR508C GNP1 Glutamine permease (high affinity) CYSxt + HEXT <-> CYS gnp3

YBR068C BAP2 Branched chain ammo acid permease CYSxt + HEXT <-> CYS bap2J

YDR046C BAP3 Branched chain ammo acid permease CYSxt + HEXT <-> CYS bap3_l

YBR069C VAP1 Am o acid permease CYSxt + HEXT <-> CYS vap7

YOL020W TAT2 Tryptophan permease CYSxt + HEXT <-> CYS tat7

YKR039W gapl General amino acid permease GLYxl + HEXT <-> GLY gap6

YOL020W TAT2 Tryptophan permease GLYxt + HEXT <-> GLY tal6

YPL265W DIP5 Dicarboxyhc amino acid permease GLYxt ÷ HEXT <-> GLY dιp8

YOR348C PUT4 Proline permease GLYxt + HEXT <-> GLY put5

YKR039W gapl General amino acid permease GLNxt ÷ HEXT <-> GLN gap7

YCL025C AGP 1 Ammo acid permease for most neutral amino acids GLNxt + HEXT <-> GLN gap22

YDR508C GNP1 Glutamine permease (high affinity) GLNxt + HEXT <-> GLN gnpl

YPL265W DIPS Dicarboxyhc amino acid permease GLNxt + HEXT <-> GLN dιp9

YGR191W HIP 1 Histidine permease HISxt + HEXT <-> HIS hipl

YKR039 gapl General amino acid permease HISxt + HEXT <-> HIS gap9

YCL025C AGP1 Amino acid permease for most neutral amino acids HlSxt + HEXT <-> HIS gap23

YBR069C VAP1 Amino acid permease HISxt + HEXT <-> HIS vap6

YBR069C TATI Amino acid permease that transports valine, leucine. ILExt + HEXT <-> ILE tatl 2 isleucine, tyrosine, tryptophan, and threonine

YKR039W gapl General amino acid permease lLExt + HEXT <-> ILE gaplO

YCL025C AGP1 Amino acid permease for most neutral amino acids ILExt + HEXT <-> ILE gap32

YBR068C BAP2 Branched chain amino acid permease ILExt + HEXT <-> ILE bap2 2

YDR046C BAP3 Branched chain amino acid permease ILExt + HEXT <-> ILE bap3J

YBR069C VAP1 Amino acid permease ILExt + HEXT <-> ILE vap3

YBR069C TAT1 Amino acid permease that transports valine, leucine, LEUxt + HEXT <-> LEU tatlj isleucine, tyrosine, tryptophan, and threonine

YKR039W gapl General amino acid permease LEUxt + HEXT <- ^■> LEU gapl l

YCL025C AGP1 Amino acid permease for most neutral amino acids LEUxt + HEXT <- •> LEU gap33

YBR068C BAP2 Branched chain amino acid permease LEUxt + HEXT <- ■> LEU bap2J

YDR046C BAP3 Branched chain amino acid permease LEUxt + HEXT <- •> LEU bap3J

YBR069C VAP1 Amino acid permease LEUxt + HEXT <- ■> LEU vap4

YDR508C GNP1 Glutamine permease (high affinity) LEUxt + HEXT <■ ■> LEU gnp7

YKR039W gapl General amino acid permease METxt + HEXT <■ > MET gapl 3

YCL025C AGP1 Amino acid permease for most neutral ammo acids METxt + HEXT <■ > MET gap26

YDR508C GNP1 Glutamine permease (high affinity) METxt + HEXT <■ > MET gnp4

YBR068C BAP2 Branched chain amino acid permease METxt + HEXT <■ > MET bap2J

YDR046C BAP3 Branched chain am o acid permease METxt + HEXT <• > MET bap3J

YGR055W MUPI High-affinity methionine permease METxt + HEXT < > MET mupl

YHL036W MUP3 Low-affinity methionine permease METxt + HEXT <■ > MET mup3

YKR039W gapl General am o acid permease PHExt + HEXT <- > PHEN gapl4

YCL025C AGP1 Amino acid permease for most neutral amino acids PHExt + HEXT <- > PHEN gap29

YOL020W TAT2 Tryptophan permease PHExt + HEXT <- > PHEN tat4

YBR068C BAP2 Branched chain ammo acid permease PHExt + HEXT <- ■> PHEN bap2 5

YDR046C BAP3 Branched chain amino acid permease PHExt + HEXT <■ •> PHEN bap3J

YKR039W gapl General amino acid permease PROxt + HEXT <- ■> PRO gapl 5

YOR348C PUT4 Proline permease PROxt + HEXT <- ■> PRO put6

YBR069C TAT1 Ammo acid permease that transports valine, leucine, TRPxt + HEXT <- > TRP tatl isleucine, tyrosine, tryptophan, and threonine

YKR039W gapl General amino acid permease TRPxt + HEXT <-> TRP gapl 8

YBR069C VAP1 Ammo acid permease TRPxt + HEXT <-> TRP vap2

YOL020W TAT2 Tryptophan permease TRPxt + HEXT <-> TRP tat3

YBR068C BAP2 Branched chain ammo acid permease TRPxt + HEXT <-> TRP bap2J

YDR046C BAP3 Branched chain am o acid permease TRPxt + HEXT <-> TRP bap3 _6

YBR069C TAT1 Amino acid permease that transports valine, leucine, TYRxt + HEXT <-> TYR tatl 7 isleucine, tyrosine, tryptophan, and threonine

YKR039W gapl General amino acid permease TYRxt HEXT <- > TYR gap!9

YCL025C AGP1 Amino acid permease for most neutral amino acids TYRxt HEXT <- > TYR

YBR068C BAP2 Branched chain amino acid permease TYRxt HEXT <- > TYR bap2J

YBR069C VAP1 Amino acid permease TYRxt HEXT <- > TYR vapl

YOL020W TAT2 Tryptophan permease TYRxt + HEXT <- > TYR taf2

YDR046C BAP3 Branched chain amino acid permease TYRxt + HEXT <- > TYR bap3J

YKR039W gapl General amino acid permease VALxt + HEXT <■ ■> VAL gap20

YCL025C AGP1 Ammo acid permease for most neutral ammo acids VALxt + HEXT <• •> VAL gap31

YDR046C BAP3 Branched chain amino acid permease VALxt + HEXT <■ ■> VAL bap3J

YBR069C VAP1 Amino acid permease VALxt + HEXT <■ -> VAL vap5

YBR068C BAP2 Branched chain amino acid permease VALxt + HEXT <■ > VAL bap2J

YFL055W AGP3 Amino acid permease for seπne, aspartate, and SERxt + HEXT <• > SER agp3J glutamate

YCL025C AGP1 Amino acid permease for most neutral amino acids SERxt + HEXT <-> SER gap27

YDR508C GNP1 Glutamine permease (high affinity) SERxt + HEXT <-> SER gnpS

YKR039W gapl General ammo acid permease SERxt + HEXT <-> SER gap!6

YPL265W DIP5 Dicarboxyhc ammo acid permease SERxt + HEXT <-> SER dip 11

YBR069C TAT1 Ammo acid permease that transports valine, leucine, THRXI + HEXT <-> THR tatlj isleucine, tyrosine, tryptophan, and threonine

YCL025C AGP1 Amino acid permease for most neutral amino acids THRxt + HEXT <-> THR gap30

YKR039W gapl General amino acid permease THRxt + HEXT <-> THR gapl 7

YDR508C GNP1 Glutamine permease (high affinity) THRxt + HEXT <-> THR gnp6

YNL268W LYP1 Lysine specific permease (high affinity) LYSxt + HEXT <-> LYS lypl

YKR039W gapl General amino acid permease LYSxt + HEXT <-> LYS gapl2

YLL061W MMPl High affinity S-methylmethionine permease MMETxt + HEXT -> MMET mmpl

YPL274W SAM3 High affinity S-adenosylmethιonιπe permease SAMxt + HEXT -> SAM sam3

YOR348C PUT4 Proline permease GABAxt + HEXT -> GABA puf7

YDL210W uga4 Ammo acid permease with high specificity for GABA GABAxt + HEXT -> GABA uga4

YBR132C AGP2 Plasma membrane carnitine transporter CARxt <-> CAR agp2

YGL077C HNM1 Chohne permease CHOxt + HEXT -> MET hnml

YNR056C BIOS transmembrane regulator of KAPA/DAPA transport BIOxt + HEXT -> BIO bioSa

YDL210W uga4 Ammo acid permease with high specificity for GABA ALAVxt + HEXT -> ALAV ugaS

YKR039W gapl General amino acid permease ORNxt + HEXT <-> ORN gapl b

YEL063C canl Permease for basic amino acids ORNxt + HEXT <-> ORN can lb

Putrescine PTRSCxt + HEXT -> PTRSC ptrup

Spermidine & putrescine SPRMDxt + HEXT -> SPRMD sprupl

YKR093W PTR2 Dipeptide DIPEPxt + HEXT -> DIPEP ptr2

YKR093W PTR2 Oligopeptide OPEPxt + HEXT -> OPEP ptr3

YKR093W PTR2 Peptide PEPTxt + HEXT -> PEPT ptr4 YBR021W FUR4 Uracil URAxt + HEXT -> URA uraupl

Nicotinamide mononucleotide transporter NMNxt + HEXT -> NMN nmnup

YER056C FCY2 Cytosine puπne permease CYTSxt + HEXT -> CYTS fcy2_l

YER056C FCY2 Adenine ADxt + HEXT -> AD fcy2_2

YER056C FCY2 Guanine GNxt + HEXT <-> GN fcy2_3

YER060W FCY21 Cytosine puπne permease CYTSxt + HEXT -> CYTS fcy21_l

YER060W FCY21 Adenine ADxt + HEXT -> AD fcy21_2

YER060W FCY21 Guanine GNxt + HEXT <-> GN fcy21_3

YER060W-A FCY22 Cytosine puπne permease CYTSxt + HEXT -> CYTS fcy22_l

YER060W-A FCY22 Adenine ADxt + HEXT -> AD fcy22_2

YER060W-A FCY22 Guanine GNxt + HEXT <-> GN fcy22_3

YGL186C YGL186 Cytosine purine permease CYTSxt + HEXT -> CYTS cytupl

C

YGL186C YGL186 Adenine ADxt + HEXT -> AD adupl

C

YGL186C YGL186 Guanine GNxt + HEXT <-> GN

C

G-system ADNxt + HEXT -> ADN ncgupl

G-system GSNxt + HEXT -> GSN ncgup3

YBL042C FUI1 Undine permease, G-system URIxt + HEXT -> URI uπup

G-system CYTDxt + HEXT -> CYTD ncguρ4

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

G-system XTSINExt + HEXT -> XTSINE ncgυpό

G-system DTxt + HEXT -> DT ncgup7

G-system DINxt + HEXT -> DIN ncgup8

G-system DGxt + HEXT -> DG πcgup9

G-system DAxt + HEXT -> DA ncguplO

G-system DCxt + HEXT -> DC ncgup 11

G-system DUxt + HEXT -> DU ncgup 12

C-system ADNxt + HEXT -> ADN nccup 1

YBL042C Undine permease, C-system URIxt + HEXT -> URI nccup2

C-system CYTDxt + HEXT -> CYTD nccup3

C-system DTxt + HEXT -> DT nccuρ4

C-system DAxt + HEXT -> DA nccup5

C-system DCxt + HEXT -> DC nccup6

C-system DUxt + HEXT -> DU nccup7

Nucleosides and deoxynucleoside ADNxt + HEXT -> ADN ncupl

Nucleosides and deoxynucleoside GSNxt + HEXT -> GSN ncup2

YBL042C FUI1 Undine permease, Nucleosides and deoxynucleoside URIxt + HEXT -> URI ncup3

Nucleosides and deoxynucleoside CYTDxt + HEXT -> CYTD ncup4

Nucleosides and deoxynucleoside INSxt + HEXT -> INS ncup5

Nucleosides and deoxynucleoside DTxt + HEXT -> DT ncup7

Nucleosides and deoxynucleoside DINxt + HEXT -> DIN ncupδ

Nucleosides and deoxynucleoside DGxt + HEXT -> DG ncuρ9

Nucleosides and deoxynucleoside DAxt + HEXT -> DA ncup 10

Nucleosides and deoxynucleoside DCxt + HEXT -> DC ncup 11

Nucleosides and deoxynucleoside DUxt + HEXT -> DU ncup 12

Hypoxanthine HYXNxt + HEXT <-> HYXN hyxnup

Xanthine XANxt <-> XAN xaπup

Metabolic By-Products YCR032W BPH 1 Probable acetic acid export pump, Acetate transport ACxt + HEXT <-> AC acup

Formate transport FORxt <-> FOR forup

Ethanol transport ETHxt <-> ETH ethup

Succinate transport SUCCxt + HEXT <-> SUCC succup

YKL217W JE 1 Pyruvate lactate proton symport PYRxt + HEXT -> PYR jeπl_l

Other Compounds

YHL016C dur3 Urea active transport UREAxt + 2 HEXT <-> UREA dur3

YGR121C MEP1 Ammonia transport NH3xt <-> NH3 mepl

YNL142W MEP2 Ammonia transport, low capacity high affinity NH3xt <-> NH3 mep2

YPR138C MEP3 Ammonia transport, high capacity low affinity NH3xt <-> NH3 meρ3

YJL129C trkl Potassium transporter of the plasma membrane, high Kxt + HEXT <-> K trkl affinity, member of the potassium transporter (TRK) family of membrane transporters

YBR294W SUL1 Sulfate permease SLFxt -> SLF sull

YLR092W SUL2 Sulfate permease SLFxt -> SLF su!2

YGR125W YYGGRRI125 Sulfate permease SLFxt -> SLF sulup

W

YML123C pho84 inorganic phosphate transporter, transmembrane protein Plxt + HEXT <-> PI pho84

Citrate CITxt + HEXT <-> CIT citup

Dicarboxylates FUMxt + HEXT <-> FUM fumup

Fatty acid transport C140xt -> C140 faupl

Fatty acid transport C160xt -> C160 faup2

Fatty acid transport C161xt - C161 faup3

Fatty acid transport C180xt -> C180 faup4 Fatty acid transport C18lxt -> C181 faupS a-Kctoglutarate AKGxt + HEXT <-> AKG akgup

YLR138W NHA1 Putative Na+/H+ antiporter NAxt <-> NA + HEXT πhal

YCR028C FEN2 Pantothenate PNTOxt + HEXT <-> PNTO fen2

ATP drain flux for constant maintanence requirements ATP -> ADP + PI atpmt

YCR024c-a PMP1 H+-ATPase subunit, plasma membrane ATP -> ADP + PI + HEXT pmpl

YEL017c-a PMP2 H+-ATPase subunit, plasma membrane ATP -> ADP + PI + HEXT pmp2

YGL008C PMA1 H+-transportιng P-type ATPase, major isoform, plasma ATP -> ADP + PI + HEXT pmal membrane

YPL036w PMA2 H+-transportιng P-type ATPase, minor isoform, plasma ATP -> ADP + PI + HEXT pma2 membrane

Glyceraldehyde transport GLALxt <-> GLAL glaltx

Acetaldehyde transport ACALxt <-> ACAL acaltx

YLR237W THI7 Thiamine transport protein THMxt + HEXT -> THIAMIN thml

YOR071 C YOR071 Probable low affinity thiamine transporter THMxt + HEXT -> THIAMIN thm2

C

YOR192C YORI92 Probable low affinity thiamine transporter THMxt + HEXT -> THIAMIN thm3

C

YIR028W dal4 ATNxt -> ATN dal4

YJR152W da!5 ATTxt -> ATT dal5 MTHNxt <-> MTHN mthup PAPxt <-> PAP papx DTTPxt <-> DTTP dttpx THYxt <-> THY + HEXT thyx GA6Pxt <-> GA6P ga6pup

YGR065C VHT1 H+/bιotιn symporter and member of the atlantoate BTxt + HEXT <-> BT btup permease family of the major facilitator superfamily

AONAxt + HEXT <-> AONA kapaup DANNAxt + HEXT <-> DANNA dapaup OGTxt -> OGT ogtup SPRMxt -> SPRM sprmup PIMExt -> PIME pimeup

Oxygen transport 02xt <-> 02 o2tx Carbon dioxide transport C02xt <-> C02 co2tx

YOR01 1 W AUS1 ERGOSTxt <-> ERGOST ergup YOR01 1 W AUS I Putative sterol transporter ZYMSTxt <-> ZYMST zymup RFLAVxt + HEXT -> RIBFLAV rflup

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

TABLE 3

2NPMBm 2-Nonaprenyl-6- methoxy-1,4- benzoqυinoncM

2NPMMBm 2-Noπaprenyl-3- methyl-6- methoxy-1,4- benzoqumoneM

2NPMP 2-Noπapreπyl-6- methoxyphenol

2NPMPm 2-Nonaprenyl-6- methoxyphenol

M

2NPPP 2-

Nonaprenylphen ol

2PG 2-Phospho-D- glycerate

3DDAH7P 2-Dehydro-3- deoxy-D- arabino- heptonate 7- phosphate

3HPACP (3R)-3-

Hydroxypalmito yl-[acy! -earner protein]

3PG 3-Phosρho-D- glycerate

3PSER 3 -Phosphosenne

3PSME 5-0-(l-

Carboxyvinyl)-

3- phosphoshikima te

4HBZ 4-

Hydroxybenzoat

4HLT 4-Hydroxy-L- threomne

4HPP 3-(4-

Hydroxyphenyl) pyruvate

4PPNCYS (R -

Phosphopaπtoth enoyl-L-cysteme

4PPNTE Pantetheme 4'- phosphate

4PPNTEm Pantetheme 4'- phosphateM

4PPNTO D-4'-

Phosphopantoth enate

5MTA 5'-

Methylthioaden osine

6DGLC D-Gal alpha 1-

>6D-Glucose

A6RP 5-Amιno-6- nbιtylamιno-2,4

(1H. 3H)- py midinedione

A6RP5P 5-Amιno-6-(5'- phosphonbosyla mιno)uracιl

A6RP5P2 5-A ιno-6-(5'- phosphonbityla mιno)uracιl

AACCOA Acetoacetyl-

CoA

AACP Acyl-[acyl- carner-protcin]

AATRE6P alpha,alpha'-

Trehalose 6- phosphate

ABUTm 2-Aceto-2- hydroxy butyrateM

AC Acetate

ACACP Acyl-[acyl- carrier protein]

ACACPm Acyl-[acyl- carrier proteinJM

ACAL Acetaldehyde

ACALm AcetaldehydeM

ACAR 0-

Acetylc arm tine

ACARm 0-

Acetylca itme

M

ACCOA Acetyl-CoA

ACCOAm Acetyl-CoAM

AC LAC 2-Acetolactate

ACLACm 2-AcetolactateM

Acm AcetateM

ACNL 3-

Iπdoleacetonitnl e

ACOA Acyl-CoA

ACP Acyl-carner protein

ACPm Acyl-carner proteinM

ACTAC Acetoacetate

ACTACm AcetoacetateM

ACYBUT gamma-Ami no- gam ma- cyanobutanoate

AD Adenine

ADCHOR 4-amιno-4- deoxychoπsmat

Adm AdenineM

ADN Adenosine

ADNm AdenoπsineM

ADP ADP

ADPm ADPM

ADPRIB ADPπbose

ADPRIBm ADPnboseM

AGL3P Acyl-sn-glycerol -phosphate

AHHMD 2-Amιno-7,8- dιhydro-4- hydroxy-6-

(diphosphooxym ethyl)pteπdιne

AHHMP 2-Amιno-4- hydroxy-6- hydroxymethyl-

7,8- dihy drop tend ine

AHM 4-Amιno-5- hydroxymelhyl-

2- methylpynmidin

AHMP 4-Amιno-2- methyl-5- phosphomethylp ynmi ine

AHMPP 2-Methyl-4- amtno-5- hydroxymethylp ynmidine diphosphate

AHTD 2-Amιπo-4- hydroxy-6-

(erythro-1,2,3- tnhydroxypropy

0- dihydropteridine tnphosphate

AICAR l -(5--

Phosphoπbosyl)

-5-amιno-4- lmidazolecarbox amide

AIR Aminoimidazole nbotide

AKA 2-Oxoadιρate

AKA 2-OxoadιpateM

ATRP Pl,P4-Bιs(5'- adenosyl) tetraphosphate

ATT Allantoate bALA beta-Atamne

BASP 4-Phospho-L- aspartate bDG6P beta-D-Glucose

6 -phosphate bDGLC beta-D-Glucose

BIO Biotin

BT Biotin

C100ACP Decanoyl-[acp]

C120ACP Dodecanoyl-

[acyl-carπer protein]

C 120 ACPm Dodecanoyl-

[acyl-carner proteinjM

CHO My stic acid

C140ACP Myπstoyl-[acyl- carner protein]

C140ACPm Myπstoyl-[acyl- carrier proteιn]M

C141ACP Tetradecenoyl-

[acyl-carπer protein]

C141ACPm Tetradecenoyl-

[acyl-carner proteιn]M

C160 Palm l tat e

C160ACP Hexadecanoyl-

[acp]

C 160 ACPm He adecanoyl-

[acp]M

C161 1-Hexadecene

C16IACP Palmιtoyl-[acyl- carrier protein]

ClόlACPm Palmιtoyl-[acyl- carner proteinjM

C16A C 16_aldehydes

C180 Stearate

C180ACP Stearoyl-[acyl- carner protein]

C180ACPm Stearoyl-[acyl- carrier protein ]M

C181 1 -Octadecene

C181ACP Oleoyl-[acyl- carner protein]

C181ACPm Oleoyl-[acyl- carner protein] M

C 182 ACP Linolenoyl-

[acyl -carrier protein]

C182ACPm Lmolenoyl-

[acyl-carπer proteιn]M

CAASP N-Carbamoyl-L- aspartate

CAIR l-(5-Phospho-

D-πbosyl)-5- amιno-4- imidazolecarbox ylate

CALH 2-(3-Carboxy-3- amιnopropyl)-L- histidine cAMP S'.S'-Cyclic

AMP

CAP Carbamoyl phosphate

CAR Carmtine

CARm CarnitineM

CBHCAP 3-

Isopropyl malate

CBHCAPm 3-

DATP dATP

DB4P L-3,4-

Dιhydroxy-2- butaπone 4- phosphate

DC Deoxycytidine

DCDP dCDP

DCMP dCMP

DCTP dCTP

DFUC alpha-D-

Fucoside

DG Deoxyguanosine

DGDP dGDP

DGMP dGMP

DGPP Diacylglycerol pyrophosphate

DGTP dGTP

DHF Dihydrofolate

DHFm DihydrofolateM

DHMVAm (R)-2,3- dιhydroxy-3- methylbutanoate

M

DHP 2-Amιno-4- hydroxy-6-(D- erythro- 1,2,3 - tπhydroxypropy l)-7,8- dihy drop tend me

DHPP Dihydroneopten π phosphate

DHPT Dihydropteroate

DHSK 3-

Dehydroshikima te

DHSP Sphingamne 1- phosphate

DHSPH 3-

Dehydrosphinga nine

DHVALm (R)-3-Hydroxy-

3-methyl-2- oxobutanoateM

DIMGP D-erythro-1-

(lmιdazol-4- y glycerol 3- phosphate

DIN Deoxyinosine

DIPEP Dipeptide

DISAC1P 2,3-bιs(3- hydroxytetradec anoyl)-D- glucosaminyl- l ,6-beta-D-2,3- bιs(3- hydroxytetradec anoyl)-beta-D- glucosammyl 1- phosphate

DLIPOm Dihydrolipoami deM

DMPP Dimethylallyl diphosphate

DMZYMST 4,4-

Dimethylzymost erol

DOL Dolichol

DOLMANP Dolichyl beta-

D-mannosyl phosphate

DOLP Dolichyl phosphate

DOLPP Dehydrodohchol diphosphate

DOROA (S)-

Dihydroo rotate

DPCOA Dephospho-CoA

DPCOAm Dephospho-

CoAM

DPTH 2-[3-Carboxy-3-

bisphosphate

LIPOm LipoamideM

LIPX Lipid X

LLACm (S)-LactateM

LLCT L-Cyslathionine

LLTRNA L-lysyl- tRNA(Lys)

LLTRNAm L-lysyl- tRNA(Lys)M

LNST Lanosterol

LTRNA tRNA(Lys)

LTRNAm tRNA(Lys)M

LYS L-Lysine

LYSm L-LysineM

MAACOA a-

Methylacetoacet yl-CoA

MACAC 4-

Mateylacetoacet ate

MACOA 2-Methylprop-2- enoyl-CoA

MAL Malate

MALACP Malonyl-[acyl- carner protein]

MALACPm Malonyl-[acyl- carner proteιn]M

MALCOA Malonyl-CoA

MALm MalateM

MALT Malonate

MALTm MaloπateM

MAN alpha-D-

Mannose

MAN I P alpha-D-

Mannose 1- phosphate

MAN2PD beta-D-

Mannosyidiacet ytchltobiosyldip hosphodohchol

MAN6P D-Mannose 6- phosphate

MANNAN Mannan

MBCOA Methylbutyryl-

CoA

MCCOA 2-Methylbut-2- enoyl-CoA

MCRCOA 2-Methylbut-2- enoyl-CoA

MDAP Meso- diaminopimelate

MELI Melibiose

MELT Melibutol

MET L-Melhiomne

METH Methanelhiol

METHF 5,10-

Methenyltetrahy drofolate

METHFm 5,10-

Methenyltetrahy drofolateM

METTHF 5,10-

Methylenetetrah ydrofolate

METTHFm 5,10-

Methylenetetrah ydrofolaleM

MGCOA 3-

Methylglutacon yl-CoA

MHIS N(paι)-Methyl-

L-histidine

MHVCOA a-Methyl-b- hydroxyvaleryl-

CoA

Ml myo-Inositol

MI 1P 1 L-myo-Inositol

1 -phosphate

ribonucleotide

NAMNm Nicotinate D- nbonucleotideM

NAORNm N2-Acetyl-L- ornithineM

NH3 NH3

NH3m NH3M

NH4 NH4+

NPP all-traπs-

Nonaprenyl diphosphate

NPPm all-traπs-

Nonaprenyl diphosphateM

NPRAN N-(5-Phospho-

D- rιbosyl)anthranιl ate

02 Oxygen

02m OxygenM

OA Oxaloacetate

OACOA 3-Oxoacyl-CoA

OAHSER O-Acetyl-L- homosenne

OAm OxaloacetateM

OBUT 2-Oxobutanoate

OBUTm 2-

OxobutanoateM

OFP Oxidized flavoprotein

OGT Oxidized glutathione

OHB 2-0x0-3- hydroxy-4- phosphobutanoa te

OHm HO-M

OICAP 3-Carboxy-4- methyl-2- oxopentanoate

OICAPm 3-Carboxy-4- methyl-2- oxopentanoateM

OΓVAL (R)-2-

Oxoisovalerate

OIVALm (R)-2-

Oxoisovalerate

M

OMP Orotidine 5 - phosphate

OMVAL 3-Methyl-2- oxobutanoate

OMVALm 3-MethyI-2- oxobutanoateM

OPEP Oligopeptide

ORN L-Orni thine

ORNm L-OmithineM

OROA Orotate

OSLHSER O-Succinyl-L- homosenne

OSUC Oxalosuccinate

OSUCm Oxalosuccmate

M

OTHIO Oxidized thioredoxin

OTHIOm Oxidized thioredoxinM

OXA Oxaloglutarate

OXAm Oxaloglutarate

M

P5C (S)-l-Pyrrolιne-

5 -carboxy late

P5Cm (S)-l-Pyrrohne-

5 -carboxy lateM

P5P Pyndoxine phosphate

PA Phosphatidate

PABA 4-

Aminobenzoate

PAC Phenylacetic

amιno-2,6- dihydroxypyπmi dine

RAF Raffmose

RFP Reduced flavoprotein

RGT Glutathione

RGTm GlutathioneM

RIB D-Ribose

RIBFLAV RiboflavinM

RBOFLAV Riboflavin

RIPm alpha-D-Ribose

1 -phosphateM

RL5P D-Ribulose 5- phosphate

RMN D-Rhamnose

RTHIO Reduced thioredoxin

RTHIOm Reduced thioredoxinM

S Sulfur

S17P Sedoheptulose

1.7- bisphosphate

S23E (S)-2,3-

Epoxysqualene

S7P Sedoheptulose

7 -phosphate

SACP N6-(L-1,3-

Dicarboxypropy l)-L-lysιne

SAH S-Adenosyl-L- homocysteine

SAHm S-Adenosyl-L- homocysteineM

SAICAR l -(5'-

Phosphonbosyl)

-5-amιno-4-(N- succinocarboxa mιde)-ιmιdazole

SAM S-Adenosyl-L- ethiomne

SAMm S-Adenosyl-L- methioπineM

SAMOB S-Adenosyl-4- methylthιo-2- oxobutanoate

SAPm S-

Aminomethyldi hydrolipoylprote M

SER L-Senne

SERm L-SenneM

SLF Sulfate

SLFm SulfateM

SME Shikimate

SME5P Shikimate 3- phosphate

SOR Sorbose

SOR1 P Sorbose 1- phosphate

SOT D-Sorbitol

SPH Sphiπgamne

SPMD Sperm id me

SPRM Sperm ine

SPRMD Sperm id i ne

SQL Squalene sue Sucrose

SUCC Succinate

SUCCm SuccinateM

SUCCOAm Succinyl-CoAM

SUCCSAL Succinate semialdehyde

T3P1 D-

Glyceraldehyde

3 -phosphate

T3P2 Glycerone phosphate

T3P2m Glycerone phosphateM

TAG16P D-Tagatose 1,6- bisphosphate

TAG6P D-Tagatose 6- phosphate

TAGLY Triacylglycerol

TCOA Tetrad ecanoyl-

CoA

TGLP N-

Tetradecanoylgl ycylpeptide

THF Tetrahydrofolate

THFG Tetrahydrofolyl-

[Glu](n)

THFm Tetrahydrofolate

M

THIAMIN Thiamin

THMP Thiamin monophosphate

THPTGLU Tetrahydroptero yltn-L- glutamate

THR L-Threonine

THRm L-ThreonineM

THY Thymine

THZ 5-(2-

Hydroxy ethyl)-

4-methylthιazole

THZP 4-Methyl-5-(2- phosphoethyl)- thiazole

TPI D-myo-inositol

1,4,5- tnsphosphate

TPP Thiamin diphosphate

TPPP Thiamin tnphosphate

TRE alpha,alpha-

Trehaiose

TRE6P alpha,alpha'-

Trehalose 6- phosphate

TRNA tRNA

TRNAG tRNA(Glu)

TRNAGm tRNA(Glu)M

TRNAm tRNAM

TRP L-Tryptophan

TRPm L-TryptophanM

TRPTRNAm L-Tryp tophany 1- tRNA(Trp)M

TYR L-Tyrosine

UDP UDP

UDPG UDPglucose

UDPG23A UDP-2,3-bιs(3- hydroxytetradec anoyl)glucosamι

UDPG2A UDP-3-0-(3- hydroxytetradec anoyl)-D- glucosamine

UDPG2AA UDP-3-0-{3- hydroxytetradec anoyl)-N- acetylglucosami

UDPGAL UDP-D- galactose

UDPNAG UDP-N-acetyl-

D-galactosamine

UDPP Undecaprenyl diphosphate

UGC (-)-

Ureidoglycolate

UMP UMP

UPRG Uropoφhyπnog en III

URA Uracil

UREA Urea UREAC Urea-1- carboxylate

URI Undine

UTP UTP

VAL L-Valine

X5P D-Xylose-5- phosphate

XAN Xanthine

XMP Xanthosme 5 phosphate

XTSINE Xanthosme

XTSN Xanthosme

XUL D-Xylulose

XYL D-Xylose

ZYMST Zymosterol

[0056] Depending upon the particular environmental conditions being tested and the desired activity, a reaction network data structure can contain smaller numbers of reactions such as at least 200, 150, 100 or 50 reactions. A reaction network data structure having relatively few reactions can provide the advantage of reducing computation time and resources required to perform a simulation. When desired, a reaction network data structure having a particular subset of reactions can be made or used in which reactions that are not relevant to the particular simulation are omitted. Alternatively, larger numbers of reactions can be included in order to increase the accuracy or molecular detail of the methods of the invention or to suit a particular application. Thus, a reaction network data structure can contain at least 300, 350, 400, 450, 500, 550, 600 or more reactions up to the number of reactions that occur in or by S. cerevisiae or that are desired to simulate the activity of the full set of reactions occurring in S. cerevisiae. A reaction network data structure that is substantially complete with respect to the metabolic reactions of S. cerevisiae provides the advantage of being relevant to a wide range of conditions to be simulated, whereas those with smaller numbers of metabolic reactions are limited to a particular subset of conditions to be simulated.

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

Table 4. Reactions specific to S. cerevisiae metabolic network glkl_3, hxkl_l, hxk2_l, hxkl_4, hxk2_4, pfkl_3, idhl, idpl_l, idpl_2, idp2_l, idp3_l, idp2_2, idp3_2, IsclR, pycl, pyc2, cyb2, dldl, ncpl, cytr_, cyto, atpl, pmal, pma2, pmpl, pmp2, coxl, rbkl_2, achl_l, achl_2, sfal lR, unkrxllR, pdcl, pdc5, pdc6, lys20, adhlR, adh3R, adh2R, adh4R, adh5R, sfal_2R, psal, pfk26, pfk27, fbp26, gal7R, mell_2, mell_3, mell_4R, mell_5R, mell_6R, mell_7R, fsp2b, sorl, gsyl, gsy2, fksl, fks3, gsc2, tpsl, tps3, tsll, tps2, athl, nthl, nth2, fdhl, tfola, tfolb, 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_lR, uralOR, ura5R, ura3, npkR, furl, fcyl, tdkl, tdk2, urkl_l, urkl_2, urkl_3, deoalR, deoa2R, cddl_l, cddl_2, cdc8R, dutl, cdc21, cmka2R, dcdlR, ura7_2, ura8_2, deglR, puslR, pus2R, pus4R, ural_2R, aral_l, aral_2, gnalR, pcmlaR, qrilR, chsl, chs2, chs3, put2_l, put2, gltl, gdh2, cat2, yatl, mhtl, sam4, ecm40_2, cpa2, ura2_2, arg3, spe3, spe4, amd, amd2_l, atma, msrl, rnas, ded81, hom6_l, cys4, glyl, agtR, gcv2R, sahl, metό, cys3, metl7_l, metl7hR, dph5, met3, metl4, metl7_2, metl7_3, lys21, lys20a, lys3R, lys4R, lysl2R, lysl2bR, amitR, lys2_l, lys2_2, lys9R, lyslaR, krsl, mskl, pro2_l, gpslR, gps2R, pro3_3, pro3_4, pro3_l, pro3_5, dallR, dal2R, dal3R, his4_3, htsl, hmtl, tyrl, etal, cttl, aldό, 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 1, gpxlR, gpx2R, hyrlR, ecm38, nit2_l, nit2_2, nmtl, natl, nat2, bgl2, exgl, exg2, sprl, thi80_l, thi80_2, unkrxn8, phol 1, fmnl_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, clOOsn, cl80sy, cl82sy, faalR, faa2R, faa3R, faa4R, fox2bR, potl_l, erglO_lR, erglO_2R, Gatl_2, Gat2_2, ADHAPR, AGAT, slcl, Gatl_l, Gat2_l, cholaR, cholbR, cho2, opi3_l, opi3_2, ckil, pctl, cptl, ekil, ectl, eptlR, inol, impal, pisl, tori, tor2, vps34, pikl, sst4, fabl, mss4, plcl, pgslR, crdl, dppl, Ippl, hmgsR, hmglR, hmg2R, ergl2_l, ergl2_2, ergl2_3, ergl2_4, erg8, mvdl, erg9, ergl, erg7, unkrxn3, unkrxn4, cdisoa, ergl 1_1, erg24, erg25_l, erg26_l, ergl 1_2, erg25_2, erg26_2, ergl 1_3, ergo, erg2, erg3, erg5, erg4, lcbl, lcb2, tsclO, 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, pmt5, pmtό, kre2, ktrl, ktr2, ktr3, ktr4, ktrό, yurl, hor2, rhr2, cdal, cda2, daga, dakl, dak2, gpdl, nadglR, nadg2R, nptl, nadi, mnadphps, mnadglR, mnadg2R, mnptl, mnadi, heml, bet2, coql, coq2, coxlO, raml, rer2, srtl, mo2R, mco2R, methR, mmthnR, mnh3R, mthfR, mmthfR, mserR, mglyR, mcbhR, moicapR, mproR, mcmpR, macR, macar_, mcar_, maclacR, mactcR, moivalR, momvalR, mpmalRR, mslf, mthrR, maka, aacl, aac3, pet9, mirlaR, mirldR, dicl_2R, dicl_lR, dicl_3, mmltR, moabR, ctpl_lR, ctpl_2R, ctpl_3R, pyrcaR, mlacR, gcaR, gcb, ortlR, crcl, gut2, gpd2, mt3p, mgl3p, fad, mriboR, mdtbR, mmcoaR, mmvlR, mpaR, mppntR, madR, mp pR, mdhfR, mqaR, moppR, msamR, msahR, sfcl, odclR, odc2R, hxtl_2, hxtl0_2, hxtl l_2, hxtl3_2, hxtl5_2, hxtl6_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 1, hxtl 1_1, hxtl3_l, hxtl5_l, hxtl6_l, hxtl7_l, hxt2_l, hxt3_l, hxt4, hxt4_l, hxt5_l, hxt6_l, hxt7_l, hxt8_4, hxt9_l, stll 1, gaupR, mmpl, mltup, mntup, nagup, rmnup, ribup, treup_2, treup_l, xylupR, uga5, bap2_lR, bap3_lR, gap5R, gnp3R, tat7R, vap7R, sam3, put7, uga4, dip9R, gap22R, gap7R, gnplR, gap23R, gap9R, hiplR, vap6R, bap2_4R, bap3_4R, gapl3R, gap26R, gnp4R, muplR, mup3R, bap2_5R, bap3_5R, gapl4R, gap29R, tat4R, ptrup, sprupl, ptr2, ptr3, ptr4, mnadd2, fcy2_3R, fcy21_3R, fcy22_3R, gnupR, hyxnupR, nccup3, nccup4, nccupό, nccup7, ncgup4, ncgup7, ncgupl l, ncgupl2, ncup4, ncup7, ncupll, ncupl2, ethupR, sull, sul2, sulup, citupR, amgupR, atpmt, glaltxR, dal4, dal5, mthupR, papxR, thyxR, gaόpupR, btupR, kapaupR, dapaupR, ogtup, sprmup, pimeup, thml, thm2, thm3, rflup, hnml, ergupR, zymupR, hxtl_5, hxtl0_3, hxtl 1_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, gltup

[0058] A reaction network data structure or index of reactions used in the data structure such as that available in a metabolic reaction database, as described above, can be annotated to include information about a particular reaction. A reaction can be annotated to indicate, for example, assignment of the reaction to a protein, macromolecule or enzyme that performs the reaction, assignment of a gene(s) that codes for the protein, macromolecule or enzyme, the Enzyme Commission (EC) number of the particular metabolic reaction or Gene Ontology (GO) number of the particular metabolic reaction, a subset of reactions to which the reaction belongs, citations to references from which information was obtained, or a level of confidence with which a reaction is believed to occur in S. cerevisiae. A computer readable medium or media of the invention can include a gene database containing annotated reactions. Such information can be obtained during the course of building a metabolic reaction database or model of the invention as described below.

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

[0060] A gene database of the invention can include a substantially complete collection of genes and/or open reading frames in S. cerevisiae or a substantially complete collection of the macromolecules encoded by the S. cerevisiae genome. Alternatively, a gene database can include a portion of genes or open reading frames in S. cerevisiae or a portion of the macromolecules encoded by the S. cerevisiae genome. The portion can be at least 10%, 15%, 20%, 25%, 50%, 75%, 90% or 95% of the genes or open reading frames encoded by the S. cerevisiae genome, or the macromolecules encoded therein. A gene database can also include macromolecules encoded by at least a portion of the nucleotide sequence for the S. cerevisiae genome such as at least 10%, 15%, 20%, 25%, 50%, 75%, 90% or 95% of the S. cerevisiae genome. Accordingly, a computer readable medium or media of the invention can include at least one reaction for each macromolecule encoded by a portion of the S. cerevisiae genome.

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

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

[0064] In the course of developing an in silico model of S. cerevisiae metabolism, the types of data that can be considered include, for example, biochemical information which is information related to the experimental characterization of a chemical reaction, often directly indicating a protein(s) associated with a reaction and the stoichiometry of the reaction or indirectly demonstrating the existence of a reaction occurring within a cellular extract; genetic information which is information related to the experimental identification and genetic characterization of a gene(s) shown to code for a particular protein(s) implicated in carrying out a biochemical event; genomic information which is information related to the identification of an open reading frame and functional assignment, through computational sequence analysis, that is then linked to a protein performing a biochemical event; physiological information which is information related to overall cellular physiology, fitness characteristics, substrate utilization, and phenotyping results, which provide evidence of the assimilation or dissimilation of a compound used to infer the presence of specific biochemical event (in particular translocations); and modeling information which is information generated through the course of simulating activity of S. cerevisiae using methods such as those described herein which lead to predictions regarding the status of a reaction such as whether or not the reaction is required to fulfill certain demands placed on a metabolic network.

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

[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. Annotating a metabolic reaction database with these associations can allow the methods to be used to determine the effects of adding or eliminating a particular reaction not only at the reaction level, but at the genetic or protein level in the context of running a simulation or predicting S. cerevisiae activity.

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

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

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

[0071] The invention further provides a computer readable medium, containing (a) a data structure relating a plurality of S. cerevisiae reactants to a plurality of S. cerevisiae reactions, wherein each of the S. cerevisiae reactions includes a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating the substrate and the product, and (b) a constraint set for the plurality of S. cerevisiae reactions. [0072] Constraints can be placed on the value of any of the fluxes in the metabolic network using a constraint set. These constraints can be representative of a minimum or maximum allowable flux through a given reaction, possibly resulting from a limited amount of an enzyme present. Additionally, the constraints can determine the direction or reversibility of any of the reactions or transport fluxes in the reaction network data structure. Based on the in vivo environment where S. cerevisiae lives the metabolic resources available to the cell for biosynthesis of essential molecules for can be determined. Allowing the corresponding transport fluxes to be active provides the in silico S. cerevisiae with inputs and outputs for substrates and by-products produced by the metabolic network.

[0073] Returning to the hypothetical reaction network shown in Figure 1, constraints can be placed on each reaction in the exemplary format, shown in Figure 3, as follows. The constraints are provided in a format that can be used to constrain the reactions of the stoichiometric matrix shown in Figure 2. The format for the constraints used for a matrix or in linear programming can be conveniently represented as a linear inequality such as

β_j ≤ v, ≤ a, : j = l . . . . n (Eq. 1)

where v-, is the metabolic flux vector, β-, is the minimum flux value and α₃ is the maximum flux value. Thus, α-, can take on a finite value representing a maximum allowable flux through a given reaction or β-, 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 β, to negative infinity and α_D to positive infinity as shown for reaction R₂ in Figure 3. Ifreactions proceed only in the forward reaction β_D is set to zero while α-, is set to positive infinity as shown for reactions Ri , R₃ , R , 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 α_D and β-, 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 α-, and β, to be zero. On the other hand if a substrate is only allowed to enter or exit the cell via transport mechanisms, the corresponding fluxes can be properly constrained to reflect this scenario.

[0074] The in silico S. cerevisiae model and methods described herein can be implemented on any conventional host computer system, such as those based on Intel.RTM. microprocessors and running Microsoft Windows operating systems. Other systems, such as those using the UNIX or LINUX operating system and based on IBM.RTM., DEC.RTM. or Motorola.RTM. 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 column N_{gr0 th} to the stoichiometric matrix to represent fluxes to describe the production demands placed on the metabolic system. Setting this new flux as the objective function and asking the system to maximize the value of this flux for a given set of constraints on all the other fluxes is then a method to simulate the growth of the organism.

[0079] Continuing with the example of the stoichiometric matrix applying a constraint set to a reaction network data structure can be illustrated as follows. The solution to equation 2 can be formulated as an optimization problem, in which the flux distribution that minimizes a particular objective is found. Mathematically, this optimization problem can be stated as:

Minimize Z (Eq. 3) where (Eq. 4)

where Z is the objective which is represented as a linear combination of metabolic fluxes Vj using the weights Cj in this linear combination. The optimization problem can also be stated as the equivalent maximization problem; i.e. by changing the sign on Z. Any commands for solving the optimization problem can be used including, for example, linear programming commands.

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

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

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

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

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

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

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

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

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

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

[0090] The methods of the invention can be used to determine the activity of a plurality of S. cerevisiae reactions including, for example, biosynthesis of an amino acid, degradation of an amino acid, biosynthesis of a purine, biosynthesis of a pyrimidine, biosynthesis of a lipid, metabolism of a fatty acid, biosynthesis of a cofactor, transport of a metabolite and metabolism of an alternative carbon source. In addition, the methods can be used to determine the activity of one or more of the reactions described above or listed in Table 2.

[0091] The methods of the invention can be used to determine a phenotype of a S. cerevisiae mutant. The activity of one or more S. cerevisiae reactions can be determined using the methods described above, wherein the reaction network data structure lacks one or more gene-associated reactions that occur in S. cerevisiae. Alternatively, the methods can be used to determine the activity of one or more S. cerevisiae reactions when a reaction that does not naturally occur in S. cerevisiae is added to the reaction network data structure. Deletion of a gene can also be represented in a model of the invention by constraining the flux through the reaction to zero, thereby allowing the reaction to remain within the data structure. Thus, simulations can be made to predict the effects of adding or removing genes to or from S. cerevisiae. The methods can be particularly useful for determining the effects of adding or deleting a gene that encodes for a gene product that performs a reaction in a peripheral metabolic pathway.

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

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

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

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

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

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

EXAMPLE I Reconstruction of the metabolic network of S. cerevisiae

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

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

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

• The MIPS Comprehensive Yeast Genome Database (CYGD) (Mewes et al., Nucleic Acids Research 30(11: 31-34 (2002));

• The Saccharomyces 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. cerevisiae. Table 5

Amino acid biosynthesis

Strathem et al., The Molecular biology of the yeast Saccharomyces : metabolism and gene 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 Enzvmol. 311 :3-9 (2000);

Dickson, Annu Rev Biochem 67: 27-48 (1998);

Parks, CRC Crit Rev Microbiol 6(4): 301-41 (1978))

Nucleotide Metabolism

Strathem et al., supara (1982))

Oxidative phosphorylation and electron transport

(Nerduyn et al., Antonie Nan Leeuwenhoek 59(1): 49-63 (1991); Overkamp et al., J. of Bacteriol 182(10): 2823-2830 (2000))

Primary Metabolism

Zimmerman et al., Yeast sugar metabolism : biochemistry, genetics, biotechnology, and applications Technomic Pub., Lancaster, PA (1997); Dickinson et al., supra (1999); Strathem et al., supra (1982)) Transport across the cytoplasmic membrane

Paulsen et al., FEBS Lett 430(1-2): 116-125 (1998); Wieczorke et al., FEBS Lett 464(3): 123-128 (1999); Regenberg et al., Curr Genet 36(6): 317-328 (1999); Andre, Yeast 11(16): 1575-1611 (1995))

Transport across the mitochondnal membrane

Palmieri et al., J Bioenerg Biomembr 32(1): 67:77 (2000);

Palmieri et al., Biochim Biophys Acta 1459(2-3): 363-369 (2000);

Palmieri et al., J Biol Chem 274(32):22184-22190 (1999);

Palmieri et al., FEBS Lett 417(1): 114-118 (1997);

Paulsen et al., supra (1998);

Pallotta et al., FEBS Lett 428(3): 245-249 (1998);

Tzagologg et al. Mitochondria Plenum Press, New York (1982); Andre Yeast

11(16): 1575-611 (1995))

[0103] All reactions are localized into the two main compartments, cytosol and mitochondria, as most of the common metabolic reactions in S. cerevisiae take place in these compartments. Optionally, one or more additional compartments can be considered. Reactions located in vivo in other compartments or reactions for which no information was available regarding localization were assumed to be cytosol. All corresponding metabolites were assigned appropriate localization and a link between cytosol and mitochondria was established through either known transport and shuttle systems or through inferred reactions to meet metabolic demands.

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

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

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

EXAMPLE II Calculation of the P/O ratio

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

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

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

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

EXAMPLE III Phenotypic phase plane analysis

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

[0112] For this analysis, O₂ 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 (O₂ uptake rate), with the objective function defined as the growth flux. Further details regarding Phase-Plane Analysis are provided in Edwards et al., Biotechnol. Bioeng. 77:27-36 (2002) and Edwards et al., Nature Biotech. 19:125-130 (2001)).

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

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

[0115] From left to right: [0116] PI: Growth is completely aerobic. Sufficient oxygen is available to complete the oxidative metabolism of glucose to support growth requirements. This zone represents a futile cycle. Only CO₂ is formed as a metabolic by-product. The growth rate is less than the optimal growth rate in region P2. The PI 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 byproduct enabling additional high-energy phosphate bonds via substrate level phosphorylation. With the increase of O₂ 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 (Nan Dijken and Scheffers Yeast 2(2): 123- 7 (1986)). [0124] Line of Optimality: Metabolically, the line of optimality (LO) represents the optimal utilization of the metabolic pathways without limitations on the availability of the substrates. On an oxygen/glucose phenotypic phase plane diagram, LO represents the optimal aerobic glucose-limited growth of S. cerevisiae metabolic network to produce biomass from unlimited oxygen supply for the complete oxidation of the substrates in the cultivation processes. The line of optimality therefore represents a completely respiratory metabolism, with no fermentation by-product secretion and the futile cycle fluxes equals zero.

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

EXAMPLE IV

Calculation of line of optimality and respiratory quotient

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

[0127] The oxygen uptake rate (OUR) and the carbon dioxide evolution rate (CER) are direct indicators of the yeast metabolic activity during the fermentation processes. RQ is a key metabolic parameter that is independent of cell number. As illustrated in Figure 7, if the S. cerevisiae is grown along the line of optimality, LO, its growth is at optimal aerobic rate with all the carbon sources being directed to biomass formation and there are no metabolic by-products secreted except C0 . The calculated RQ along the LO is a constant value of 1.06; the RQ in PI region is less than 1.06; and the RQ in the remaining regions in the yeast PhPP are greater than 1.06. The RQ has been used to determine the cell growth and metabolism and to control the glucose feeding for optimal biomass production for decades (Zeng et al. Biotechnol. Bioeng. 44: 1107-1114 (1994)). Empirically, several researchers have proposed the values of 1.0 (Zigova, J Biotechnol 80: 55-62 (2000). Journal of Biotechnology), 1.04 (Wang et al., Biotechnol & Bioeng 19:69-86 (1977)) and 1.1 (Wang et al., Biotechnol. & Bioeng. 21:975-995 (1979)) as optimal RQ which should be maintained in fed-batch or continuous production of yeast's biomass so that the highest yeast biomass could be obtained (Dantigny et al., Appl. Microbiol. Biotechnol. 36:352-357 (1991)). The constant RQ along the line of optimality for yeast growth by the metabolic model is thus consistent with the empirical formulation of the RQ through on-line measurements from the fermentation industry.

EXAMPLE V Computer simulations

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

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

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

[0131] This example shows how the S. cerevisiae metabolic model can be used to predict optimal flux distributions that would optimize fermentation performance, such as specific product yield or productivity. In particular, this example shows how flux based analysis can be used to determine conditions that would minimize the glucose uptake rate of S. cerevisiae grown on glucose in a continuous culture under anaerobic and under aerobic conditions.

[0132] In a continuous culture, growth rate is equivalent to the dilution rate and is kept at a constant value. Calculations of the continuous culture of S. cerevisiae were performed by fixing the in silico growth rate to the experimentally determined dilution rate, and minimizing the glucose uptake rate. This formulation is equivalent to maximizing biomass production given a fixed glucose uptake value and was employed to simulate a continuous culture growth condition. Furthermore, a non growth dependent ATP maintenance of 1 mmol/gDW, a systemic P/O ratio of 1.5 (Verduyn et al. Antonie Van Leeuwenhoek 59(1): 49-63 (1991)), a polymerization cost of 23.92 mmol ATP/gDW, and a growth dependent ATP maintenance of 35.36 mmol 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^"1. The computed by-product secretion rates were then compared to the experimental data (Nissen et al. Microbiology 143(1): 203-18 (1997)). The calculated uptake rates of glucose and the production of ethanol, glycerol, succinate, and biomass are in good agreement with the independently obtained experimental data (Figure 9). The relatively low observed acetate and pyruvate secretion rates were not predicted by the in silico model since the release of these metabolites does not improve the optimal solution of the network.

[0134] It is possible to constrain the in silico model further to secrete both, pyruvate and acetate at the experimental level and recompute an optimal solution under these additional constraints. This calculation resulted in values that are closer to the measured glucose uptake rates (Figure 9A). This procedure is an example of an iterative data-driven constraint-based modeling approach, where the successive incorporation of experimental data is used to improve the in silico model. Besides the ability to describe the overall growth yield, the model allows further insight into how the metabolism operates. From further analysis of the metabolic fluxes at anaerobic growth conditions the flux through the glucose-6-phosphate dehydrogenase was found to be 5.32% of the glucose uptake rate at dilution rate of 0.1 h^"1, which is consistent with experimentally determined value (6.34%) for this flux when cells are operating with fermentative metabolism (Nissen et al., Microbiology 143(1): 203-218 (1997)).

[0135] Optimal growth properties of S. cerevisiae were also calculated under aerobic glucose-limited continuous culture in which the Crabtree effect plays an important role. The molecular mechanisms underlying the Crabtree effect in S. cerevisiae are not known. The regulatory features of the Crabtree effect (van Dijken et al. Antonie Van Leeuwenhoek 63(3- 4):343-52 (1993)) can, however, be included in the in silico model as an experimentally determined growth rate-dependent maximum oxygen uptake rate (Overkamp et al. J. of Bacteriol 182(10): 2823-30 (2000))). With this additional constraint and by formulating growth in a chemostat as described above, the in silico model makes quantitative predictions about the respiratory quotient, glucose uptake, ethanol, CO2, and glycerol secretion rates under aerobic glucose-limited continuous condition (Fig. 10).

EXAMPLE VII

Analysis of deletion of genes involved in central metabolism in S. cerevsiae

[0136] This example shows how the S. cerevisiae metabolic model can be used to determine the effect of deletions of individual reactions in the network.

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

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

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

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

Table 6

Defined Medium Complete Minimal Minimal Minimal

Carbon Source Glucose Glucose Acetate Ethanol

in silico/ in silico/ in silico/ in silico/ References:

Gene in vivo in vivo in vivo in vivo (Minimal media)

ACOl +/+ -/- (Gangloff et al., 1990)

CDC in +/- +/- (Boles et al., 1998)

CITl +/+ +/+ (Kim et al., 1986)

CIT2 +/+ +/+ (Kim et al., 1986)

CIT3 +/+

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

ENOl +/+

EN02" +/- +/-

FBAl * +/- +/-

(Sedivy and Fraenkel, 1985;

FBP1 +/+ +/+ Gancedo and Delgado, 1984)

FUM1 +/+

GLK1 +/+

GND1^M +/- +/-

GND2 +/+

GPMl¹¹ +/- +/-

GPMl +/+

GPM3 +/+

HXK1 +/+

HXK2 +/+ ICL1 +/+ +/+ (Smith et al., 1996)

(Cupp and McAlister-Henn,

IDH1 +/+ +/+

1992)

(Cupp and McAlister-Henn,

IDH2 +/+ +/+

1992)

IDP1 +/+ +/+ (Loftus et al., 1994)

IDP2 +/+ +/+ (Loftus et al., 1994)

IDP3 +/+

KGD1 +/+ +/+ (Repetto and Tzagoloff, 1991)

KGD2 +/+ +/+ (Repetto and Tzagoloff, 1991)

LPD1 +/+

LSC1 +/+ +/+ +/+ (Przybyla-Zawislak et al, 1998)

LSC2 +/+ +/+ +/+ (Przybyla-Zawislak et al, 1998)

MAEl +/+ +/+ +/+ (Boles et al., 1998)

(McAlister-Henn and Thompson,

MDH1 +/+ +/+ +/-

1987)

(McAlister-Henn and Thompson,

MDH2 +/+ +/- +/-

1987)

MDH3 +/+

MLS1 +/+ +/+ +/+ +/+ (Hartig et al., 1992)

OSMJ +/+

PCK1 +/+

PDC1 +/+ +/+ (Flikweert et al., 1996)

PDC5 +/+ +/+ (Flikweert et al., 1996)

PDC6 +/+ +/+ (Flikweert et al., 1996)

PFK1 +/+ +/+ (Clifton and Fraenkel, 1982)

PFK2 +/+ +/+ (Clifton and Fraenkel, 1982)

PGI1 + * +/- +/- (Clifton et al., 1978)

PGK1 * +/- +/-

PGM1 +/+ +/+ (Boles et al., 1994)

PGM2 +/+ +/+ (Boles et al., 1994)

PYCl +/+ +/+ +/- +/- (Wills and Melham, 1985)

PYC2 +/+

(Boles et al., 1998; McAlister-

PYK2 +/+ +/+ +/+ Henn and Thompson, 1987)

RKIl -/-

RPEl +/+

SOLI +/+

SOL2 +/+

SOL3 +/+

SOL4 +/+

(Schaaff-Gerstenschlager and

TALI +/+ +/+ Zimmermann, 1993)

TDHl +/+

TDH2 +/+

TDH3 +/+

(Schaff-Gerstenschlager and

TKLl +/+ +/+ Zimmermann, 1993)

TKL2 +/+

TPIl'¹ +/-

(Schaaff-Gerstenschlager and

ZWFl +/+ +/+ Zimmermann, 1993)

+/- Growth/no growth

# The isoenyzme Pyk2p is glucose repressed, and cannot sustain growth on glucose.

* Model predicts single deletion mutant to be (highly) growth retarded. $ Growth of single deletion mutant is inhibited by glucose.

& Different hypotheses exist for why PgUp deficient mutants do not grow on glucose, e.g. the pentose phosphate pathway in S. cerevisiae is insufficient to support growth and cannot supply the EMP pathway with sufficient amounts of fructose-6-phosphate and glyceraldehydes-3-phosphate (Boles, 1997).

|| The isoenzymes Gpm2p and Gpm3p cannot sustain growth on glucose. They only show residual in vivo activity when they are expressed from a foreign promoter (Heinisch et al., 1998).

## Gndlp accounts for 80% of the enzyme activity. A mutant deleted in GND1 accumulates gluconate-6- phosphate, which is toxic to the cell (Schaaff-Gerstenschlager and Miosga, 1997).

$$ ENOl plays central role in gluconeogenesis whereas EN02 is used in glycolysis (Muller and Entian,

1997).

EXAMPLE VIII

Large-scale gene deletion analysis in S. cerevisiae

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

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

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

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

^■ The initial simulation using the synthetic medium described above, referred to as simulation 1.

^■ False predictions of simulation 1 were subsequently examined to determine if the failure was due to incomplete information in the in silico model, such as missing reactions, the reversibility of reactions, regulatory events, and missing substrates in the synthetic complete medium. In simulation 2, any such additional information was introduced into the in silico model and growth was re-simulated for gene deletion mutants whose in silico phenotype was not in agreement with its in vivo phenotype.

^■ A third simulation was carried out, in which dead end pathways (i.e. pathways leading to intracellular metabolites that were not further connected into the overall network), were excluded from the analysis (simulation 3).

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

1. True negatives (correctly predicted lethal phenotype);

2. False negatives (wrongly predicted lethal phenotype); 3. True positives (correctly predicted viable phenotypes);

4. False positives (wrongly predicted viable phenotypes).

[0146] In simulation 1, 509 out of 599 (85%) simulated phenotypes were in agreement with experimental data. The number of growth retarding genes in simulation 1 was counted to be 19, a surprisingly low number. Only one deletion, the deletion of TPIl, 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 tpi/ mutant could only sustain a specific growth rate of as low as 17% of the wild type. All other growth retarding deletions sustained approximately 99% of wild type growth, with the exception of a deletion of the mitochondrial ATPase that resulted in a specific growth rate of approximately 90% of wild type.

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

1. Medium composition used for the simulation;

2. The biomass composition used in the simulation;

3. Incomplete biochemical information; and

4. Effects of gene regulation.

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

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

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

Genes

Simulation 1 2 involved in dead end 3 pathways

Number of deletion 599 599 530

Predicted Total 509 526 475

True positive 481 496 51 445

True negative 28 30 0 30

False positive 63 59 17 42

False negative 27 14 1 13

Overall Prediction 85.0% 87.8% 89.6%

Positive Prediction 88.4% 89.4% 91.4%

Negative Prediction 50.9% 68.2% 69.8%

[0151] A comprehensive list of all the genes used in the in silico deletion studies and results of the analysis are provided in Table 8. Table 8 is organized according to the categories true negative, false negative, true positive and false positive predictions. Genes highlighted in grey boxes, such as INOJ, 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 |77?R2| were excluded in simulation 3, as the corresponding reactions catalysed steps in dead end pathways.

Table 8

False Negative

ADE3 ADK1 CHOI CH02 DPP1 ERG 3 ERG4 ERGS ERG6 INM1 MET6 OPI3 PPT2 YNK1

True Negative

ACC1 WLIEIJ CDSl DPMI ERG1 ERG7 ERG8 ERG9 ERG11 ERG12 ERG20 ERG25 ERG26 ERG27 FBA1

OTNli GUKl IDI1 IPP1 MVD1 PG11 PGK1 PIS1 PMI40 PSA1 RKIl SAH1 SEC53 TRR1 YDR531 W

True Positive

AAC1 AAC3 AAHI AAT1 AAT2 ABZ1 ACOl ACS1 ADE1 ADE12 ADE16ADE17 ADE2 ADE4 ADE5 ADE6

ADE7 ADE8 ADH1 ADH2 ADH3 ADH4 ADH5 ADK2 A GP1 \AGP2\ A GP3 ALD2 ALD3 ALD4 ALD5 ALD6

[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: ecmlΔ, yill45cΔ, ergl Δ, erg24 Δ, fasl Δ, ural Δ, ura2 Δ, ura3 Δ and ura4 Δ 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 Yill45c are involved in pantothenate synthesis. Ecml catalyses the formation of dehydropantoate from 2- oxovalerate, whereas Yill45c catalyses the final step in pantothenate synthesis from β- alanine and panthoate. In vivo, ecml Δ, and yill 45c Δ mutants require pantothenate for growth (White et al., J Biol Chem 276(14): 10794-10800 (2001)). By supplying pantothenate to the synthetic complete medium in silico, the model predicted a viable phenotype and the growth rate was similar to in silico wild type S. cerevisiae.

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

• Mutants deleted in ERG2 or ERG24 are auxotroph for ergosterol (Silve et al., Mol Cell Biol 16(6): 2719-2727 (1996); Bourot and Karst, Gene 165(1): 97-102 (1995)). Simulating growth on a synthetic complete medium supplemented with ergosterol allowed the model to accurately predict viable phenotypes.

• A deletion of FAS 1 (fatty acid synthase) is lethal unless appropriate amounts of fatty acids are provided, and by addition of fatty acids to the medium, a viable phenotype was predicted.

• Strains deleted in URA1, URA2, URA 3, 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): 383-397 (1997)). Adenine was supplied in the in silico synthetic complete medium. By not supplying adenine, a lethal mutant was predicted. Therefore, this case was considered as a true negative prediction.

[0158] A similar case was the deletion of GLN1, which codes a glutamine synthase, the only pathway to produce glutamine from ammonia. Therefore, glnlΔ 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 glnlΔ mutants are therefore not viable. However, in silico, glutamine was supplied in the complete synthetic medium and growth was predicted. By not supplying glutamine to the synthetic complete medium, the model predicted a lethal phenotype resulting in a true negative prediction.

[0159] Ilv3 and Ilv5 are both involved in branched amino acid metabolism. One may expect that a deletion of ILV3 or ILV5 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 //v3Δ and t/V5Δ 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 CH02 and the latter two steps are catalyzed by phospholipid methyltransferase encoded by OPI3. Strains deleted in CH02 or OPI3 are viable (Summers et al., Genetics 120(4): 909-922 (1988); Daum et al., Yeast 14(16): 1471- 1510 (1998)); however, either null mutant accumulates mono- and dimethylated phosphatidylethanolamine under standard conditions and display greatly reduced levels of phosphatidylcholine (Daum et al., Yeast 15(7): 601-614 (1999)). Hence, phosphatidylethanolamine can replace phosphatidylcholine as a biomass component. In silico, phosphatidylcholine is required for the formation of biomass. One may further speculate on whether an alternative pathway for the synthesis of phosphatidylcholine is missing in the model, since Daum et al., supra (1999) detected small amounts of phosphatidylcholine in cho2Δ mutants. An alternative pathway, however, was not included in the in silico model.

[0162] Deletions in the ergosterol biosynthetic pathways of ERG3, ERG4, ERG5 or ERG6 lead in vivo to viable phenotypes. The former two strains accumulate ergosta-8,22,24 (28)- trien-3-beta-ol (Bard et al., Lipids 12(8): 645-654 (1977); Zweytick et al., FEBS Lett 470(1): 83-87 (2000)), whereas the latter two accumulate ergosta-5,8-dien-3beta-ol (Hata et al., J Biochem (Tokyo^") 94(2): 501-510 (1983)), or zymosterol and smaller amounts of cholesta- 5,7,24-trien-3-beta-ol and cholesta-5,7,22,24-trien-3-beta-ol (Bard et al., supra (1977); Parks et al., Crit Rev Biochem Mol Biol 34(6): 399-404 (1999)), respectively, components that were not included in the biomass equations.

[0163] The deletion of the following three genes led to false positive predictions: RER2, SEC59 and QIR1. 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 , metόA , ynklΔ , pho84Δ. psd2A, tps2Δ) , 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 INM1, 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 INM1 and this addition would have led to a correct prediction . However, an inmlΔimp2Δ 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] MetόΔ mutants are methionine auxotroph (Thomas and Surdin-Kerjan, Microbiol Mol Biol Rev 61(4):503-532 (1997)), and growth may be sustained by the supply of methionine or S-adenosyl-L-methionine. In silico growth was supported neither by the addition of methionine nor by the addition of S-adenosyl-L-methionine. Investigation of the metabolic network showed that deleting MET6 corresponds to deleting the only possibility for using 5-methyltetrahydrofolate. Hence, the model appears to be missing certain information. A possibility may be that the carbon transfer is carried out using 5- methyltetrahydropteroyltri-L-glutamate instead of 5-methyltetrahydrofolate. A complete pathway for such a by-pass was not included in the genome-scale model.

[0167] The function of Ynklp is the synthesis of nucleoside triphosphates from nucleoside diphosphates. YNK1Δ 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 pho84U 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 in silico growth of a PSD2 deleted mutant.

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

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

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

• Sphingohpid 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, KRSl, YDR41C, YGL245W).

• However, pathways of tRNA synthesis were not fully included.

• Heme synthesis was considered in the reconstructed model (HEM1, HEM12, HEM13, HEM15, HEM2, HEM3, HEM4). However no reaction was included that metabolized heme in the model.

• Hence, the incomplete structure of metabolic network may be a reason for false prediction of the phenotype of aurlΔ, IcblΔ, lcb2Δ, tsclOΔ, ded81Δ, htslΔ, krslΔ, ydr41cΔ, ygl245wΔ, hemlΔ, heml2Δ , heml3Δ, hemlSΔ, hem2Δ , hem3Δ , and hem4Δ deletion mutants. Reaction reversibility. The CHOI gene encodes a phosphatidylserine synthase, an integral membrane protein that catalyses a central step in cellular phospholipid biosynthesis. In vivo, a deletion in CHOI is viable (Winzeler et al., Science 285: 901-906 (1999)). However, mutants are auxotrophic for choline or ethanolamine on media containing glucose as the carbon source (Birner et al., Mol Biol Cell 12(4): 997-1007 (2001)).

• Nevertheless, the model did not predict growth when choline and/or ethanolamine were supplied. Further investigation of the genome-scale model showed that this might be due to defining reactions leading from phosphatidylserine to phosphatidylcholine via phosphatidylethanolamine exclusively irreversible. By allowing these reactions to be reversible, either supply of choline and ethanolamine could sustain growth in silico. Gene Regulation

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

[0174] A total of 17 false positive predictions could be related to regulatory events. For a deletion of CDC19, ACS2 or EN02 one may usually expect that the corresponding isoenzymes may take over the function of the deleted genes. However, the corresponding genes, either PYK2, ACSl or ENOl, respectively, are subject to catabolite repression (Boles et al., J Bacteriol 179(9): 2987-2993 (1997); van den Berg and Steensma, Eur J Biochem 231(3): 704-713 (1995); Zimmerman et al., Yeast sugar metabolism : biochemistry, genetics, biotechnology, and applications Technomic Pub., Lancaster, PA (1997)). A deletion of GPMl should be replaced by either of the two other isoenzymes, Gpm2 and Gpm3; however for the two latter corresponding gene products usually no activity is found (Heinisch et al., Yeast 14(3): 203-13 (1998)).

[0175] Falsely predicted growth phenotypes can often be explained when the corresponding deleted metabolic genes are involved in several other cell functions, such as cell cycle, cell fate, communication, cell wall integrity, etc. The following genes whose deletions yielded false positive predictions were found to have functions other than just metabolic function: ACS2, BET2, CDC19, CDC8, CYR1, DIM1, EN02, FADl, GFAl, GPMl, HIPl, MSS4, PET9, PIK1, 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

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Clifton,D., Weinstock,S.B. & Fraenkel,D.G. Glycolysis mutants in Saccharomyces cerevisiae. Genetics 88, 1-11 (1978).

Clifton,D. & Fraenkel,D.G. Mutant studies of yeast phosphofructokinase. Biochemistry 21, 1935-1942 (1982).

Cupp,J.R. & McAlister-Henn,L. Cloning and Characterization of the gene encoding the IDHl subunit of NAD(+)-dependent isocitrate dehydrogenase from Saccharomyces cerevisiae. J. Biol. Chem. 267, 16417-16423 (1992).

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Gangloff,S.P., Marguet,D. & Lauquin.G.J. Molecular cloning of the yeast mitochondrial 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., Muller,S., Schluter,E., Jacoby . & 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. Saccharomyces cerevisiae contains two functional citrate synthase genes. Mol. Cell Biol. 6, 1936-1942 (1986).

Loftus,T.M., Hall,L.V., Anderson,S.L. & McAlister-Henn,L. Isolation, characterization, and disruption of the yeast gene encoding cytosohc NADP-specific isocitrate dehydrogenase. Biochemistry 33, 9661-9667 (1994).

McAlister-Henn,L. & Thompson,L.M. Isolation and expression of the gene encoding yeast mitochondrial malate dehydrogenase. J. Bacteriol. 169, 5157-5166 (1987).

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[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

What is claimed is:

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 intra-system reaction, an exchange reaction, a reaction from a peripheral metabolic pathway, reaction from a central metabolic pathway, a gene associated reaction and a non-gene associated reaction.

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.