Patent Publication Number: US-2003224363-A1

Title: Compositions and methods for modeling bacillus subtilis metabolism

Description:
BACKGROUND OF THE INVENTION  
       [0001] 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  Bacillus subtilis  reaction networks.  
       [0002] Members of the Bacillus genus are Gram-positive, endospore forming, rod-shaped bacteria found in soil and associated water sources.  Bacillus subtilis , the type species of the genus, is a non-pathogenic organism that has been studied for many years as a model organism for many aspects of the biochemistry, genetics and physiology of Gram-positive bacteria, and also used to investigate the simple developmental process of sporulation. Research into  B. subtilis  has more recently been motivated by the widespread use of this organism in the production of industrially important products, including enzymes used in the food, brewing, dairy, textile and detergent industries, as well as nucleosides, antibiotics, vitamins and surfactants.  
       [0003] Over two-thirds of the world market of industrial enzymes is produced by Bacillus species. Commercially important enzymes made by Bacillus include proteases, amylases, glucanases and cellulases, which can be produced in abundance using simple media under industrial fermentation conditions.  B. subtilis , and particularly protease-deficient strains, has also proven useful in the production of recombinant enzymes and proteins, including human growth factors.  
       [0004] 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  B. subtilis . 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 the addition or deletion of one or more genes, it would be useful to be able to predict how these changes would affect cellular behavior.  
       [0005] However, it is currently difficult to make these sorts of predictions for  B. subtilis  because of the complexity of the metabolic reaction network that is encoded by the  B. subtilis  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  B. subtilis  reaction networks, such as its metabolic network, which can be used to simulate many different aspects of the cellular behavior of  B. subtilis  under different conditions. The present invention satisfies this need, and provides related advantages as well.  
       SUMMARY OF THE INVENTION  
       [0006] The invention provides a computer readable medium or media, including: (a) a data structure relating a plurality of  Bacillus subtilis  reactants to a plurality of  Bacillus subtilis  reactions, wherein each of the  Bacillus subtilis  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  Bacillus subtilis  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 the at least one flux distribution is predictive of a  Bacillus subtilis  physiological function. In one embodiment, at least one of the  Bacillus subtilis  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  Bacillus subtilis  reactions is a regulated reaction and the computer readable medium or media further includes a constraint set for the plurality of  Bacillus subtilis  reactions, wherein the constraint set includes a variable constraint for the regulated reaction.  
       [0007] The invention provides a method for predicting a  Bacillus subtilis  physiological function, including: (a) providing a data structure relating a plurality of  Bacillus subtilis  reactants to a plurality of  Bacillus subtilis  reactions, wherein each of the  Bacillus subtilis  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  Bacillus subtilis  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  Bacillus subtilis  physiological function. In one embodiment, at least one of the  Bacillus subtilis  reactions in the data structure is annotated to indicate an associated gene and the method predicts a  Bacillus subtilis  physiological function related to the gene.  
       [0008] The invention provides a method for predicting a  Bacillus subtilis  physiological function, including: (a) providing a data structure relating a plurality of  Bacillus subtilis  reactants to a plurality of  Bacillus subtilis  reactions, wherein each of the  Bacillus subtilis  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, wherein at least one of the  Bacillus subtilis  reactions is a regulated reaction; (b) providing a constraint set for the plurality of  Bacillus subtilis  reactions, wherein the constraint set includes a variable constraint for the regulated reaction; (c) providing a condition-dependent value to the variable constraint; (d) providing an objective function, and (e) 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  Bacillus subtilis  physiological function.  
       [0009] Also provided by the invention is a method for making a data structure relating a plurality of  Bacillus subtilis  reactants to a plurality of  Bacillus subtilis  reactions in a computer readable medium or media, including: (a) identifying a plurality of  Bacillus subtilis  reactions and a plurality of  Bacillus subtilis  reactants that are substrates and products of the  Bacillus subtilis  reactions; (b) relating the plurality of  Bacillus subtilis  reactants to the plurality of  Bacillus subtilis  reactions in a data structure, wherein each of the  Bacillus subtilis  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  Bacillus subtilis  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 the at least one flux distribution is not predictive of a  Bacillus subtilis  physiological function, then adding a reaction to or deleting a reaction from the data structure and repeating step (e), if the at least one flux distribution is predictive of a  Bacillus subtilis  physiological function, then storing the data structure in a computer readable medium or media. The invention further provides a data structure relating a plurality of  Bacillus subtilis  reactants to a plurality of  Bacillus subtilis  reactions, wherein the data structure is produced by the method. 
     
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
     [0010]FIG. 1 shows contour diagrams for glucose uptake (A and D), oxygen uptake (B and E), and carbon dioxide evolution (C and F) rates as a function of ratio of ATP molecules produced per atom of oxygen (PO ratio) and ATP maintenance requirement. The data from Tables 1 and 2 were used as inputs to the system. Growth rates are fixed at 0.11 hr −1  (A-C) or 0.44 hr −1  (D-F).  
     [0011]FIG. 2 shows phase plane analysis for possible byproduct patterns under different oxygen and glucose uptake rates. Units are in mmol/g dry cell weight (DCW)/hr. Depending on which byproducts are allowed to be secreted, different phase planes can be formed. Panel A: Acetate, acetoin, and diacetoin are allowed. Panel B: Butanediol, acetate, acetoin, and diacetoin are allowed. Panel C: Lactate (or ethanol), acetate, acetoin, and diacetoin are allowed. Thin lines in the upper and middle panels are isoclines that represent the locus of points in the two-dimensional space that define the same value of the objective function.  
     [0012]FIG. 3 shows maximum yield graphs for riboflavin (A), subtilisin (B), and amylase (C) as a function of growth rate and PO ratio.  
     [0013]FIG. 4 shows, in part A, carbon flux distributions that maximize biomass, riboflavin, amylase or protease (top, second, third and bottom numbers, respectively, in boxes) production in  B. subtilis  on glucose as the carbon substrate and ammonia as the nitrogen substrate, and, in part B, carbon flux distributions that maximize riboflavin biosynthesis as a function of PO ratio of 0.5, 1.0 and 1.5 (top, second and bottom numbers, respectively, in boxes).  
     [0014]FIG. 5 shows a schematic representation of a hypothetical metabolic network.  
     [0015]FIG. 6 shows mass balance constraints and flux constraints (reversibility constraints) that can be placed on the hypothetical metabolic network shown in FIG. 5.  
     [0016]FIG. 7 shows the stoichiometric matrix (S) for the hypothetical metabolic network shown in FIG. 5.  
     [0017]FIG. 8 shows a balanced pathway for histidine utilization in  B. subtilis.    
     [0018]FIG. 9 shows a flux distribution map comparing results for simulation with a stand-alone metabolic model (lower numbers) and a combined regulatory/metabolic model (upper numbers).  
     [0019]FIG. 10 shows two possible routes for the synthesis of UDP-N-acetylglucosamine.  
     [0020]FIG. 11 shows, in Panel A, an exemplary biochemical reaction network and in Panel B, an exemplary regulatory control structure for the reaction network in panel A. 
    
    
     DETAILED DESCRIPTION OF THE INVENTION  
     [0021] The present invention provides an in silico  B. subtilis  model that describes the interconnections between the metabolic genes in the  B. subtilis  genome and their associated reactions and reactants. The model can be used to simulate different aspects of the cellular behavior of  B. subtilis  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  B. subtilis.    
     [0022] As an example, the  B. subtilis  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  B. subtilis  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 design of improved fermentation conditions and 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  B. subtilis  strains and conditions for their use.  
     [0023] The  B. subtilis  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  Bacillus subtilis  reactants to a plurality of  Bacillus subtilis  reactions, wherein each of the  Bacillus subtilis  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 “ Bacillus subtilis  reaction” is intended to mean a conversion that consumes a substrate or forms a product that occurs in or by a viable strain of  Bacillus subtilis . The term can include a conversion that occurs due to the activity of one or more enzymes that are genetically encoded by a  Bacillus subtilis  genome. The term can also include a conversion that occurs spontaneously in a  Bacillus subtilis  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, reduction, oxidation or changes in location such as those that occur due to a transport reaction that moves a reactant 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 “ Bacillus subtilis  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  Bacillus subtilis . The term can include substrates or products of reactions performed by one or more enzymes encoded by a  Bacillus subtilis  genome, reactions occurring in  Bacillus subtilis  that are performed by one or more non-genetically encoded macromolecule, protein or enzyme, or reactions that occur spontaneously in a  Bacillus subtilis  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  Bacillus subtilis.    
     [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  Bacillus subtilis  reactions or reactants, is intended to mean at least 2 reactions or reactants. The term can include any number of  Bacillus subtilis  reactions or reactants in the range from 2 to the number of naturally occurring reactants or reactions for a particular strain of  Bacillus subtilis . 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  Bacillus subtilis  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  Bacillus subtilis.    
     [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. Alternatively, a boundary can be a variable boundary value as set forth below.  
     [0033] As used herein, the term “variable,” when used in reference to a constraint is intended to mean capable of assuming any of a set of values in response to being acted upon by a constraint function. The term “function,” when used in the context of a constraint, is intended to be consistent with the meaning of the term as it is understood in the computer and mathematical arts. A function can be binary such that changes correspond to a reaction being off or on. Alternatively, continuous functions can be used such that changes in boundary values correspond to increases or decreases in activity. Such increases or decreases can also be binned or effectively digitized by a function capable of converting sets of values to discreet integer values. A function included in the term can correlate a boundary value with the presence, absence or amount of a biochemical reaction network participant such as a reactant, reaction, enzyme or gene. A function included in the term can correlate a boundary value with an outcome of at least one reaction in a reaction network that includes the reaction that is constrained by the boundary limit. A function included in the term can also correlate a boundary value with an environmental condition such as time, pH, temperature or redox potential.  
     [0034] As used herein, the term “activity,” when used in reference to a reaction, is intended to mean the amount of product produced by the reaction, the amount of substrate consumed by the reaction or the rate at which a product is produced or a substrate is consumed. The amount of product produced by the reaction, the amount of substrate consumed by the reaction or the rate at which a product is produced or a substrate is consumed can also be referred to as the flux for the reaction.  
     [0035] As used herein, the term “activity,” when used in reference to  Bacillus subtilis , is intended to mean the magnitude or rate of a change from an initial state of  Bacillus subtilis  to a final state of  Bacillus subtilis . The term can include the amount of a chemical consumed or produced by  Bacillus subtilis , the rate at which a chemical is consumed or produced by  Bacillus subtilis , the amount or rate of growth of  Bacillus subtilis  or the amount of or rate at which energy, mass or electrons flow through a particular subset of reactions.  
     [0036] The invention provides a computer readable medium, having a data structure relating a plurality of  Bacillus subtilis  reactants to a plurality of  Bacillus subtilis  reactions, wherein each of the  Bacillus subtilis  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. The plurality of  Bacillus subtilis  reactions can include reactions of a peripheral metabolic pathway.  
     [0037] 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) and associated anapleurotic reactions.  
     [0038] A plurality of  Bacillus subtilis  reactants can be related to a plurality of  Bacillus subtilis  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  B. subtilis.    
     [0039] The methods and models of the invention can be applied to any strain of  Bacillus subtilis  including, for example, strain 168 or any laboratory or production strain. A strain of  Bacillus subtilis  can be identified according to classification criteria known in the art. Those skilled in the art will be able to recognize a strain as a  Bacillus subtilis  because it will have characteristics that are closer to known strains of  Bacillus subtilis  than to strains of other organisms. Such characteristics can 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.  
     [0040] 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.  
     [0041] 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.  
     [0042] 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.  
     [0043] 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  Bacillus subtilis . 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.  
     [0044] 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.  
     [0045] 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  B. subtilis . 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.  
     [0046] The metabolic demands placed on the  B. subtilis  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  B. subtilis  can be determined by microbiological experiments in which the uptake rate is determined by measuring the depletion of the substrate from the growth medium. 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.  
     [0047] 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 are always 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.  
     [0048] 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 the components listed in Table 3 and ALA, ARG, ASP, ASN, CYS, GLU, GLN, GLY, HIS, ILE, LEU, LYS, MET, PHE, PRO, THR, TRP, TYR, VAL, DATP, DGTP, DCTP, DTTP, GTP, CTP, UTP, PEPTIDO, PS, PE, CL, PG, THIAMIN, GLYTC1, GLYTC2, TEICHU, MTHF, SUCCOA, PTRC, Q, HEMEA, SHEME, FAD, NADP and SPMD.  
     [0049] A demand exchange reactions 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.  
     [0050] 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.  
     [0051] A hypothetical reaction network is provided in FIG. 5 to exemplify the above-described reactions and their interactions. The reactions can be represented in the exemplary data structure shown in FIG. 7 as set forth below. The reaction network, shown in FIG. 5, includes intrasystem reactions that occur entirely within the compartment indicated by the shaded oval such as reversible reaction R 2  which acts on reactants B and G and reaction R 3  which converts one equivalent of B to 2 equivalents of F. The reaction network shown in FIG. 5 also contains exchange reactions such as input/output exchange reactions A xt  and E xt , and the demand exchange reaction, V growth , which represents growth in response to the one equivalent of D and one equivalent of F. Other intrasystem reactions include R 1  which is a translocation and transformation reaction that translocates reactant A into the compartment and transforms it to reactant G and reaction R 6  which is a transport reaction that translocates reactant E out of the compartment.  
     [0052] 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×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 FIG. 5 is shown in FIG. 7. As shown in FIG. 7, 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 R 2  and R 3  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 external ) such that an exchange reaction (R 6 ) 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 R 5 , which produces the internal reactant (E) but does not act on the external reactant (E external ) 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.  
     [0053] 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.  
     [0054] A reaction network data structure can be constructed to include all reactions that are involved in  Bacillus subtilis  metabolism or any portion thereof. A portion of  Bacillus subtilis  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, cell wall metabolism, transport processes and alternative carbon source catabolism. Examples of individual pathways within the peripheral pathways are set forth in Table 8, including, for example, the cofactor biosynthesis pathways for isoprenoid biosynthesis, quinone biosynthesis, enterochelin biosynthesis, riboflavin biosynthesis, folate biosyntheis, coenzyme A biosynthesis, NAD biosynthesis, tetrapyrrole biosynthesis, biotin biosynthesis and thaimin biosynthesis. A reaction network can also include the production of a particular protein such as amylase or its secretion or both as demonstrated in the Examples below.  
     [0055] Depending upon a particular application, a reaction network data structure can include a plurality of  Bacillus subtilis  reactions including any or all of the reactions listed in Table 8. Exemplary reactions that can be included are those that are identified as being required to achieve a desired  B. subtilis  growth rate or activity including, for example, reactions identified as SUCA, GND, PGL, ACKA, ACS, ACNA, GLTA, ENO, FBP, FBA, FRDA, GLK2, ZWF, GAPA, ICDA, MDH, PC, PFKA, PGI1, PGK, PTA, GPMA, ACEE, PYKF, RPIA, ARAD, SDHA1, TKTA1 or TPIA in Table 7. 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  B. subtilis  model of the invention including, for example, reactions identified as ADCSASE, MCCOAC, MGCOAH, ARGA, FORAMD, PMDPHT, PATRAN, PCDCL, PCLIG, NADF, ISPB, HMPK, THIK, BISPHDS, DAPC, METF, MTHIPIS, MTHRKN, MENG, NE1PH, NE3UNK, TNSUNK, SERB, CYSG3, CYSG2, PGPA, PLS2, 3MBACP, 2 MBACP, ISBACP, UDPNA4E, GLMM, MMCOAEP, MMCOAMT or PGL in Table 1. Standard chemical names for the acronyms used to identify the reactants in the reactions of Tables 1 and 7 are provided in Table 9.  
     [0056] For some applications, it can be advantageous to use a reaction network data structure that includes a minimal number of reactions to achieve a particular  B. subtilis  activity under a particular set of environmental conditions. A reaction network data structure having a minimal number of reactions can be identified by performing the simulation methods described below in an iterative fashion where different reactions or sets of reactions are systematically removed and the effects observed. As demonstrated in Example V, such methods were used to identify a reaction network data structure having at least 252 reactions. Accordingly, the invention provides a computer readable medium, containing a data structure relating a plurality of  Bacillus subtilis  reactants to a plurality of  Bacillus subtilis  reactions, wherein the plurality of  Bacillus subtilis  reactions contains at least 252 reactions. In another embodiment, a data structure of the invention can exclude one or more peripheral pathway including, for example, the cofactor biosynthesis pathways for isoprenoid biosynthesis, quinone biosynthesis, enterochelin biosynthesis, riboflavin biosynthesis, folate biosyntheis, coenzyme A biosynthesis, NAD biosynthesis, tetrapyrrole biosynthesis, biotin biosynthesis and thaimin biosynthesis.  
     [0057] 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  B. subtilis  or that are desired to simulate the activity of the full set of reactions occurring in  B. subtilis . A reaction network data structure that is substantially complete with respect to the metabolic reactions of  B. subtilis  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.  
     [0058] A  B. subtilis  reaction network data structure can include one or more reactions that occur in or by  Bacillus subtilis  and that do not occur, either naturally or following manipulation, in or by another organism, such as  Escherichia coli, Haemophilus influenzae, Saccharomyces cerevisiae  or human. Examples of reactions that are unique to  B. subtilis  compared to  Escherichia coli, Haemophilus influenzae, Saccharomyces cerevisiae  and human include those identified in Table 8 as any of BS001 through BS125. It is understood that a  B. subtilis  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 and protein expression in  B. subtilis , for example, when designing or engineering man-made strains.  
     [0059] The reactions included in a reaction network data structure of the invention can be metabolic reactions. A reaction network data structure can also be constructed to include other types of reactions such as regulatory reactions, signal transduction reactions, cell cycle reactions, reactions controlling developmental processes such as sporulation, reactions involved in protein synthesis and regulation thereof, reactions involved in gene transcription and translation, and regulation thereof, and reactions involved in assembly of a cell and its subcellular components.  
     [0060] 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, 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  B. subtilis . 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.  
     [0061] 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 regulated with respect to performing a reaction, being expressed or being degraded; assignment of a cellular component that regulates a macromolecule; 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 Genbank, a site maintained by the NCBI (ncbi.nlm.gov) or the Subtilist database (see, for example, Moszer et al.,  Nucl. Acids Res.  30:62-65 (2002)).  
     [0062] A gene database of the invention can include a substantially complete collection of genes or open reading frames in  B. subtilis  or a substantially complete collection of the macromolecules encoded by the  B. subtilis  genome. Alternatively, a gene database can include a portion of genes or open reading frames in  B. subtilis  or a portion of the macromolecules encoded by the  B. subtilis  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  B. subtilis  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  B. subtilis  genome such as at least 10%, 15%, 20%, 25%, 50%, 75%, 90% or 95% of the  B. subtilis  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  B. subtilis  genome.  
     [0063] An in silico  B. subtilis  model of 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.  
     [0064] Thus, the invention provides a method for making a data structure relating a plurality of  Bacillus subtilis  reactants to a plurality of  Bacillus subtilis  reactions in a computer readable medium or media. The method includes the steps of: (a) identifying a plurality of  Bacillus subtilis  reactions and a plurality of  Bacillus subtilis  reactants that are substrates and products of the  Bacillus subtilis  reactions; (b) relating the plurality of  Bacillus subtilis  reactants to the plurality of  Bacillus subtilis  reactions in a data structure, wherein each of the  Bacillus subtilis  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  Bacillus subtilis  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 the at least one flux distribution is not predictive of  Bacillus subtilis  physiology, then adding a reaction to or deleting a reaction from the data structure and repeating step (e), if the at least one flux distribution is predictive of  Bacillus subtilis  physiology, then storing the data structure in a computer readable medium or media.  
     [0065] 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  B. subtilis  such as the Subtilist database (see, for example, Moszer et al.,  Nucl. Acids Res.  30:62-65 (2002)). In the course of developing an in silico model of  B. subtilis  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  B. subtilis  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.  
     [0066] The majority of the reactions occurring in  B. subtilis  reaction networks are catalyzed by enzymes/proteins, which are created through the transcription and translation of the genes found within the chromosome in the cell. The remaining reactions occur either spontaneously or 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  B. subtilis  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 UDP-N-acetylglucosamine diphosphorylase shown in Table 8, which combines the reactions for glucosamine-1-phosphate N-acetyltransferase and UDP-N-acetylglucosamine diphosphorylase.  
     [0067] The reactions that occur due to the activity of gene-encoded enzymes can be obtained from a genome database which lists genes 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  B. subtilis  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.  
     [0068] 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 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  B. subtilis  activity.  
     [0069] A reaction network data structure of the invention can be used to determine the activity of one or more reactions in a plurality of  B. subtilis  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&#39;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 can either occur spontaneously or 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.  
     [0070] 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 subdviding 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.  
     [0071] 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  B. subtilis . 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.  
     [0072] The invention further provides a computer readable medium, containing (a) a data structure relating a plurality of  Bacillus subtilis  reactants to a plurality of  Bacillus subtilis  reactions, wherein each of the  Bacillus subtilis  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  Bacillus subtilis  reactions.  
     [0073] 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  B. subtilis  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  B. subtilis  with inputs and outputs for substrates and by-products produced by the metabolic network.  
     [0074] Returning to the hypothetical reaction network shown in FIG. 5, constraints can be placed on each reaction in the exemplary format, shown in FIG. 6, as follows. The constraints are provided in a format that can be used to constrain the reactions of the stoichiometric matrix shown in FIG. 7. 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   j ≦α j   :j= 1 . . .  n   (Eq. 1)  
     [0075] where v j  is the metabolic flux vector, β j  is the minimum flux value and α j  is the maximum flux value. Thus, α j  can take on a finite value representing a maximum allowable flux through a given reaction or β j  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 j  to negative infinity and α j  to positive infinity as shown for reaction R 2  in FIG. 6. If reactions proceed only in the forward reaction β j  is set to zero while α j  is set to positive infinity as shown for reactions R 1 , R 3 , R 4 , R 5 , and R 6  in FIG. 6. 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 j  and β j  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 j  and β j  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.  
     [0076] The ability of a reaction to be actively occurring is dependent on a large number of additional factors beyond just the availability of substrates. These factors, which can be represented as variable constraints in the models and methods of the invention include, for example, the presence of cofactors necessary to stabilize the protein/enzyme, the presence or absence of enzymatic inhibition and activation factors, the active formation of the protein/enzyme through translation of the corresponding mRNA transcript, the transcription of the associated gene(s) or the presence of chemical signals and/or proteins that assist in controlling these processes that ultimately determine whether a chemical reaction is capable of being carried out within an organism. Regulation can be represented in an in silico  B. subtilis  model by providing a variable constraint as set forth below.  
     [0077] Thus, the invention provides a computer readable medium or media, including (a) a data structure relating a plurality of  B. subtilis  reactants to a plurality of  B. subtilis  reactions, 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, and wherein at least one of the reactions is a regulated reaction; and (b) a constraint set for the plurality of reactions, wherein the constraint set includes a variable constraint for the regulated reaction.  
     [0078] As used herein, the term “regulated,” when used in reference to a reaction in a data structure, is intended to mean a reaction that experiences an altered flux due to a change in the value of a constraint or a reaction that has a variable constraint.  
     [0079] As used herein, the term “regulatory reaction” is intended to mean a chemical conversion or interaction that alters the activity of a protein, macromolecule or enzyme. A chemical conversion or interaction can directly alter the activity of a protein, macromolecule or enzyme such as occurs when the protein, macromolecule or enzyme is post-translationally modified or can indirectly alter the activity of a protein, macromolecule or enzyme such as occurs when a chemical conversion or binding event leads to altered expression of the protein, macromolecule or enzyme. Thus, transcriptional or translational regulatory pathways can indirectly alter a protein, macromolecule or enzyme or an associated reaction. Similarly, indirect regulatory reactions can include reactions that occur due to downstream components or participants in a regulatory reaction network. When used in reference to a data structure or in silico  B. subtilis  model, the term is intended to mean a first reaction that is related to a second reaction by a function that alters the flux through the second reaction by changing the value of a constraint on the second reaction.  
     [0080] As used herein, the term “regulatory data structure” is intended to mean a representation of an event, reaction or network of reactions that activate or inhibit a reaction, the representation being in a format that can be manipulated or analyzed. An event that activates a reaction can be an event that initiates the reaction or an event that increases the rate or level of activity for the reaction. An event that inhibits a reaction can be an event that stops the reaction or an event that decreases the rate or level of activity for the reaction. Reactions that can be represented in a regulatory data structure include, for example, reactions that control expression of a macromolecule that in turn, performs a reaction such as transcription and translation reactions, reactions that lead to post translational modification of a protein or enzyme such as phophorylation, dephosphorylation, prenylation, methylation, oxidation or covalent modification, reactions that process a protein or enzyme such as removal of a pre- or pro-sequence, reactions that degrade a protein or enzyme or reactions that lead to assembly of a protein or enzyme.  
     [0081] As used herein, the term “regulatory event” is intended to mean a modifier of the flux through a reaction that is independent of the amount of reactants available to the reaction. A modification included in the term can be a change in the presence, absence, or amount of an enzyme that performs a reaction. A modifier included in the term can be a regulatory reaction such as a signal transduction reaction or an environmental condition such as a change in pH, temperature, redox potential or time. It will be understood that when used in reference to an in silico  B. subtilis  model or data structure a regulatory event is intended to be a representation of a modifier of the flux through a  B. subtilis  reaction that is independent of the amount of reactants available to the reaction.  
     [0082] The effects of regulation on one or more reactions that occur in  B. subtilis  can be predicted using an in silico  B. subtilis  model of the invention. Regulation can be taken into consideration in the context of a particular condition being examined by providing a variable constraint for the reaction in an in silico  B. subtilis  model. Such constraints constitute condition-dependent constraints. A data structure can represent regulatory reactions as Boolean logic statements (Reg-reaction). The variable takes on a value of 1 when the reaction is available for use in the reaction network and will take on a value of 0 if the reaction is restrained due to some regulatory feature. A series of Boolean statements can then be introduced to mathematically represent the regulatory network as described for example in Covert et al.  J. Theor. Biol.  2131:73-88 (2001). For example, in the case of a transport reaction (A_in) that imports metabolite A, where metabolite A inhibits reaction R 2  as shown in FIG. 11, a boolean rule can state that:  
     Reg−R 2 =IF NOT( A _in).  (Eq. 2)  
     [0083] This statement indicates that reaction R2 can occur if reaction A_in is not occurring (i.e. if metabolite A is not present). Similarly, it is possible to assign the regulation to a variable A which would indicate an amount of A above or below a threshold that leads to the inhibition of reaction R 2 . Any function that provides values for variables corresponding to each of the reactions in the biochemical reaction network can be used to represent a regulatory reaction or set of regulatory reactions in a regulatory data structure. Such functions can include, for example, fuzzy logic, heuristic rule-based descriptions, differential equations or kinetic equations detailing system dynamics.  
     [0084] A reaction constraint placed on a reaction can be incorporated into an in silico  B. subtilis  model using the following general equation:  
     (Reg-Reaction)*β j   ≦v   j ≦α j *(Reg-Reaction): j=1 . . .  n   (Eq. 3)  
     [0085] For the example of reaction R2 this equation is written as follows:  
     (0)*Reg−R 2 ≦R 2 ≦(∞)*Reg−R 2 .  (Eq. 4)  
     [0086] Thus, during the course of a simulation, depending upon the presence or absence of metabolite A in the interior of the cell where reaction R 2  occurs, the value for the upper boundary of flux for reaction R 2  will change from 0 to infinity, respectively.  
     [0087] With the effects of a regulatory event or network taken into consideration by a constraint function and the condition-dependent constraints set to an initial relevant value, the behavior of the  B. subtilis  reaction network can be simulated for the conditions considered as set forth below.  
     [0088] Although regulation has been exemplified above for the case where a variable constraint is dependent upon the outcome of a reaction in the data structure, a plurality of variable constraints can be included in an in silico  B. subtilis  model to represent regulation of a plurality of reactions. Furthermore, in the exemplary case set forth above, the regulatory structure includes a general control stating that a reaction is inhibited by a particular environmental condition. Using a general control of this type, it is possible to incorporate molecular mechanisms and additional detail into the regulatory structure that is responsible for determining the active nature of a particular chemical reaction within an organism.  
     [0089] Regulation can also be simulated by a model of the invention and used to predict a  B. subtilis  physiological function without knowledge of the precise molecular mechanisms involved in the reaction network being modeled. Thus, the model can be used to predict, in silico, overall regulatory events or causal relationships that are not apparent from in vivo observation of any one reaction in a network or whose in vivo effects on a particular reaction are not known. Such overall regulatory effects can include those that result from overall environmental conditions such as changes in pH, temperature, redox potential, or the passage of time.  
     [0090] The in silico  B. subtilis  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.  
     [0091] 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.  
     [0092] 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&#39;Reilly and Associates, Sebastopol, Calif. (2001).  
     [0093] 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. 5)  
     [0094] 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 5 representing the reaction constraints and mass balances, respectively, effectively define the capabilities and constraints of the metabolic genotype and the organism&#39;s metabolic potential. All vectors, v, that satisfy Equation 5 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.  
     [0095] Objectives for activity of  B. subtilis  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 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 FIG. 7 as an example, adding such a constraint is analogous to adding the additional column V growth  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.  
     [0096] 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 5 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. 6)  
     where  z=Σc   i   ·v   i   (Eq. 7)  
     [0097] where Z is the objective which is represented as a linear combination of metabolic fluxes v i  using the weights c i  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 optimazation problem can be used including, for example, linear programming commands.  
     [0098] 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  B. subtilis  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.  
     [0099] 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.  
     [0100] 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.  
     [0101] 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.  
     [0102] Thus, the invention provides a method for predicting a  Bacillus subtilis  physiological function. The method includes the steps of (a) providing a data structure relating a plurality of  Bacillus subtilis  reactants to a plurality of  Bacillus subtilis  reactions, wherein each of the  Bacillus subtilis  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  Bacillus subtilis  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  Bacillus subtilis  physiological function.  
     [0103] A method for predicting a  Bacillus subtilis  physiological function can include the steps of (a) providing a data structure relating a plurality of  Bacillus subtilis  reactants to a plurality of  Bacillus subtilis  reactions, wherein each of the  Bacillus subtilis  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 wherein at least one of the reactions is a regulated reaction; (b) providing a constraint set for the plurality of reactions, wherein the constraint set includes a variable constraint for the regulated reaction; (c) providing a condition-dependent value to the variable constraint; (d) providing an objective function, and (e) 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  Bacillus subtilis  physiological function.  
     [0104] As used herein, the term “physiological function,” when used in reference to  Bacillus subtilis , is intended to mean an activity of a  Bacillus subtilis  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  Bacillus subtilis  cell to a final state of the  Bacillus subtilis  cell. 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  B. subtilis  cell or substantially all of the reactions that occur in a  B. subtilis  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, production of a cell wall component 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)).  
     [0105] A physiological function of  B. subtilis  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.  
     [0106] 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  B. subtilis  model of the invention.  
     [0107] A physiological function of  B. subtilis  can also be determined using a reaction map to display a flux distribution. A reaction map of  B. subtilis  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  B. subtilis  is shown in FIG. 4.  
     [0108] Thus, the invention provides an apparatus that produces a representation of a  Bacillus subtilis  physiological function, wherein the representation is produced by a process including the steps of: (a) providing a data structure relating a plurality of  Bacillus subtilis  reactants to a plurality of  Bacillus subtilis  reactions, wherein each of the  Bacillus subtilis  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  Bacillus subtilis  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  Bacillus subtilis  physiological function, and (e) producing a representation of the activity of the one or more  Bacillus subtilis  reactions.  
     [0109] The methods of the invention can be used to determine the activity of a plurality of  Bacillus subtilis  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, metabolism of a cell wall component, 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 8.  
     [0110] The methods of the invention can be used to determine a phenotype of a  Bacillus subtilis  mutant. The activity of one or more  Bacillus subtilis  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  Bacillus subtilis . Alternatively, the methods can be used to determine the activity of one or more  Bacillus subtilis  reactions when a reaction that does not naturally occur in  B. subtilis  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  B. subtilis . 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.  
     [0111] A drug target or target for any other agent that affects  B. subtilis  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 α j  or β j  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 α 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.  
     [0112] Once a reaction has been identified for which activation or inhibition produces a desired effect on  B. subtilis  function, an enzyme or macromolecule that performs the reaction in  B. subtilis  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. Pat. No. 5,556,762 which describe the 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.  
     [0113] 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  B. subtilis  model or method of the invention. The effect of a candidate drug or agent on  B. subtilis  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  B. subtilis  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  B. subtilis  physiological function can be predicted.  
     [0114] The methods of the invention can be used to determine the effects of one or more environmental components or conditions on an activity of  Bacillus subtilis . 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 j  or β j  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  B. subtilis  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  B. subtilis.    
     [0115] The invention further provides a method for determining a set of environmental components to achieve a desired activity for  Bacillus subtilis . The method includes the steps of (a) providing a data structure relating a plurality of  Bacillus subtilis  reactants to a plurality of  Bacillus subtilis  reactions, wherein each of the  Bacillus subtilis  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  Bacillus subtilis  reactions; (c) applying the constraint set to the data representation, thereby determining the activity of one or more  Bacillus subtilis  reactions (d) determining the activity of one or more  Bacillus subtilis  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).  
     [0116] The following examples are intended to illustrate but not limit the present invention.  
     EXAMPLE I  
     [0117] This example shows the construction of a substantially complete  B. subtilis  metabolic model. This example also demonstrates the iterative model building approach for identifying  B. subtilis  metabolic reactions that are not present in the scientific literature or genome annotations and adding these reactions to a  B. subtilis  in silico model to improve the range of physiological functions that can be predicted by the model.  
     [0118] A metabolic reaction database was constructed as follows. The metabolic reactions initially included in the metabolic reaction database were compiled from the biochemical literature (Sonenshein et al.,  Bacillus subtilis and other gram - positive bacteria: biochemistry, physiology, and molecular genetics.  ASM Press, Washington, D.C. (1993) and Sonenshein et al.,  Bacillus subtilis and its closest relatives: from genes to cells . ASM Press, Washington, D.C. (2002), from genomic reference databases, including SubtiList (described in Moszer et al.,  Nucleic Acids Res.  30:62-65 and from Kunst et al.,  Nature  390:249-256 (1997).  
     [0119] Additional reactions, not described in the biochemical literature or genome annotation, were subsequently included in the database following preliminary simulation testing and model content refinement. A list of reactions that were not present in the literature or genome annotations but were determined in the course of metabolic model building to be essential to support growth, as defined by the production of required biomass components, of  B. subtilis  under several different fermentation conditions is provided in Table 1.  
                       TABLE 1                               Reaction       Enzyme Name   Reaction Stoichiometry   Name                  Adenosyl   HCYS + ADN &lt;-&gt; SAH   ADCSASE       homocysteinase       (unknown)       Methylcrotonoyl-   3M2ECOA + ATP + CO2 -&gt;   MCCOAC       CoA carboxylase   3MGCOA + PI + ADP       Methylgutaconyl-   3MGCOA -&gt; 3HMGCOA   MGCOAH       CoA hydratase       Formamidase   FAM -&gt; NH3 + FOR   FORAMD       Pyrimidine   A6RP5P2 -&gt; A6RP + PI   PMDPHT       phosphatase       Phospho-   4PPNTE + ATP -&gt; PPI +   PATRAN       pantethiene   DPCOA       adenyly-       transferase       Phosphopanto-   4PPNCYS -&gt; CO2 + 4PPNTE   PCDCL       thenate-cysteine       decarboxylase       Phosphopanto-   4PPNTO + CTP + CYS -&gt;   PCLIG       thenate-cysteine   CMP + PPI + 4PPNCYS       ligase       NAD kinase   NAD + ATP -&gt; NADP + ADP   NADF       Octoprenyl   5 IPPP + FPP -&gt; OPP + 5   ISPB       pyrophosphate   PPI       synthase       (5 reactions)       HMP kinase   AHM + ADP -&gt; AHMP + ADP   HMPK       Thiamin kinase   THMP + ADP &lt;-&gt; THIAMIN +   THIK           ATP       3′-5′   PAP -&gt; AMP + PI   BISPHDS       Bisphosphate       nucleotidase       Succinyl   NS2A6O + GLU &lt;-&gt; AKG +   DAPC       diaminopimelate   NS26DP       aminotransferase       Methylene   METTHF + NADH -&gt; NAD +   METF       tetrahydrofolate   MTHF       reductase       5-   5MTRP &lt;-&gt; 5MTR1P   MTHIPIS       Methylthioribose-       1-phosphate       isomerase       5-   5MTR + ATP -&gt; 5MTRP +   MTHRKN       Methylthioribose   ADP       kinase       S-   DMK + SAM -&gt; MK + SAH   MENG       Adenosylmethionine-       2-DMK       methyltransferase       E-1 (Enolase-   5MTR1P -&gt; DKMPP   NE1PH       phosphatase)       E-3 (Unknown)   DKMPP -&gt; FOR + KMB   NE3UNK       Transamination   KMB + GLN -&gt; GLU + MET   TNSUNK       (Unknown)       Phosphoserine   3PSER -&gt; PI + SER   SERB       phosphatase       Siroheme   SHCL -&gt; SHEME   CYSG3       ferrochelatase       1,3-Dimethyluro-   PC2 + NAD -&gt; NADH + SHCL   CYSG2       (porphyrinogen)       III dehydrogenase       Phosphatidyl-   PGP -&gt; PI + PG   PGPA       glycerol       phosphate       phosphatase A       Acyltransferase   GL3P + 0.035   PLS2           C140ACP + 0.102 C141ACP +           0.717 C160ACP + 0.142           C161ACP + 1.004 C181ACP -&gt;           2ACP + PA       Isovaleryl-CoA   3MBACP + COA &lt;-&gt;   3MBACP       ACP transacylase   2MBCOA + ACP       2-Methylbutyryl-   2MBACP + COA &lt;-&gt;   2MBACP       CoA ACP   2MBCOA + ACP       transacylase       Isobutyryl-CoA   ISBACP + COA &lt;-&gt;   ISBACP       ACP transacylase   ISBCOA + ACP       UDP-N-   UDPNAG -&gt; UDPNAGAL   UDPNA4E       acetylglucosamine       4-epimerase       Phosphogluco-   GA6P &lt;-&gt; GA1P   GLMM       samine mutase       Methylmalonyl-CoA   SMMCOA &lt;-&gt; RMMCOA   MMCOAEP       epimerase       (5.1.99.1)       Methylmalonyl-CoA   RMMCOA -&gt; SUCCOA   MMCOAMT       mutase (5.4.99.2)       6-Phosphoglucono-   D6PGL -&gt; D6PGC   PGL       lactonase                  
 
     [0120] As an example, the formamidase reaction, identified as FORAMD in Table 1, was added to the  B. subtilis  in silico model as follows. It is known from microbiological experiments that histidine can be metabolized as a carbon and nitrogen source in  B. subtilis , indicating that a histidine degradation pathway must be present in the metabolic network (Fisher et al.,  Bacillus subtilis and its closest relatives: from genes to cells,  ASM Press, Washington, D.C. (2002)). Four genes capable of degrading histidine were found in the Subtilist genome sequence and annotation including HUTH, HUTU, HUTI and HUTG. Therefore, to incorporate histidine utilization into the model, the HUTH, HUTU, HUTI and HUTG reactions were added to the stoichiometric matrix and metabolic reaction database to represent the pathway shown in FIG. 8.  
     [0121] A preliminary simulation was run using the stoichiometric matrix having equations for the reactions described in the biochemical literature or genome annotation including HUTH, HUTU, HUTI and HUTG. The simulation was setup with histidine as the only carbon source available to the model by constraining the input/output exchange flux on all other carbon sources to be only positive, whereby only allowing those other compounds to exit the metabolic network. The result of this simulation was that the model could not utilize histidine contrary to experimental evidence. The simulation indicates that the histidine cannot be utilized because the production of formamide (FAM) by the HUTG reaction was found to be unbalanced in the simulation and resulted in a flux of zero for the histidine degradation pathway. There are no reactions in the network capable of using FAM as a substrate to balance the production of FAM by the HUTG reaction. In order to allow the model to represent histidine utilization, a decision was made to balance the production of FAM by adding a reaction that would allow FAM to be utilized by the reaction network. This decision lead to the inclusion of the FORAMD reaction into the network. The simulation was then rerun with this reaction added to the reaction index and hence to the stoichiometric matrix. Addition of the reaction for FORAMD to the stoichiometric matrix was found to balance the production of FAM and to allow flux of mass from histidine through ammonia (NH3) and formate (FOR) to other reactions in the network, thereby simulating histidine utilization as a carbon source for the network in agreement with the true physiology of the organism.  
     [0122] The reactions for methylcrotonoyl-CoA carboxylase (MCCOAC), methylglutaconyl-CoA hydratase (MGCOAH), methylmalonyl-CoA epimerase (MMCOAEP), and methylmalonyl-CoA mutase (MMCOAMT) were also added to the  B. subtilis  stoichiometric matrix and metabolic reaction database based on iterative model building. The MCCOAC, MGCOAH, MMCOAEP, and MMCOAMT reactions were not apparent from the  B. subtilis  biochemical literature or from the Subtilist database annotation. However, it is known from microbiological experiments that leucine, isoleucine, and valine are degraded by  B. subtilis  (Fisher et al., supra (2002)). Therefore, reactions for methylcrotonoyl-CoA carboxylase, methylglutaconyl-CoA hydratase, methylmalonyl-CoA epimerase, and methylmalonyl-CoA mutase were added to complete the degradation pathways. Prior to addition of these reactions the model was not able to accurately predict utilization of leucine, isoleucine, or valine by  B. subtilis . However, once the MCCOAC, MGCOAH, MMCOAEP, and MMCOAMT reactions were added, utilization of leucine, isoleucine, or valine by  B. subtilis  was accurately predicted by the model.  
     [0123] Other reactions added to the metabolic reaction index and stoichiometric matrix during the course of iterative model building included, (1) the pyrimidine phosphatase reaction which, when added, balanced the riboflavin biosynthetic pathway and (2) Isovaleryl-CoA ACP transacylase, 2-methylbutyryl-CoA ACP transacylase and isobutyryl-CoA ACP transacylase which, when added, balanced the production of the multitude of fatty acid structures found in the  B. subtilis  membranes.  
     [0124] The enzyme 6-phosphogluconolactonase (EC: 3.1.3.31 denoted as PGL) is missing from the Subtilist database. This reaction may or may not be essential for cell growth depending on how constraints are set for the reactions involved in the pentose phosphate pathway. For example, if the transaldolase and transketolase reactions are assumed to be reversible, the cell can replenish all of pentose phosphate intermediates without the action of this enzyme. However, if the transaldolase and transketolase reactions are assumed to be irreversible and operate only in the direction from ribulose 5-phosphate to ribose 5-phosphate and xylulose 5-phosphate, then the PGL reaction becomes essential. Since the latter is most likely operative in most cellular systems, the PGL reaction was added to the reaction database and stoichiometric matrix.  
     [0125] The presence of the PGL reaction in the  B. subtilis  reaction network was further supported by the results shown in Example V. It was shown by both in silico simulation and in vivo experimentation that deletion of the PGI or GPM reaction was not lethal but only growth-retarding as the pentose phosphate pathway can compensate partially for the inactivity of the glycolytic functions. However, if the PGL reaction is removed from the metabolic network, no carbon flow via PPP can occur which results in no cell growth. Thus, the PGL reaction must be present to reconcile the results of preliminary simulation without the PGL reaction with the results of Example V.  
     [0126] When the 6-phosphoglucolactonase gene of  Neisseria meningitides  MC58 (nme: NMB1391) was used in a BLAST search of the  Bacillus subtilis  genome, significant homology (E value of 6E-5) was found with the gamA gene. The gamA gene is putatively assigned to be glucosamine-6-phosphate isomerase (EC: 3.5.99.6). These results demonstrate that a  Bacillus subtilis  in silico model can be used to identify a putative activity for  Bacillus subtilis  which can be further used in combination with sequence comparison methods to determine a putative activity for a protein encoded by the  Bacillus subtilis  genome.  
     [0127] The enzyme phosphoglucosamine mutase (EC:5.4.2.10) is also missing from the Subtilist database. This enzyme is involved in the pathway for bacterial cell-wall peptidoglycan and lipopolysaccharide biosynthesis in  E. coli , being an essential step in the pathway for UDP-N-acetylglucosamine biosynthesis. In  B. subtilis , UDP-N-acetylglucosamine is required in the synthesis of glycerol techoic acid, a major cell-wall component. The first step in the glycerol techoic acid is catalyzed by the TagO gene product which links the carrier undecaprenyl phosphate with UDP-N-acetylglucosamine to form undecaprenylpyrophosphate-N-acetylglucosamine. FIG. 10 shows two possible routes for the synthesis of UDP-N-acetylglucosamine in  E. coli . Neither of these two pathways is complete in the  B. subtilis  genome but it is likely that one or both of these two pathways is active in  B. subtilis.    
     [0128] When the  E. coli  glmM gene, encoding phosphoglucosamine mutase (EC:5.4.2.10), was searched using BLAST against the  Bacillus subtilis  genome, two likely candidates were identified: ybbT (E value 1.6e-67) and yhxB (E value 1.2e-7). Annotation for the ybbT gene indicated that its role was “unknown; similar to phosphoglucomutase (EC 5.4.2.2 involved in glycolysis, different from phosphoglucosamine mutase)”. The role of the yhxB was “unknown; similar to phosphomannomutase.” It is therefore very likely that the ybbT gene encodes phosphoglucosamine mutase, and thus the reaction was added to the reaction database and stoichiometric matrix. The pathway from D-glucosamine 6-phosphate to D-glucosaminel-phosphate to N-acetyl-D-glucosaminel-phosphate to N-acetyl-D-glucosamine was chosen to be active in the  B. subtilis  model.  
     [0129] The alternative route via glucosamine-phosphate N-acetyltransferase was not included since no significant homology was found when the glucosamine-phosphate N-acetyltransferase genes from  Drosophila melanogaster  and  Caenorhabditis elegans  were searched using BLAST against the  B. subtilis  genome.  
     [0130] It should be noted that in Table 8, UDP-N-acetylglucosamine diphosphorylase reaction is combined to catalyze both glucosamine-1-phosphate N-acetyltransferase (EC 2.3.1.157) and UDP-N-acetylglucosamine diphosphorylase reaction.  
                           TABLE 2                                   Growth on               Putative   glucose of           Reaction   gene   Knockout       Enzyme Name   Stoichiometry   assigned   mutant                  Cardiolipin   2PG &lt;-&gt; CL + GL   ywnE   Same       synthase       Tetrahydro-   PIP26DX + SUCCOA -&gt;   ykuQ       dipicolinate   COA + NS2A6O       succinylase       Succinyl   NS26DP -&gt; SUCC +   yodQ       diaminopimelate   D26PIM       desuccinylase       DephosphoCoA   DPCOA + ATP -&gt;   ytaG   Same       kinase   ADP + COA       Isoprenyl   IPPP -&gt; DMPP   ypgA   Same       pyrophosphate       isomerase       NAMN adenylyl   NAMN + ATP -&gt; PPI +   yqeJ1       transferase   NAAD       Ketopantoate   AKP + NADPH -&gt;   ylbQ   Slow       reductase   NADP + PANT       Phosphogluco-   G1P &lt;-&gt; G6P   ybbT       mutase       Ribose-5-   RL5P &lt;-&gt; R5P   ywlF       phosphate       isomerase A       Transaldolase A   T3P1 + S7P &lt;-&gt;   ywjH   Same           E4P + F6P       Hydroxymethyl-   3HMGCOA -&gt; ACCOA +   yngG       glutaryl-CoA   AAC       lysase                  
 
     [0131] Table 2 shows 11 reactions that were added to the  B. subtilis  metabolic reaction database and stoichiometric matrix based on putative assignments provided by the Subtilist genome database. The in silico  B. subtilis  model predicted that all of these reactions were essential for  B. subtilis  growth on glucose. Phenotypic studies using gene knockout studies on five of these genes have been performed by the European consortium group MICADO (MICrobial Advanced Database Organization; see, for example, Biaudet et al.,  Comput. Appl. Biosci.  13:431-438 (1997)) and include cardiolipin synthase, dephosphoCoA kinase, isoprenyl pyrophosphate isomerase, ketopantoate reductase and transaldolase A. Eleven reactions in Table 2 are also essential reactions. However, these reactions are slightly different from those in Table 1 in that at least some putative genes can be found. Deletion of any of the reactions in Table 2 should be lethal. However, the in vivo data (five reactions) which is shown in Column 4 of Table 2 indicated that they are not essential. Since the observed results from the gene deletion studies are inconsistent with the results predicted by the model, it is likely that the five genes are incorrectly assigned to the associated reactions.  
     [0132] The complete list of the 792 metabolic reactions included in the database, with the corresponding gene whose product catalyzes each reaction, is provided in Table 8. A list of abbreviations for the 525 metabolites that act as substrates and products of the reactions listed in Table 8 is provided in Table 9. The dimensions of the stoichiometric matrix including all reactions and reactants in the database is, therefore, 525×792. Individual exchange reactions (such as glucose and oxygen) and lumped demand exchange reaction (such as amylase and biomass) are not shown in the Tables 8 and 9 but are included in the reaction matrices for the specific simulations described below.  
     [0133] Thus, this example demonstrates that investigation of the metabolic biochemistry of  B. subtilis  using an in silico model of the invention can be useful for assigning pertinent biochemical reactions to sequences found in the genome; validating and scrutinizing annotation found in a genome database; and determining the presence of reactions or pathways in  B. subtilis  that are not indicated in the annotation of the  B. subtilis  genome or the biochemical literature.  
     EXAMPLE II  
     [0134] This example shows how two parameters, the ratio of the number of ATP molecules produced per atom of oxygen (PO ratio), and the ATP maintenance requirement (M), can be determined using the  B. subtilis  metabolic model described in Example I.  
     [0135] The PO ratio and maintenance requirement (M) cannot be independently determined from fermentation studies alone because these two values are coupled as  
       m   ATP   =m   GLC (12 *PO+ 4)  (Eq. 8)  
     [0136] where m ATP  is the mass of ATP, PO is the PO ratio and m GLC  is the mass of glucose consumed. The PO ratio is a molecular property which remains constant regardless of environmental conditions whereas the maintenance requirement is a macroscopic property which changes under different environmental conditions as described, for example, in Sauer and Bailey,  Biotechnol. Bioeng.  64: 750-754 (1999). However, combinations of both parameters can be determined that are consistent with experimental data using the  B. subtilis  in silico model of the invention. The requirements for certain cellular building blocks, as listed in Table 3, were included in the metabolic flux analysis. The values in Table 3 were obtained from Dauner et al.,  Biotechnol. Bioeng.  76:132-143 (2001).  
               TABLE 3                          REQUIREMENT (μmol/g DCW)                                 Component   D = 0.11 hr −1     D = 0.44 hr −1                                               ATP   35115   39440           NADH   −3015   −4052           NADPH   14405   14512           CO2   −2852   −3011           G6P   712   444           R5P   445   644           E4P   397   460           T3P (T3P1)   428   235           PGA (3PG)   1241   1505           PEP   642   685           PYR   2994   3143           ACA (ACCOA)   2097   1524           OAA (OA)   1785   1998           OGA (AKG)   1236   1309           SER   262   304           GLY   542   629           Cl   411   549           (not included)           Pi   1640   1737           NH4 (NH3)   8066   9275           SO4 (H2SO4)   195   226                      
 
     [0137] The values for certain extracellular fluxes, as listed in Table 4, were also included in the analysis. The values in Table 4 were obtained from Dauner et al.,  Biotechnol. Bioeng.  76:144-156 (2001).  
               TABLE 4                          FLUX (mmol/g DCW/hr)                                 Component   D = 0.11 hr −1     D = 0.44 hr −1                                               Riboflavin   0.02   0.03           Acetate   0.01   0.09           Citrate   0.03   0.06           Diaceytl   0.09   0.17           (Diacetoin)           Glucose   1.98   7.05           CO2   6.69   19.75           O2   6.71   19.71                      
 
     [0138] To estimate the values of P0 and M, linear programming (LP) was used to determine optimal flux for the in silico  B. subtilis  model. FIG. 1 shows the expected glucose uptake rate, O 2  uptake rate and CO 2  evolution rate as a function of PO and M at a growth rate (μ) of 0.11 hr −1  or 0.44 hr −1 . The LP problem was repeatedly solved while varying the values of PO and M at a fixed value for μ. The objective function was to minimize the glucose uptake rate at given values of PO, M and μ. The values for certain extracellular fluxes, as listed in Table 4, were also included in the simulation as additional constraints. For example, at the dilution rate of 0.11 hr −1 , the riboflavin secretion rate was set at 0.11 mmol/g DCW/hr, the acetate secretion rate at 0.01 mmol/g DCW/hr, the citrate secretion rate at 0.03 mmol/g DCW/hr, and the diacetoin secretion rate at 0.09 mmol/g DCW/hr.  
     [0139] A combination of PO and M that minimize the following error function (sum of squares of weighted errors) was searched:  
               SSE   =         (         q   GLC   m     -     q   GLC   e         q   GLC   m       )     2     +       (         q   O2   m     -     q   O2   e         q   O2   m       )     2     +       (         q   CO2   m     -     q   CO2   e         q   CO2   m       )     2              
            q   GLC   m     =     measured                 glucose                 uptake                 rate            
            q   O2   m     =     measured                 oxygen                 uptake                 rate            
            q   CO2   m     =     measured                 cabon                 dioxide                 evolution                 rate            
            q   GLC   e     =     calculated                 glucose                 uptake                 rate            
            q   O2   e     =     calculated                 oxygen                 uptake                 rat            
            q   CO2   e     =     calculated                 cabon                 dioxide                 evolution                 rate               (     Eq   .              9     )                       
 
     [0140]FIG. 1 shows contour diagrams for glucose uptake (top), oxygen uptake (middle), and carbon dioxide evolution (bottom) rates as a function of PO ratio and maintenance requirement. From the analysis, it was found that there were multiple solutions of the combinations of PO and M that fit with the experimental data. Using D=0.11 hr −1 , the best fit values were found at M=4.7 mmol ATP/g DCW/hr when PO=0.5, M=10.3 mmol ATP/g DCW/hr when PO=1.0, and M=16.9 mmol ATP/g DCW/hr when PO=1.5, as shown in FIG. 1B, which shows SSE as a function of M at different PO values. When D=0.44 hr −1 , the best fit values were found at M=6.6 mmol ATP/g DCW/hr when PO=0.5, M=23.3 mmol ATP/g DCW/hr when PO=1.0, and M=42.8 mmol ATP/g DCW/hr when PO=1.5. Therefore, no combination of PO and M that was consistent for both sets of experimental data was found. This discrepancy could be possibly due to experimental errors.  
     [0141] However, the genomic analysis of the electron transport system in  B. subtilis  suggests that the PO ratio is most likely close to 1. This is based on the assumptions that (1) only two electrons are transferred via the NADH dehydrogenase reactions without any proton translocation, (2) two protons are translocated per one electron by the cytochrome oxidase reactions, and (3) the ATP synthase reaction requires four protons to drive phosphorylation of one ATP molecule. This leads to the estimation of M to be 10.3 using the data of D=0.11 hr −1 . The estimated value of M=23.3 with the data of D=10.44 hr −1  appears to be too high and, therefore, unlikely.  
     [0142] Thus, this example demonstrates use of an in silico  B. subtilis  model to predict the ATP maintenance requirement for optimal growth.  
     EXAMPLE III  
     [0143] This example shows how the  B. subtilis  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.  
     [0144] For this analysis, O 2  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 (O 2  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)).  
     [0145] Lactate, acetoin, diacetoin, and butanediol were reported as fermentation byproducts from in vivo experimental results reported in the literature. Production of ethanol and succinate were not confirmed as fermentation byproducts in the reported in vivo experiments.  
     [0146] For each simulation, the maintenance requirement was constrained as M=9.5 mmol ATP/g DCW/hr, the PO ratio was 1 and ammonia was used as the nitrogen source.  
     [0147]FIG. 2A shows the results of the simulation where only acetoin, acetate, and diacetoin were allowed to be secreted as byproducts. In phase 1, both acetate and acetoin are secreted. In phase 2, only acetate is secreted. In phase 3, no organic acids are secreted and all carbon is converted to biomass or CO 2 .  
     [0148]FIG. 2B shows the results of the simulation where butanediol along with acetoin, acetate, and diacetoin were allowed to be secreted as byproducts. In phase 1, acetate and butanediol are secreted. In phase 2, acetate is secreted. In phase 3, no organic acids are secreted. Note that no acetoin or diacetoin can be secreted under this condition.  
     [0149]FIG. 2C shows the results of the simulation where lactate (or ethanol) can be secreted along with acetoin, acetate, and diacetoin. The feasible metabolic region is slightly larger than in FIGS. 2A and 2B, and allows the O 2  uptake rate to be zero. However,  B. subtilis  is strictly aerobic unless nitrate or nitrite is provided. The phase plane in FIG. 2C shows that  B. subtilis  can be anaerobic only if the glucose uptake rate is in the range of 4 to 5 mmol/g DCW/hr. These results indicate that the reason why  B. subtilis  is a strict aerobic is due to its inability to secrete organic byproducts such as lactate ethanol and succinate that can supply the reducing equivalent, NADH.  B. subtilis  can metabolize TCA cycle intermediates as carbon substrates but no TCA cycle intermediates are found as byproducts. This means that the uptake systems for these metabolites work in only one direction and that the transporter systems involved in uptake of TCA cycle intermediates are different from those involved in secretion.  
     [0150] Thus, this example demonstrates that Phase Plane Analysis can be used to determine the optimal fermentation pattern for  B. subtilis , and to determine the types of organic byproducts that can be accumulated under different oxygenation conditions and glucose uptake rates.  
     EXAMPLE IV  
     [0151] This example shows how the  B. subtilis  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 (FBA) can be used to determine conditions that would maximize riboflavin, amylase (amyE), or protease (aprE) yields by  B. subtilis  grown on glucose.  
     [0152] The constraints on the system were set using the following assumptions set forth in Table 5.  
                       TABLE 5                                      q glc  = 10 mmol/g DCW/hr (no limit on O 2 )           PO ratio is set either at 0.5 or 1.0 or 1.5           M = 9.5 mmol ATP/g DCW/hr           One ATP needed to transport one molecule of protein           (amylase or subtilisin)           Biomass composition stays constant, and is the           composition of the growth rate at 0.11 hr −1  (Table 1)           4.323 ATPs per peptide bond formed           Both Spase and SSPase are ATP independent, i.e. no ATP           needed to degrade the cleaved signal peptide into           individual amino acids                      
 
     [0153] Table 6 shows the amino acid composition for amylase and subtilisin.  
                                   TABLE 6                                      amylase (amyE)       subtilisin (aprE)                                                 Pre-process   Mature   Pre-process   Mature               Form   Form   Form   Form                                                     Ala   57   50   47   44           Arg   25   24   5   4           Asn   56   56   18   17           Asp   44   44   13   13           Cys   1   1   0   0           Gln   29   29   14   13           Glu   25   25   12   12           Gly   53   51   37   37           His   17   16   8   8           Ile   35   35   21   19           Leu   44   36   22   17           Lys   33   31   25   23           Met   11   10   9   6           Phe   25   20   8   5           Pro   25   23   14   14           Ser   58   56   51   47           Thr   47   46   24   22           Trp   14   14   4   3           Tyr   28   28   16   16           Val   33   32   33   32           ATP   2853   2711   1647   1522                      
 
     [0154] As shown in FIGS.  3 A-C, yields of riboflavin, subtilisin and amylase, respectively, are lower at higher growth rates and at lower PO ratio. These results suggest that one metabolic engineering target is to increase the PO ratio to improve energetic efficiency of carbon substrate utilization.  
     [0155]FIG. 4A shows carbon flux distribution patterns at optimal yield for the above three different cases and optimal biomass case. The flux patterns are very different depending on the choice of objective functions, indicating that different metabolic optimization strategies are needed for different fermentation objectives.  
     [0156] The results shown in FIG. 4B suggest that in order to maximize riboflavin fermentation yield, high flux via the pentose phophate pathway (PPP) is required. The gene deletion study in Example V indicates that  B. subtilis  seems to possess very inefficient PPP. Therefore, the PPP will be a good metabolic engineering target to improve riboflavin fermentation yield.  
     [0157] Thus, this Example demonstrates use of an in silico  B. subtilis  model for the prediction of conditions for optimal production of riboflavin, amylase, or protease when  B. subtilis  is grown on glucose. This example further demonstrates use of the model to identify targets for engineering  B. subtilis  for improved fermentation yield.  
     EXAMPLE V  
     [0158] This example shows how the  B. subtilis  metabolic model can be used to determine the effect of deletions of individual reactions in the network.  
     [0159] For this analysis, the objective function was the basic biomass function described in Table 3 in Example II except that the following additional metabolites were included in the biomass function: ALA, ARG, ASP, ASN, CYS, GLU, GLN, GLY, HIS, ILE, LEU, LYS, MET, PHE, PRO, THR, TRP, TYR, VAL, DATP, DGTP, DCTP, DTTP, GTP, CTP, UTP, PEPTIDO, PS, PE, CL, PG, THIAMIN, GLYTC1, GLYTC2, TEICHU, MTHF, SUCCOA, PTRC, Q, HEMEA, SHEME, FAD, NADP, SPMD. Since these metabolites were included in the modified biomass function at very low values, the quantitative changes in flux and growth rates due to this change in the biomass composition were insignificant. However, the addition of these biomass constituents ensured that the central and peripheral pathways leading to the synthesis of these metabolites were active and that inability to produce any of these metabolites would result in lethality. This representation is advantageous for determining the impact of deletion of a particular gene or reaction, that is not represented in the lumped biomass function, on the overall cell growth. For example, thiamin was not involved in calculating the composition of cellular building blocks in Example II. Therefore, in simulations with the lumped biomass function, the effect of deletions of thiamin biosynthetic genes cannot be addressed. However, thiamin serves as the coenzyme for a large number of enzyme systems in the metabolism of carbohydrates and amino acids such as pyruvate dehydrogenase, and deletions of any of the thiamin biosynthetic genes should be lethal.  
     [0160] For the simulations in this example, the uptake rates for oxygen, nitrogen, sulfate and phosphate were set very high and were essentially unlimited. The glucose uptake rate was set at 10 mmol/g DCW/hr. PO ratio was set at 1.375. The CYOA reaction was set not to generate protons as QH2+0.5 O2−&gt;Q. Additionally, the constraints were set on the following reactions:  
     [0161] 0&lt;ATOB&lt;10 (ATOB is irreversible)  
     [0162] PTA&lt;ACxtI (acetate uptake rate)  
     [0163] ACS&lt;ACxtI (acetate uptake rate)  
     [0164] KBL2&lt;THRxtI (threonine uptake rate)  
     [0165] HUTH&lt;HISxtI (histidine uptake rate)  
     [0166] MMCOAMT&lt;LEUxtI (leucine uptake rate)  
     [0167] HMGCOAL&lt;VALxtI (valine uptake rate)  
     [0168] SFCA (NAD-malic enzyme reaction)=0 (not active under glycolytic conditions)  
     [0169] MAEB (NADP-malic enzyme reaction)=0 (not active under glycolytic conditions)  
     [0170] PCKA (PEP carboxykinase reaction)=0 (not active under glycolytic conditions)  
     [0171] Simulations were conducted in which all 663 unique reactions were deleted one at a time. Of these, 252 reactions were determined to be essential for growth on glucose minimal medium. These results indicate that a high degree of redundancy exists in the  B. subtilis  metabolic network, such that inactivity of certain metabolic reactions can be compensated. The essential reactions are marked as “E” in Table 8.  
     [0172] It must be noted that a minimal reaction set is different from a minimal gene set for cellular growth and function. Also deletion of a reaction is different from deletion of a gene. For example, the ACEE reaction is a lumped reaction catalyzed by enzymes encoded by four genes, pdhABCD. Therefore, deletion of ACEE is equivalent to deletion of the four genes. Conversely, some genes encode enzymes that carry out multiple reactions. In these cases, deletion of any one of the associated reactions may not be lethal whereas deletion of the gene may be. For example, the adk (adenylate kinase) gene reaction is represented to catalyze four reactions: ADK1, ADK2, ADK3 and ADK4. Deletion of any of these reactions is not lethal for cell growth on glucose but deletion of the adk gene is lethal. At least one of the four ADK reactions is essential for growth on glucose minimal medium. In Table 8, all four ADK reactions are indicated as nonessential. Similar cases can be found in phosphate transport reactions (PIUP1 and PIUP2), CTP synthetase reactions (PYRG1 and PYRG2) and transketolase II reactions (TKTB1 and TKTB2) in which only one in each set is essential.  
     [0173] There are 17 reactions, marked “R” in Table 9, that were determined to be important for growth, in that their deletion led to growth retardation.  
     [0174] Table 7 shows a comparison of the results of the in silico gene deletion study with the experimental results of mutants grown on glucose minimal medium for some selected reactions in central carbon metabolism. As shown in Table 7, there exists a good qualitative correlation between the predicted in silico result and the observed experimental result.  
                               TABLE 7                                   In silico   In vivo       Reaction   Enzyme   Stoichiomety   Prediction   Result                  SUCA   2-Ketoglutarate   AKG + NAD +   +   +           dehyrogenase   COA -&gt; CO2 +   +   +               NADH + SUCCOA       GND   6-   D6PGC + NADP -&gt;   +   No           Phosphogluconate   NADPH + CO2 +       data           dehydrogenase   RL5P           (decarboxylating)       PGL   6-   D6PL -&gt; D6PGC   +   No           Phosphoglucono-           data           lactonase       ACKA   Acetate kinase A   ACTP + ADP &lt;-&gt;   +   −               ATP + AC       ACS   Acetyl-CoA   ATP + AC +   +   No           synthetase   COA -&gt; AMP +       data               PPI + ACCOA       ACNA   Aconitase A   CIT &lt;-&gt; ICIT   −   −       GLTA   Citrate synthase   ACCOA + OA -&gt;   −   −               COA + CIT       ENO   Enolase   2PG &lt;-&gt; PEP   Reduced   No                       data       FBP   Fructose-1, 6-   FDP -&gt; F6P + PI   +           bisphosphatase       FBA   Fructose-1, 6-   FDP &lt;-&gt; T3P1 +   Reduced   No           bisphosphatate   T3P2       data       FRDA   Fumurate   FUM + FADH -&gt;   +   No           reductase   SUCC + FAD       data       GLK2   Glucokinase   GLC + ATP -&gt;   +   +               G6P + ADP       ZWF   Glucose 6-   G6P + NADP &lt;-&gt;   +   No           phosphate-1-   D6PGL + NADPH       data           dehydrogenase       GAPA   Glyceraldehyde-   T3PI + PI +   −   −           3-phosphate   NAD &lt;-&gt;           dehydrogenase-A   NADH + 13DPG           complex       ICDA   Isocitrate   ICIT + NADP &lt;-&gt;   −   No           dehydrogenase   CO2 + NADPH +       data               AKG       MDH   Malate   MAL + NAD &lt;-&gt;   +   No           dehydorgenase   NADH + OA       data       PC   Pyruvate   PYR + CO2 -&gt;   −   −           carboxylase   OA + PI       PFKA   Phospofructo-   F6P + ATP -&gt;   Reduced   No           kinase   FDP + ADP       data       PGI1   Phosphoglucose   G6P &lt;-&gt; F6P   Reduced   Slow           isomerase       PGK   Phosphoglycerate   13DPG + ADP &lt;-&gt;   −   −           kinase   3PG + ATP       PTA   Phosphotrans-   ACCOA + PI &lt;-&gt;   +   −           acetylase   ACTP + COA       GPMA   Phosphoglycerate   3PG &lt;-&gt; 2PG   Reduced   Very           mutase           slow       ACEE   Pyruvate   PYR + COA +   −   −           dehydrogenase   NAD -&gt; NADH +               CO2 + ACCOA       PYKF   Pyruvate Kinase I   PEP + ADP -&gt;   +   +               PYR + ATP       RPIA   Ribose-5-   RL5P &lt;-&gt; R5P   −   No           phosphate           data           isomerase A       ARAD   Ribulose   RL5P &lt;-&gt; X5P   −   No           phosphate 3-           data           epimerase       SDHA1   Succinate   SUCC + FAD -&gt;   +   No           dehydrogenase   FADH + FUM       data       TKTA1   Transketolase I   R5P + X5P &lt;-&gt;   +   No               T3P1 + S7P       data       TPIA   Triosphosphate   T3PI &lt;-&gt; T3P2   Reduced   No           Isomerase           data                  
 
     [0175] There exist some quantitative discrepancy in the phosphoglycerate mutase (GPM reaction) and phosphoglucose isomerase (PGI) reaction deletion cases. The in silico model predicts growth rates to be reduced by 14% and 2% in the phosphoglycerate mutase and phosphoglucose isomerase (PGI) cases, respectively. In both simulation cases, the growth rates are relatively unaffected despite blockage of the glycolytic steps because carbon metabolites can pass from the upper to the lower glycolytic metabolic pathways via the pentose phosphate pathway. When a GPM-deficient  B. subtilis  mutant was grown in glucose minimal media and observed in vivo, the mutant strain grew extremely slow at growth rates reduced by up to 90% (see, for example, Leyva-Vasquez and Setlow,  J. Bacteriol.  176:3903-3910 (1994)). A PGI-deficient  B. subtilis  mutant grew at 42% of !the wild type growth rate (see, for example, Freese et al.,  Spores V.  Halvorson et al. (ed.), American Society for Microbiology, Washington D.C. pp 212-224, (1972)). These results suggest that  B. subtilis  possess an inefficient pentose phosphate pathway which cannot compensate for the inactivity of glycolysis in these mutants.  
     [0176] There is a discrepancy in the acetate kinase A (ACKA) deletion case between the in silico simulation and in vivo results. In silico simulation predicted that the deletion of ACKA did not affect cell growth on glucose. When a ACKA mutant was grown in minimal medium with excess glucose, the growth rate was 33% of the wild type growth rate suggesting that acetate kinase A is important to deal with excess carbohydrate. It is likely that the blockage of acetate secretion leads to an accumulation of acetyl phosphate, which could be growth inhibitory. (See, for example, Grundy et al.,  J. Bacteriol.  175:7348-7355 (1993)). The effect of such inhibition and regulation was not accounted for in the current model.  
     [0177] There are about 1,800 genes in the  B. subtilis  genome for which no functional information is available. One of the approaches to assess gene function is a phenotypic analysis of mutants missing each one. Ogasawara constructed 789 such mutants for this purpose and observed phenotypic changes in 328 mutants under various conditions (Ogasawara,  Res. Microbiol.  151: 129-134 (2000)). Ogasawara identified several novel essential genes that were not identified in previous genetic studies. Three of these genes were predicted to be essential by the in silico model including yybQ (inorganic pyrophosphatase), ispA or yqiD (farnesyl-diophosphate synthase) and dxs or yqiD (1-deoxyxylulose-5-phosphate synthase).  
     [0178] Thus, this example demonstrates that the in silico model can be used to uncover essential genes to augment or circumvent traditional genetic studies.  
     EXAMPLE VI  
     [0179] This example demonstrates simulation of  B. subtilis  growth using a combined regulatory/metabolic model. This example further demonstrates the effects on growth rate prediction when regulation is represented in a  B. subtilis  metabolic model.  
     [0180] Glucose repression is a phenomenon of catabolite repression mediated by CcpA (catabolite control protein) in  B. subtilis  (see, for example, Grundy et al.,  J. Bacteriol.  175:7348-7355 (1993)). CcpA acts both as a negative regulator of carbohydrate (including, for example, arabinose and ribose) utilization genes and as a positive regulator of genes involved in excretion of excess carbon.  
     [0181] Growth of  B. subtilis  in the presence of glucose and arabinose was simulated as follows. The  B. subtilis  model described in Example I was modified to incorporate the logic statement:  
     [0182] Reg−ARAA=IF (Glucose exchange reaction) then NOT(ARAA).  
     [0183] The constraint for the ARAA reaction was:  
     (0)*Reg−ARAA≦R2≦(∞)*Reg−ARAA.  
     [0184] According to the logic statement if glucose is present the gene for L-arabinose isomerase is not expressed and the flux via reaction ARAA (L-arabinose isomerase, EC 5.3.1.4) is constrained to zero. When glucose is not present, for example, when it is consumed from the media, the flux via reaction ARAA has an infinite boundary value.  
     [0185] A simulation was run for the combined regulatory/metabolic model and the stand-alone metabolic model described in Example I. The following parameters were used: PO=1; M=9.5 mmol ATPs/g DCW/hr; glucose uptake rate=5 mmol/g DCW/hr; arabinose uptake rate=5 mmol/g DCW/hr; the flux via ribulose 5-phosphate isomerase reaction (converting ribulose 5-phosphate to xylose 5-phosphate) was constrained to be greater than zero; the uptake rates for oxygen, nitrogen, sulfate and phosphate were unconstrained; and the biomass composition was as set forth in Tables 3 and 4 for a growth rate at 0.11 hr −1 .  
     [0186]FIG. 9 shows the differences in network utilization between the regulated model (top numbers) and stand-alone model (bottom numbers). Absent consideration of repression mediated by CcpA, both glucose and arabinose were taken up and utilized in the simulation. However when regulation due to CcpA was included in the model, arabinose was not utilized due to the import and utilization of glucose. Comparison of the results for the non-regulated model with those for the regulated model indicated that regulation by CcpA resulted in lower cell growth rate. The predicted growth rate was 0.818 hr −1  for the stand-alone metabolic model, and for the combined regulatory/metabolic model was 0.420 hr −1 .  
     [0187] Incorporation of the regulatory controls into the metabolic model can result in more accurate representations of the true physiology of the organism. Using the methods described in this example, molecular level regulatory knowledge as well as information about causal relationships, for example, where molecular detail is not known, can be incorporated into a  B. subtilis  model. As an example, in vivo studies of gene expression have identified 66 genes which are repressed by glucose but induced when glucose levels decrease (Yoshida et al.,  Nucl. Acids Res.  29:683-692 (2001)). Incorporation of regulation at each gene in response to glucose levels using boolean logic statements such as that demonstrated above for the ARAA reaction, can be used to increase the predictive capacity of a  B. subtilis  model.  
     [0188] 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.  
     [0189] 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 limited only by the claims.  
                                   TABLE 8                                           Rxn                           Dele-       Gene Description   Gene   Reaction   Rxn Name   E. C. #   tion                  EMP Pathway                           Enolase   eno   2PG &lt;-&gt; PEP   ENO   4.2.1.11   R       Fructose-1,6-bisphosphatase   fbp   FDP -&gt; F6P + PI   FBP   3.1.3.11       Fructose-1,6-bisphosphatate aldolase   fbaA   FDP &lt;-&gt; T3P1 + T3P2   FBA   4.1.2.13   R       Fructose-1,6-bisphosphatate aldolase   fbaB   FDP &lt;-&gt; T3P1 + T3P2   FBA2   4.1.2.13       Glucokinase   glcK   GLC + ATP -&gt; G6P + ADP   GLK2   2.7.1.2       Glucose-1-phosphate   glgC   ATP + G1P -&gt; ADPGLC + PPI   GLGC   2.7.7.27       adenylytransferase       Glyceraldehyde-3-phosphate   gapA   T3P1 + PI + NAD &lt;-&gt; NADH + 13DPG   GAPA   1.2.1.12   E       dehydrogenase-A complex       Glyceraldehyde-3-phosphate   gapB   T3P1 + PI + NAD &lt;-&gt; NADH + 13DPG   GAPC   1.2.1.12       dehydrogenase-C complex       Glycogen phosphorylase   glgP   GLYCOGEN + PI -&gt; G1P   GLGP   2.4.1.1       Glycogen synthase   glgA   ADPGLC -&gt; ADP + GLYCOGEN   GLGA   2.4.1.21       Methylglyoxal synthase   mgsA   T3P2 -&gt; MTHGXL + PI   MGSA   4.2.99.11       Phosphoenolpyruvate synthase   pps   PYR + ATP -&gt; PEP + AMP + PI   PPSA   2.7.9.2       Phosphofructokinase   pfkA   F6P + ATP -&gt; FDP + ADP   PFKA   2.7.1.11   R       Phosphoglucose isomerase   pgi1   G6P &lt;-&gt; F6P   PGI1   5.3.1.9   R       Phosphoglucose isomerase   pgi2   bDG6P &lt;-&gt; G6P   PGI2   5.3.1.9       Phosphoglucose isomerase   pgi3   bDG6P &lt;-&gt; F6P   PGI3   5.3.1.9       Phosphoglycerate kinase   pgk   13DPG + ADP &lt;-&gt; 3PG + ATP   PGK   2.7.2.3   E       Phosphoglycerate mutase 1   pgm   3PG &lt;-&gt; 2PG   GPMA   5.4.2.1   R       Phosphoglycerate mutase 2   yhfR —     3PG &lt;-&gt; 2PG   GPMB   5.4.2.1       Pyruvate dehydrogenase   pdhA   PYR + COA + NAD -&gt; NADH +   ACEE   1.2.4.1,   E               CO2 + ACCOA       2.3.1.12,                       1.8.1.4       Pyruvate Kinase I   pyk   PEP + ADP -&gt; PYR + ATP   PYKF   2.7.1.40   R       Triosphosphate Isomerase   tpiA   T3P1 &lt;-&gt; T3P2   TPIA   5.3.1.1   R       Pentose Phosphate Pathway       2-Keto-3-deoxy-6-phosphogluconate   kdgA   2KD6PG -&gt; T3P1 + PYR   EDA   4.1.2.14       aldolase       6-Phosphogluconate dehydrogenase   gntZ   D6PGC + NADP -&gt; NADPH +   GND   1.1.1.44       (decarboxylating)       CO2 + RL5P       6-Phosphogluconate dehydrogenase   yqjI   D6PGC + NADP -&gt; NADPH +   GND2   1.1.1.44       (decarboxylating)       CO2 + RL5P       6-Phosphogluconolactonase   PGL   D6PGL -&gt; D6PGC   PGL   3.1.1.31       Glucose 6-phosphate-1-dehydrogenase   zwf   G6P + NADP &lt;-&gt; D6PGL + NADPH   ZWF   1.1.1.49       Ribose-5-phosphate isomerase A   ywlF   RL5P &lt;-&gt; R5P   RPIA   5.3.1.6   E       Ribulose phosphate 3-epimerase   rpe   RL5P &lt;-&gt; X5P   RPE   5.1.3.1       Transaldolase A   ywjH   T3P1 + S7P &lt;-&gt; E4P + F6P   TALA   2.2.1.2       Transketolase II   tkt1   R5P + X5P &lt;-&gt; T3P1 + S7P   TKTB1   2.2.1.1       Transketolase II   tkt2   X5P + E4P &lt;-&gt; F6P + T3P1   TKTB2   2.2.1.1       The Tricarboxylic Acid Cycle       2-Ketoglutarate dehyrogenase   odhA   AKG + NAD + COA -&gt; CO2 +   SUCA   1.2.4.2, 2.3.1.   R               NADH + SUCCOA       61, 1.8.1.4       Aconitase A   citB   CIT &lt;-&gt; ICIT   ACNA   4.2.1.3   E       Citrate synthase   citA   ACCOA + OA -&gt; COA + CIT   GLTA   4.1.3.7   E       Citrate synthase   citZ   ACCOA + OA -&gt; COA + CIT   GLTA2   4.1.3.7       Citrate synthase   mmgD   ACCOA + OA -&gt; COA + CIT   GLTA3   4.1.3.7       Fumarase A   citG   FUM &lt;-&gt; MAL   FUMA   4.2.1.2   E       Fumurate reductase   sdhA3   FUM + FADH -&gt; SUCC + FAD   FRDA   1.3.99.1       Isocitrate dehydrogenase   icd   ICIT + NADP &lt;-&gt; CO2 +   ICDA   1.1.1.42   E               NADPH + AKG       Malate dehydrogenase   malS   MAL + NAD &lt;-&gt; NADH + OA   MDH   1.1.1.37   E       Malate dehydrogenase   mdh   MAL + NAD &lt;-&gt; NADH + OA   MDH2   1.1.1.37       Succinate dehydrogenase (Combine w   sdhA1   SUCC + FAD -&gt; FADH + FUM   SDHA1   1.3.99.1       FRDA)       Succinyl-CoA synthetase   sucD   SUCCOA + ADP + PI &lt;-&gt; ATP +   SUCC   6.2.1.5   R               COA + SUCC       Pyruvate Metabolism       Acetaldehyde dehydrogenase   aldX4   ACCOA + 2 NADH &lt;-&gt; ETH +   ADHE2   1.2.1.10               2 NAD + COA       Acetaldehyde dehydrogenase   aldY4   ACCOA + 2 NADH &lt;-&gt; ETH +   ADHE3               2 NAD + COA       Acetate kinase A   ackA   ACTP + ADP &lt;-&gt; ATP + AC   ACKA   2.7.2.1       Acetyl-CoA synthetase   acsA   ATP + AC + COA -&gt; AMP +   ACS   6.2.1.1               PPI + ACCOA       Acetyl-CoA synthetase   ytcl   ATP + AC + COA -&gt; AMP +   ACS2   6.2.1.1               PPI + ACCOA       Formate hydrogen lyase   fdhD   FOR -&gt; CO2   FDHF1   1.2.1.2,   E                       1.12.1.2       L-Lactate dehydrogenase   ldh   PYR + NADH &lt;-&gt; NAD + LLAC   LDH   1.1.1.27       Phosphotransacetylase   pta   ACCOA + PI &lt;-&gt; ACTP + COA   PTA   2.3.1.8       Anaplerotic Reactions       Inorganic pyrophosphatase   ppaC   PPI -&gt; 2 PI   PPA   3.6.1.1   E       Malic enzyme (NAD)   mleA1   MAL + NAD -&gt; CO2 + NADH + PYR   SFCA   1.1.1.38       Malic enzyme (NADP)   mleA2   MAL + NADP -&gt; CO2 + NADPH + PYR   MAEB   1.1.1.40       Phosphoenolpyruvate carboxykinase   pckA   OA + ATP -&gt; PEP + CO2 + ADP   PCKA   4.1.1.49       Pyruvate carboxylase   pycA   PYR + ATP + CO2 -&gt; OA + PI + ADP   PC   6.4.1.1   E       Respiration (Note: the P/O ratio is set to 1.5 as an       example)       Cytochrome oxidase bd   ctaD   QH2 + .5 O2 -&gt; Q + 2 HEXT   CYDA   1.10.2.2,   R                       1.9.3.1       Cytochrome oxidase bo3   ctaC   QH2 + .5 O2 -&gt; Q + 2.5 HEXT   CYOA   1.10.2.2,                       1.9.3.1       F0F1-ATPase   atpA   ATP &lt;-&gt; ADP + PI + 4 HEXT   ATPA   3.6.1.34   R       Glycerol-3-phosphate dehydrogenase   glpD   GL3P + Q -&gt; T3P2 + QH2   GLPD   1.1.99.5       (aerobic)       NADH dehydrogenase I   ndhF1   NADH + Q -&gt; NAD + QH2 + 3.5 HEXT   NUOA   1.6.5.3   R       NADH dehydrogenase II   ndhF2   NADH + Q -&gt; NAD + QH2   NDH   1.6.5.3       Succinate dehydrogenase complex   sdhA2   FADH + Q &lt;-&gt; FAD + QH2   SDHA2   1.3.5.1   R       Thioredoxin reductase   trxB   OTHIO + NADPH -&gt; NADP + RTHIO   TRXB   1.6.4.5   E       Alternative Carbon Source       Melibiose       Alpha-galactosidase (melibiase)   melA   MELI -&gt; GLC + GLAC   MELA   3.2.1.22       Galactose       Galactokinase   galK   GLAC + ATP -&gt; GAL1P + ADP   GALK   2.7.1.6       Galactose-1-phosphate   galT   GAL1P + UDPG &lt;-&gt; G1P + UDPGAL   GALT   2.7.7.10       uridylyltransferase       UDP-glucose 4-epimerase   galE   UDPGAL &lt;-&gt; UDPG   GALE   5.1.3.2       UDP-glucose 4-epimerase   yjaV   UDPGAL &lt;-&gt; UDPG   GALE2   5.1.3.2       UDP-glucose-1-phosphate   gtaB   G1P + UTP &lt;-&gt; UDPG + PPI   GALU1   2.7.7.9   E       uridylyltransferase       UDP-glucose-1-phosphate   ytdA   G1P + UTP &lt;-&gt; UDPG + PPI   GALU1a   2.7.7.9       uridylyltransferase       UDP-glucose-1-phosphate   yngB   G1P + UTP &lt;-&gt; UDPG + PPI   GALU1b   2.7.7.9       uridylyltransferase       Phosphoglucomutase   ybbT   G1P &lt;-&gt; G6P   PGM   5.4.2.2   E       Lactose       Periplasmic beta-glucosidase precursor   bglH   LCTS -&gt; GLC + GLAC   BGLX   3.2.1.21       phospho-beta-glucosidase   yesZ   LCTS -&gt; GLC + GLAC   BGLX2   3.2.1.21       phospho-beta-glucosidase   ydhP   LCTS -&gt; GLC + GLAC   BGLX3   3.2.1.21       Beta-galactosidase (LACTase)   yckE   LCTS -&gt; GLC + GLAC   LACZ   3.2.1.23       Beta-galactosidase (LACTase)   lacA   LCTS -&gt; GLC + GLAC   LACZ2   3.2.1.23       Trehalose       trehalose-6-phosphate hydrolase   treA   TRE6P -&gt; bDG6P + GLC   TREC   3.2.1.93       Fructose       1-Phosphofructokinase (Fructose 1-   fruK   F1P + ATP -&gt; FDP + ADP   FRUK1   2.7.1.56       phosphate kinase)       Xylose isomerase   xylA2   FRU -&gt; GLC   XYLA1   5.3.1.5       Mannose       Phosphomannomutase   yhxB   MAN6P &lt;-&gt; MAN1P   CPSG   5.4.2.8       Mannose-6-phosphate isomerase   manA   MAN6P &lt;-&gt; F6P   MANA   5.3.1.8       Mannose-6-phosphate isomerase   pmi   MAN6P &lt;-&gt; F6P   MANA2   5.3.1.8       Mannose-6-phosphate isomerase   ydhS   MAN6P &lt;-&gt; F6P   MANA3   5.3.1.8       N-Acetylglucosamine       N-Acetylglucosamine-6-phosphate   nagA   NAGP -&gt; GA6P + AC   NAGA   3.5.1.25       deacetylase       Glucosamine       Glucosamine-6-phosphate deaminase   gamA   GA6P -&gt; F6P + NH3   NAGB   5.3.1.10       Fucose       Aldehyde dehydrogenase A   aldX3   LACAL + NAD &lt;-&gt; LLAC + NADH   ALDA   1.2.1.22       Aldehyde dehydrogenase B   aldY3   LACAL + NAD &lt;-&gt; LLAC + NADH   ALDB   1.2.1.22       Aldehyde dehydrogenase   ycbD   LACAL + NAD &lt;-&gt; LLAC + NADH   ADHC1   1.1.1.1   E       Aldehyde dehydrogenase   aldX2   GLAL + NADH &lt;-&gt; GL + NAD   ADHC2   1.1.1.1   E       Aldehyde dehydrogenase   aldY2   GLAL + NADH &lt;-&gt; GL + NAD   ADHC3   1.1.1.1       Aldehyde dehydrogenase   aldX1   ACAL + NAD -&gt; AC + NADH   ALDH2   1.2.1.3       Aldehyde dehydrogenase   aldY1   ACAL + NAD -&gt; AC + NADH   ADHC4   1.2.1.3   E       Aldehyde dehydrogenase   dhaS   ACAL + NAD -&gt; AC + NADH   ADHC5   1.2.1.3       Aldehyde dehydrogenase   ywdH   ACAL + NAD -&gt; AC + NADH   ADHC6   1.2.1.3       Gluconate       Gluconokinase I   gntK   GLCN + ATP -&gt; D6PGC + ADP   GNTV   2.7.1.12       Rhamnose       L-Rhamnose isomerase   yulE   RMN &lt;-&gt; RML   RHAA   5.3.1.14       Rhamnulokinase   yulC   RML + ATP -&gt; RML1P + ADP   RHAB   2.7.1.5       Arabinose       L-Arabinose isomerase   araA   ARAB &lt;-&gt; RBL   ARAA   5.3.1.4       L-Ribulokinase   araB1   RBL + ATP -&gt; LRL5P + ADP   ARAB   2.7.1.16       L-Ribulokinase   araB2   RBL + ATP -&gt; RL5P + ADP   ARAB   2.7.1.16       L-Ribulose-phosphate 4-epimerase   araD   LRL5P &lt;-&gt; X5P   ARAD   5.1.3.4       Xylose       Xylose isomerase   xylA   XYL &lt;-&gt; XUL   XYLA2   5.3.1.5       Xylulokinase   xylB   XUL + ATP -&gt; X5P + ADP   XYLB   2.7.1.17       Xylulokinase   yoaC   XUL + ATP -&gt; X5P + ADP   XYLB2       Ribose       Ribokinase   rbsK   RIB + ATP -&gt; R5P + ADP   RBSK   2.7.1.15       Ribokinase   yurL   RIB + ATP -&gt; R5P + ADP   RBSK2   2.7.1.15       Mannitol       Mannitol-1-phosphates 5-   mtlD   MNT6P + NAD &lt;-&gt; F6P + NADH   MTLD   1.1.1.17       dehydrogenase       Glycerol       Glycerol kinase   glpK   GL + ATP -&gt; GL3P + ADP   GLPK   2.7.1.30   E       Glycerol-3-phosphate-dehydrogenase-   gpsA   GL3P + NADP &lt;-&gt; T3P2 + NADPH   GPSA1   1.1.1.94   E       [NAD(P)+]       Nucleosides and Deoxynucleosides       Phosphopentomutase   drm1   DR1P &lt;-&gt; DR5P   DEOB1   5.4.2.7       Phosphopentomutase   drm2   R1P &lt;-&gt; R5P   DEOB2   5.4.2.7       Deoxyribose-phosphate aldolase   dra   DR5P -&gt; ACAL + T3P1   DEOC   4.1.2.4       Aspartate &amp; Asparagine Biosynthesis       Asparagine synthetase (Glutamate   asnB   ASP + ATP + GLN -&gt; GLU + ASN +   ASNB1   6.3.5.4   E       dependent)       AMP + PPI       Asparagine synthetase (Glutamate   asnH   ASP + ATP + GLN -&gt; GLU + ASN +   ASNB1b   6.3.5.4       dependent)       AMP + PPI       Asparagine synthetase (Glutamate   asnO   ASP + ATP + GLN -&gt; GLU + ASN +   ASNB1c   6.3.5.4       dependent)       AMP + PPI       Asparate transaminase   aspB   OA + GLU &lt;-&gt; ASP + AKG   ASPC1   2.6.1.1   E       Asparate transaminase   yhdR —     OA + GLU &lt;-&gt; ASP + AKG   ASPC2   2.6.1.1       Asparate transaminase   ykrV   OA + GLU &lt;-&gt; ASP + AKG   ASPC3   2.6.1.1       Asparate transaminase   yurG   OA + GLU &lt;-&gt; ASP + AKG   ASPC4   2.6.1.1       Glutamate and Glutamine Biosynthesis       Glutamate dehydrogenase   gudB   AKG + NH3 + NADPH &lt;-&gt;   GDHA   1.4.1.4   R               GLU + NADP       Glutamate dehydrogenase II   rocG   AKG + NH3 + NADH &lt;-&gt; GLU + NAD   GDHA2       R       Glutamate synthase   yerD   AKG + GLN + NADPH -&gt;   GLTB   1.4.1.13               NADP + 2 GLU       Glutamate synthase: NADH specific   gltA   AKG + GLN + NADH -&gt; NAD + 2 GLU   GLTB2   1.4.1.13       Glutamate-ammonia ligase   glnA   GLU + NH3 + ATP -&gt; GLN + ADP + PI   GLNA   6.3.1.2   E       Alanine Biosynthesis       Alanine racemase, biosynthetic   alr —     ALA &lt;-&gt; DALA   ALR —     5.1.1.1       Alanine racemase, catabolic   yncD   ALA -&gt; DALA   DADX   5.1.1.1       Alanine transaminase   alaT   PYR + GLU &lt;-&gt; AKG + ALA   ALAB   2.6.1.2   E       Arginine, Putriscine, and Spermidine Biosynthesis       5-Methylthioribose kinase   MTHRKN   5MTR + ATP -&gt; 5MTRP + ADP   MTHRKN   2.7.1.100   E       5-Methylthioribose-1-phosphate   MTHIPIS   5MTRP &lt;-&gt; 5MTR1P   MTHIPIS   5.3.1.23   E       isomerase       Acetylornithine deacetylase   ylmB1   NAARON -&gt; AC + ORN   ARGE1   3.5.1.16       Acetylornithine transaminase   argD   NAGLUSAL + GLU &lt;-&gt;   ARGD   2.6.1.11   E               AKG + NAARON       Adenosylmethionine decarboxylase   speD   SAM &lt;-&gt; DSAM + CO2   SPED   4.1.1.50   E       Agmatinase   speB   AGM -&gt; UREA + PTRC   SPEB   3.5.3.11   E       Arginine decarboxylase, biosynthetic   speA   ARG -&gt; CO2 + AGM   SPEA   4.1.1.19   E       Argininosuccinate lyase   argH   ARGSUCC &lt;-&gt; FUM + ARG   ARGH   4.3.2.1   E       Argininosuccinate synthase   argG   CITR + ASP + ATP -&gt; AMP +   ARGG   6.3.4.5   E               PPI + ARGSUCC       Carbamoyl phosphate synthetase   carA   GLN + 2 ATP + CO2 -&gt; GLU + CAP +   CARA   6.3.5.5   E               2 ADP + PI       E-1 (Enolase-phosphatase)   NE1PH   5MTR1P -&gt; DKMPP   NE1PH       E       E-3 (Unknown)   NE3UNK   DKMPP -&gt; FOR + KMB   NE3UNK       E       Methylthioadenosine nucleosidase   mtn   5MTA -&gt; AD + 5MTR   MTHAKN   3.2.2.16   E       N-Acetylglutamate kinase   argB   NAGLU + ATP -&gt; ADP + NAGLUYP   ARGB   2.7.2.8   E       N-Acetylglutamate phosphate   argC   NAGLUYP + NADPH &lt;-&gt;   ARGC   1.2.1.38   E       reductase       NADP + PI + NAGLUSAL       N-Acetylglutamate synthase   ARGA   GLU + ACCOA -&gt; COA + NAGLU   ARGA   2.3.1.1       Ornithine carbamoyl transferase 1   argF   ORN + CAP &lt;-&gt; CITR + PI   ARGF   2.1.3.3   E       Ornithine transaminase   rocD   ORN + AKG -&gt; GLUGSAL + GLU   YGJG   2.6.1.13       Spermidine synthase   speE   PTRC + DSAM -&gt; SPMD + 5MTA   SPEE   2.5.1.16   E       Transamination (Unknown)   TNSUNK   KMB + GLN -&gt; GLU + MET   TNSUNK       E       Urease   ureA   UREA -&gt; CO2 + 2 NH3   UREA   3.5.1.5   E       Proline biosynthesis       γ-Glutamyl kinase   proB   GLU + ATP -&gt; ADP + GLUP   PROB   2.7.2.11       γ-Glutamyl kinase   proJ   GLU + ATP -&gt; ADP + GLUP   PROB2   2.7.2.11       Glutamate-5-semialdehyde   proA   GLUP + NADPH -&gt; NADP +   PROA   1.2.1.41       dehydrogenase       PI + GLUGSAL       Pyrroline-5-carboxylate reductase   proG   GLUGSAL + NADPH -&gt; PRO + NADP   PROC   1.5.1.2   E       Pyrroline-5-carboxylate reductase   proH   GLUGSAL + NADPH -&gt; PRO + NADP   PROC2   1.5.1.2       Pyrroline-5-carboxylate reductase   proI   GLUGSAL + NADPH -&gt; PRO + NADP   PROC3   1.5.1.2       Branched Chain Amino Acid       Biosynthesis       3-Isopropylmalate dehydrogenase   leuB   IPPMAL + NAD -&gt; NADH +   LEUB   1.1.1.85   E               OICAP + CO2       3-Isopropylmalate dehydrogenase   ycsA   IPPMAL + NAD -&gt; NADH +   LEUB2   1.1.1.85               OICAP + CO2       Acetohydroxy Acid isomeroreductase   ilvC1   ABUT + NADPH -&gt; NADP + DHMVA   ILVC1   1.1.1.86   E       Acetohydroxy acid isomeroreductase   ilvC2   ACLAC + NADPH -&gt; NADP + DHVAL   ILVC2   1.1.1.86   E       Acetohydroxybutanoate synthase I   alsS2   OBUT + PYR -&gt; ABUT + CO2   ILVB1   4.1.3.18   E       Acetohydroxybutanoate synthase II   ilvB2   OBUT + PYR -&gt; ABUT + CO2   ILVG1   4.1.3.18       Acetolactate synthase I   alsS1   2 PYR -&gt; CO2 + ACLAC   ILVB2   4.1.3.18   E       Acetolactate synthase II   ilvB1   2 PYR -&gt; CO2 + ACLAC   ILVG2   4.1.3.18       Branched chain amino acid   ywaA1   OMVAL + GLU &lt;-&gt; AKG + ILE   ILVE1   4.6.1.42   E       aminotransferase       Branched chain amino acid   ybgE1   OMVAL + GLU &lt;-&gt; AKG + ILE   ILVE1b       aminotransferase       Branched chain amino acid   ywaA2   OIVAL + GLU &lt;-&gt; AKG + VAL   ILVE2   2.6.1.42   E       aminotransferase       Branched chain amino acid   ybgE2   OIVAL + GLU &lt;-&gt; AKG + VAL   ILVE2b       aminotransferase       Branched chain amino acid   ywaA3   OICAP + GLU &lt;-&gt; AKG + LEU   ILVE4   2.6.1.42   E       aminotransferase       Branched chain amino acid   ybgE3   OICAP + GLU &lt;-&gt; AKG + LEU   ILVE4b       aminotransferase       Dihydroxy acid dehydratase   ilvD1   DHMVA -&gt; OMVAL   ILVD1   4.2.1.9   E       Dihydroxy acid dehydratase   ilvD2   DHVAL -&gt; OIVAL   ILVD2   4.2.1.9   E       Isopropylmalate isomerase   leuC   CBHCAP &lt;-&gt; IPPMAL   LEUC   4.2.1.33   E       Isopropylmalate synthase   leuA   ACCOA + OIVAL -&gt; COA + CBHCAP   LEUA   4.1.3.12   E       Threonine dehydratase, biosynthetic   ilvA   THR -&gt; NH3 + OBUT   ILVA   4.2.1.16   E       Aromatic Amino Acids       2-Dehydro-3-deoxyphosphoheptonate   aroA1   E4P + PEP -&gt; PI + 3DDAH7P   AROF   4.1.2.15   E       aldolase F       3-Dehydroquinate dehydratase   aroC   DQT &lt;-&gt; DHSK   AROD   4.2.1.10   E       3-Dehydroquinate dehydratase   yqhS   DQT &lt;-&gt; DHSK   AROD2   4.2.1.10       3-Dehydroquinate synthase   aroB   3DDAH7P -&gt; DQT + PI   AROB   4.6.1.3   E       3-Phosphoshikimate-1-   aroE   SME5P + PEP &lt;-&gt; 3PSME + PI   AROA   2.5.1.19   E       carboxyvinyltransferase       Anthranilate synthase   trpE   CHOR + GLN -&gt; GLU + PYR + AN   TRPDE   4.1.3.27       Anthranilate synthase component II   trpD   AN + PRPP -&gt; PPI + NPRAN   TRPD   2.4.2.18   E       Aromatic amino acid transaminase   hisC2   PHPYR + GLU &lt;-&gt; AKG + PHE   TYRB1   2.6.1.57   E       Aromatic amino acid transaminase   hisC3   HPHPYR + GLU &lt;-&gt; AKG + TYR   TYRB2   2.6.1.5   E       Chorismate mutase 1   aroA2   CHOR -&gt; PHEN   PHEA1   5.4.99.5   E       Chorismate mutase 2   aroH   CHOR -&gt; PHEN   TYRA1   5.4.99.5       Chorismate mutase 2   pheB   CHOR -&gt; PHEN   TYRA1b   5.4.99.5       Chorismate synthase   aroF   3PSME -&gt; PI + CHOR   AROC   4.6.1.4   E       Indoleglycerol phosphate synthase   trpC   CPAD5P -&gt; CO2 + IGP   TRPC2   4.1.1.48   E       Phosphoribosyl anthranilate isomerase   trpF   NPRAN -&gt; CPAD5P   TRPC1   5.3.1.24   E       Phosphoribosyl anthranilate isomerase   ynal   NPRAN -&gt; CPAD5P   TRPC1b   5.3.1.24       Prephanate dehydrogenase   tyrA   PHEN + NAD -&gt; HPHPYR +   TYRA2   1.3.1.12   E               CO2 + NADH       Prephenate dehydratase   pheA   PHEN -&gt; CO2 + PHPYR   PHEA2   4.2.1.51   E       Shikimate dehydrogenase   aroD   DHSK + NADPH &lt;-&gt; SME + NADP   AROE   1.1.1.25   E       Shikimate kinase I   aroK   SME + ATP -&gt; ADP + SME5P   AROK   2.7.1.71   E       Tryptophan synthase   trpA   IGP + SER -&gt; T3P1 + TRP   TRPA   4.2.1.20   E       Histidine Biosynthesis       ATP phosphoribosyltransferase   hisG   PRPP + ATP -&gt; PPI + PRBATP   HISG   2.4.2.17   E       Histidinol dehydrogenase   hisD   HISOL + 3 NAD -&gt; HIS + 3 NADH   HISD   1.1.1.23   E       Histidinol phosphatase   hisJ   HISOLP -&gt; PI + HISOL   HISB2   3.1.3.15   E       Imidazoleglycerol phosphate   hisB   DIMGP -&gt; IMACP   HISB1   4.2.1.19   E       dehydratase       Imidazoleglycerol phosphate synthase   hisF   PRLP + GLN -&gt; GLU +   HISF   2.4.2.—   E               AICAR + DIMGP       L-Histidinol phosphate   hisC1   IMACP + GLU -&gt; AKG + HISOLP   HISC   2.6.1.9   E       aminotransferase       Phosphoribosyl pyrophosphate   prs   R5P + ATP &lt;-&gt;   PRSA   2.7.6.1   E       synthase       PRPP + AMP       Phosphoribosyl-AMP cyclohydrolase   hisI1   PRBAMP -&gt; PRFP   HISI2   3.5.4.19   E       Phosphoribosyl-ATP pyrophosphatase   hisI2   PRBATP -&gt; PPI + PRBAMP   HISI1   3.6.1.31   E       Phosphoribosylformimino-5-amino-1-   hisA   PRFP -&gt; PRLP   HISA   5.3.1.16   E       phosphoribosyl-4-imidazole       carboxamide isomerase       Serine &amp; Glycine Biosynthesis       3-Phosphoglycerate dehydrogenase   serA   3PG + NAD -&gt; NADH + PHP   SERA   1.1.1.95   E       3-Phosphoglycerate dehydrogenase   yoaD   3PG + NAD -&gt; NADH + PHP   SERA2   1.1.1.95       Glycine hydroxymethyltransferase   glyA3   THF + SER -&gt; GLY + METTHF   GLYA3   2.1.2.1       Phosphoserine phosphatase   SERB   3PSER -&gt; PI + SER   SERB   3.1.3.3   E       Phosphoserine transaminase   serC   PHP + GLU -&gt; AKG + 3PSER   SERC1   2.6.1.52   E       Cysteine Biosynthesis       3′-5′ Bisphosphate nucleotidase   BISPHDS   PAP -&gt; AMP + PI   BISPHDS   3.1.3.7   E       3′-Phospho-adenylylsulfate reductase   cysH   PAPS + RTHIO -&gt; OTHIO +   CYSH   1.8.99.—   E               H2SO3 + PAP       3′-Phospho-adenylylsulfate reductase   yitB   PAPS + RTHIO -&gt; OTHIO +   CYSH2   1.8.99.—               H2SO3 + PAP       Adenylylsulfate kinase   cysC   APS + ATP -&gt; ADP + PAPS   CYSC   2.7.1.25   E       Adenylylsulfate kinase   yisZ   APS + ATP -&gt; ADP + PAPS   CYSC2   2.7.1.25       O-Acetylserine (thiol)-lyase A   cysK   ASER + H2S -&gt; AC + CYS   CYSK   4.2.99.8   E       O-Acetylserine (thiol)-lyase B   ytkP   ASER + H2S -&gt; AC + CYS   CYSM   4.2.99.8       O-Acetylserine (thiol)-lyase B   yrhA   ASER + H2S -&gt; AC + CYS   CYSM2   4.2.99.8       Serine transacetylase   cysE   SER + ACCOA &lt;-&gt; COA + ASER   CYSE   2.3.1.30   E       Sulfate adenylyltransferase   sat   H2SO4 + ATP + GTP -&gt; PPI + APS +   CYSD   2.7.7.4   E               GDP + PI       Sulfate adenylyltransferase   yitA   H2SO4 + ATP + GTP -&gt; PPI + APS +   CYSD2   2.7.7.4               GDP + PI       Sulfite reductase   yvgQ   H2SO3 + 3 NADPH &lt;-&gt; H2S + 3 NADP   CYSI   1.8.1.2   E       Sulfite reductase   yvgR —     H2SO3 + 3 NADPH &lt;-&gt; H2S + 3 NADP   CYSI2   1.8.1.2       Threonine and Lysine Biosynthesis       Aspartate kinase I   dapG   ASP + ATP &lt;-&gt; ADP + BASP   THRA1   2.7.2.4   E       Aspartate kinase II   lysC   ASP + ATP &lt;-&gt; ADP + BASP   METL1   2.7.2.4       Aspartate kinase III   yclM   ASP + ATP &lt;-&gt; ADP + BASP   LYSC   2.7.2.4       Aspartate semialdehyde   asd   BASP + NADPH &lt;-&gt; NADP +   ASD   1.2.1.11   E       dehydrogenase       PI + ASPSA       Diaminopimelate decarboxylase   lysA   MDAP -&gt; CO2 + LYS   LYSA   4.1.1.20   E       Diaminopimelate epimerase   dapF   D26PIM &lt;-&gt; MDAP   DAPF   5.1.1.7   E       Dihydrodipicolinate reductase   dapB   D23PIC + NADPH -&gt; NADP + PIP26DX   DAPB   1.3.1.26   E       Dihydrodipicolinate synthase   dapA   ASPSA + PYR -&gt; D23PIC   DAPA   4.2.1.52   E       Homoserine dehydrogenase I   hom   ASPSA + NADPH &lt;-&gt; NADP + HSER   THRA2   1.1.1.3   E       Homoserine kinase   thrB   HSER + ATP -&gt; ADP + PHSER   THRB   2.7.1.39   E       Lysine decarboxylase 1   yaaO   LYS -&gt; CO2 + CADV   CADA   4.1.1.18       Succinyl diaminopimelate   DAPC   NS2A6O + GLU &lt;-&gt; AKG + NS26DP   DAPC   2.6.1.17   E       aminotransferase       Succinyl diaminopimelate   yodQ   NS26DP -&gt; SUCC + D26PIM   DAPE   3.5.1.18   E       desuccinylase       Tetrahydrodipicolinate succinylase   ykuQ   PIP26DX + SUCCOA -&gt;   DAPD   2.3.1.117   E               COA + NS2A6O       Threonine synthase   thrC   PHSER -&gt; PI + THR   THRC1   4.2.99.2   E       Methionine Biosynthesis       Adenosyl homocysteinase (Unknown)   Deduction   HCYS + ADN &lt;-&gt; SAH   ADCSASE   3.3.1.1   E       Cobalamin-dependent methionine   metE   HCYS + MTHF -&gt; MET +THF   METH   2.1.1.13   E       synthase       Cystathionine- -lyase   yjcJ   LLCT -&gt; HCYS + PYR + NH3   METC   4.4.1.8       Homoserine transsuccinylase   metA   HSER + SUCCOA -&gt; COA + OSLHSER   META   2.3.1.46   E       O-succinlyhomoserine lyase   yjcI1   OSLHSER + CYS -&gt; SUCC + LLCT   METB1a   4.2.99.9       O-succinlyhomoserine lyase   yrhB1   OSLHSER + CYS -&gt; SUCC + LLCT   METB1b   4.2.99.9       O-Succinlyhomoserine lyase   yjcI2   OSLHSER + H2S -&gt; SUCC + HCYS   METB3a       O-Succinlyhomoserine lyase   yrhB2   OSLHSER + H2S -&gt; SUCC + HCYS   METB3b       O-Succinlyhomoserine lyase   yjcI3   OSLHSER + CH3SH -&gt; SUCC + MET   METB4a       O-Succinlyhomoserine lyase   yrhB3   OSLHSER + CH3SH -&gt; SUCC + MET   METB4b       S-Adenosylmethionine synthetase   metK   MET + ATP -&gt; PPI + PI + SAM   METK   2.5.1.6   E       Amino Acid Degradation       Alanine       Alanine dehydrogenase   ald   ALA + NAD -&gt; PYR + NH3 + NADH   ALD   1.4.1.1       Arginine       Arginase   rocF   ARG -&gt; ORN + UREA   ROCF   3.5.3.1       Aminobutyrate aminotransaminase 1   gabT   GABA + AKG -&gt; SUCCSAL + GLU   GABT   2.6.1.19       Succinate semialdehyde   gabD   SUCCSAL + NAD -&gt; SUCC + NADH   SAD   1.2.1.16       dehydrogenase-NAD       Asparagine       Asparininase I   ansA   ASN -&gt; ASP + NH3   ASNA2   3.5.1.1       Asparininase II   yccC   ASN -&gt; ASP + NH3   ASNB2   3.5.1.1       Aspartate       Aspartate ammonia-lyase   ansB   ASP -&gt; FUM + NH3   ASPA   4.3.1.1       Glutamine       Glutaminase A   ybgJ   GLN -&gt; GLU + NH3   GLNASEA   3.5.1.2       Glutaminase B   ylaM   GLN -&gt; GLU + NH3   GLNASEB   3.5.1.2       Histidine       Formiminoglutamate hydrolase   hutG   NFGLU -&gt; GLU + FAM   HUTG   3.5.3.8       Histidase   hutH   HIS -&gt; URCAN + NH3   HUTH   4.3.1.3       Imidazolone-5-propionate hydrolase   hutI   4IMZP -&gt; NFGLU   HUTI   3.5.2.7       Urocanase   hutU   URCAN -&gt; 4IMZP   HUTU   4.2.1.49       Formamidase   FORAMD   FAM -&gt; NH3 + FOR   FORAMD       Isoleucine, Leucine, Valine       Leucine dehydrogenase   bcd   LEU + NAD -&gt; OICAP + NH3 + NADH   LEUDH       Acyl-CoA dehydrogenase   mmgC1   3MBCOA + NADP -&gt;   ACYLCOA1   1.3.99.3               3M2ECOA + NADPH       Acyl-CoA dehydrogenase   mmgC2   ISBCOA + NADP -&gt; MCCOA + NADPH   ACYLCOA2   1.3.99.3       Acyl-CoA dehydrogenase   mmgC3   2MBCOA + NADP -&gt;   ACYLCOA3   1.3.99.3               2MBECOA + NADPH       Methylcrotonoyl-CoA carboxylase   MCCOAC   3M2ECOA + ATP + CO2 -&gt; 3MGCOA +   MCCOAC   6.4.1.4               PI + ADP       Methylglutaconyl-CoA hydratase   MGCOAH   3MGCOA -&gt; 3HMGCOA   MGCOAH   4.2.1.18       Hydroxymethylglutaryl-CoA lyase   yngG   3HMGCOA -&gt; ACCOA + AAC   HMGCOAL   4.1.3.4       succinyl CoA:3-oxoacid CoA-   scoA   SUCCOA + AAC -&gt; SUCC + AACCOA   SUCCOAT       transferase       branched-chain alpha-keto acid   bkdAA1   OICAP + COA + NAD -&gt; 3MBCOA +   BKDAA1       E       dehydrogenase       CO2 + NADH       branched-chain alpha-keto acid   bkdAA2   OIVAL + COA + NAD -&gt; ISBCOA +   BKDAA2       E       dehydrogenase       CO2 + NADH       branched-chain alpha-keto acid   bkdAA3   OMVAL + COA + NAD -&gt; 2MBCOA +   BKDAA3       E       dehydrogenase       CO2 + NADH       3-hydroxbutyryl-CoA dehydratase   yngF1   MCCOA -&gt; 3HIBCOA   yngF1   4.2.1.17       3-hydroxbutyryl-CoA dehydratase   ysiB1   MCCOA -&gt; 3HIBCOA   ysiB1   4.2.1.17       3-hydroxbutyryl-CoA dehydratase   yngF2   2MBECOA -&gt; 3H2MBCOA   yngF2   4.2.1.17       3-hydroxbutyryl-CoA dehydratase   ysiB2   2MBECOA -&gt; 3H2MBCOA   ysiB2   4.2.1.17       Acyl-CoA hydrolase   ykhA   3HIBCOA -&gt; 3H2MP + COA   ykhA   3.1.2.4       Methylmalonyl-CoA epimerase   MMCOAEP   SMMCOA &lt;-&gt;   MMCOAEP   5.1.99.1       (5.1.99.1)       RMMCOA       Methylmalonyl-CoA mutase (5.4.99.2)   MMCOAMT   RMMCOA -&gt; SUCCOA   MMCOAMT   5.4.99.2       Proline       Pyrroline-5-carboxylate dehydrogenase   rocA   GLUGSAL + NAD -&gt; NADH + GLU   PUTA2   1.5.1.12       Proline oxidase   ycgM   PRO + NADP -&gt; GLUGSAL + NADPH   PROOX   1.5.1.2       Serine       D-Serine deaminase   dsdA   DSER -&gt; PYR + NH3   DSDA   4.2.1.14       Serine deaminase 1   sdaAA   SER -&gt; PYR + NH3   SDAA1   4.2.1.13       Threonine       Amino ketobutyrate ligase   kbl   2A3O + COA -&gt; ACCOA + GLY   KBL2   2.3.1.29       Threonine dehydrogenase   tdh   THR + NAD -&gt; 2A3O + NADH   TDH2   1.1.1.103       Purine Biosynthesis       5′-Phosphoribosyl-4-(N-   purB2   SAICAR &lt;-&gt; FUM + AICAR   PURB1   4.3.2.2   E       succinocarboxamide)-5-aminoimidazole       lyase       Adenylosuccinate lyase   purB1   ASUC &lt;-&gt; FUM + AMP   PURB2   4.3.2.2   E       Adenylosuccinate synthetase   purA   IMP + GTP + ASP -&gt; GDP + PI + ASUC   PURA   6.3.4.4   E       AICAR transformylase   purH1   AICAR + FTHF &lt;-&gt; THF + PRFICA   PURH1   2.1.2.3   E       Amidophosphoribosyl transferase   purF   PRPP + GLN -&gt; PPI + GLU + PRAM   PURF   2.4.2.14   E       GMP reductase   guaC   GMP + NADPH -&gt; NADP + IMP + NH3   GUAC   1.6.6.8       GMP synthase   guaA   XMP + ATP + GLN -&gt; GLU + AMP +   GUAA   6.3.4.1   E               PPI + GMP       IMP cyclohydrolase   purH2   PRFICA &lt;-&gt; IMP   PURH2   3.5.4.10   E       IMP dehydrogenase   guaB   IMP + NAD -&gt; NADH + XMP   GUAB   1.1.1.205   E       IMP dehydrogenase   yhcV   IMP + NAD -&gt; NADH + XMP   GUAB2   1.1.1.205       IMP dehydrogenase   ylbB   IMP + NAD -&gt; NADH + XMP   GUAB3   1.1.1.205       Phosphoribosylamine-glycine ligase   purD   PRAM + ATP + GLY &lt;-&gt;   PURD   6.3.4.13   E               ADP + PI + GAR       Phosphoribosylaminoimidazole   purE   AIR + CO2 + ATP &lt;-&gt;   PURK   4.1.1.21   E       carboxylase 1        NCAIR + ADP + PI       Phosphoribosylaminoimidazole   purK   NCAIR + CO2 &lt;-&gt; CAIR   PURE   4.1.1.21   E       carboxylase 2       Phosphoribosylaminoimidazole-   purC   CAIR + ATP + ASP &lt;-&gt;   PURC   6.3.2.6   E       succinocarboxamide synthetase       ADP + PI + SAICAR       Phosphoribosylformylglycinamide   purM   FGAM + ATP -&gt; ADP + PI + AIR   PURM   6.3.3.1   E       cyclo-ligase       Phosphoribosylformylglycinamide   purL   FGAR + ATP + GLN -&gt; GLU + ADP +   PURL   6.3.5.3   E       synthetase       PI + FGAM       Phosphoribosylformylglycinamide   purQ   FGAR + ATP + GLN -&gt; GLU + ADP +   PURL2   6.3.5.3       synthetase       PI + FGAM       Phosphoribosylglycinamide   purN   GAR + FTHF &lt;-&gt; THF + FGAR   PURN   2.1.2.2   E       formyltransferase       Phosphoribosylglycinamide   purT   GAR + FTHF &lt;-&gt;THF + FGAR   PURN2   2.1.2.2       formyltransferase       Pyrimidine Biosynthesis       Aspartate-carbamoyltransferase   pyrB   CAP + ASP -&gt; CAASP + PI   PYRB   2.1.3.2   E       CTP synthetase   pyrG1   UTP + GLN + ATP -&gt; GLU + CTP +   PYRG1   6.3.4.2               ADP + PI       CTP synthetase   pyrG2   UTP + NH3 + ATP -&gt; CTP + ADP + PI   PYRG2       Dihydroorotase   pyrC   CAASP &lt;-&gt; DOROA   PYRC   3.5.2.3   E       Dihydroorotate dehydrogenase   pyrD   DOROA + Q &lt;-&gt; QH2 + OROA   PYRD   1.3.3.1   E       OMP decarboxylase   pyrF   OMP -&gt; CO2 + UMP   PYRF   4.1.1.23   E       Orotate phosphoribosyl transferase   pyrE   OROA + PRPP &lt;-&gt; PPI + OMP   PYRE   2.4.2.10   E       Salvage Pathways       5′-Nucleotidase   yhcR1   DUMP -&gt; DU + PI   USHA1   3.1.3.5       5′-Nucleotidase   yhcR4   DTMP -&gt; DT + PI   USHA2   3.1.3.5       5′-Nucleotidase   yhcR5   DAMP -&gt; DA + PI   USHA3   3.1.3.5       5′-Nucleotidase   yhcR6   DGMP -&gt; DG + PI   USHA4   3.1.3.5       5′-Nucleotidase   yhcR7   DCMP -&gt; DC + PI   USHA5   3.1.3.5       5′-Nucleotidase   yhcR8   CMP -&gt; CYTD + PI   USHA6   3.1.3.5       5′-Nucleotidase   yhcR9   AMP -&gt; PI + ADN   USHA7   3.1.3.5       5′-Nucleotidase   yhcR10   GMP -&gt; PI + GSN   USHA8   3.1.3.5       5′-Nucleotidase   yhcR11   IMP -&gt; PI + INS   USHA9   3.1.3.5       5′-Nucleotidase   yhcR3   XMP -&gt; PI + XTSN   USHA12   3.1.3.5       5′-Nucleotidase   yhcR2   UMP -&gt; PI + URI   USHA11   3.1.3.5       Adenine deaminase   adeC   AD -&gt; NH3 + HYXN   YICP   3.5.4.2       Adenine deaminase   yerA   AD -&gt; NH3 + HYXN   YICP2   3.5.4.2       Adenine phosphoryltransferase   apt   AD + PRPP -&gt; PPI + AMP   APT   2.4.2.7       Adenosine kinase   adk1   ADN + ATP -&gt; AMP + ADP   ADKIN   2.7.1.20       Adenylate kinase   adk2   ATP + AMP &lt;-&gt; 2 ADP   ADK1   2.7.4.3       Adenylate kinase   adk3   GTP + AMP &lt;-&gt; ADP + GDP   ADK2   2.7.4.3       Adenylate kinase   adk4   ITP + AMP &lt;-&gt; ADP + IDP   ADK3   2.7.4.3       Adenylate kinase   adk5   DAMP + ATP &lt;-&gt; ADP + DADP   ADK4   2.7.4.11       Cytidine deaminase   cdd1   CYTD -&gt; URI + NH3   CDD1   3.5.4.5       Cytidine deaminase   cdd2   DC -&gt; NH3 + DU   CDD2   3.5.4.5       Cytidylate kinase   cmk1   DCMP + ATP &lt;-&gt; ADP + DCDP   CMKA1   2.7.4.14       Cytidylate kinase   cmk2   CMP + ATP &lt;-&gt; ADP + CDP   CMKA2   2.7.4.14       Cytidylate kinase   cmk3   UMP + ATP &lt;-&gt; ADP + UDP   CMKA3   2.7.4.14       Cytodine kinase   udk5   CYTD + GTP -&gt; GDP + CMP   UDK2   2.7.1.48       Deoxyguanylate kinase   gmk2   DGMP + ATP &lt;-&gt; DGDP + ADP   GMK2   2.7.4.8       dTMP kinase   tmk   DTMP + ATP &lt;-&gt; ADP + DTDP   TMK   2.7.4.9   E       dUTP pyrophosphatase   yncF   DUTP -&gt; PPI + DUMP   DUT   3.6.1.23       dUTP pyrophosphatase   yosS   DUTP -&gt; PPI + DUMP   DUT2   3.6.1.23       Guanylate kinase   gmk1   GMP + ATP &lt;-&gt; GDP + ADP   GMK1   2.7.4.8       Nucleoside triphosphatase   phoA2   GTP -&gt; GSN + 3 PI   MUTT1   3.6.1.—       Nucleoside triphosphatase   phoB2   GTP -&gt; GSN + 3 PI   MUTT1b   3.6.1.—       Nucleoside triphosphatase   phoA3   DGTP -&gt; DG + 3 PI   MUTT2   3.6.1.—       Nucleoside triphosphatase   phoB3   DGTP -&gt; DG + 3 PI   MUTT2b   3.6.1.—       Nucleoside-diphosphate kinase   ndk2   GDP + ATP &lt;-&gt; GTP + ADP   NDK1   2.7.4.6       Nucleoside-diphosphate kinase   ndk3   UDP + ATP &lt;-&gt; UTP + ADP   NDK2   2.7.4.6   E       Nucleoside-diphosphate kinase   ndk4   CDP + ATP &lt;-&gt; CTP + ADP   NDK3   2.7.4.6       Nucleoside-diphosphate kinase   ndk5   DGDP + ATP &lt;-&gt; DGTP + ADP   NDK5   2.7.4.6       Nucleoside-diphosphate kinase   ndk6   DUDP + ATP &lt;-&gt; DUTP + ADP   NDK6   2.7.4.6       Nucleoside-diphosphate kinase   ndk7   DCDP + ATP &lt;-&gt; DCTP + ADP   NDK7   2.7.4.6       Nucleoside-diphosphate kinase   ndk8   DADP + ATP &lt;-&gt; DATP + ADP   NDK9   2.7.4.6       Nucleoside-diphosphate kinase   ndk1   DTDP + ATP &lt;-&gt; DTTP + ADP   NDK0   2.7.4.6   E       Purine nucleotide phosphorylase   deoD1   DIN + PI &lt;-&gt; HYXN + DR1P   DEOD1   2.4.2.1       Purine nucleotide phosphorylase   punA1   DIN + PI &lt;-&gt; HYXN + DR1P   PUNA1   2.4.2.1       Purine nucleotide phosphorylase   deoD2   DA + PI &lt;-&gt; AD + DR1P   DEOD2   2.4.2.1       Purine nucleotide phosphorylase   punA2   DA + PI &lt;-&gt; AD + DR1P   PUNA2   2.4.2.1       Purine nucleotide phosphorylase   deoD3   DG + PI &lt;-&gt; GN + DR1P   DEOD3   2.4.2.1       Purine nucleotide phosphorylase   punA3   DG + PI &lt;-&gt; GN + DR1P   PUNA3   2.4.2.1       Purine nucleotide phosphorylase   deoD4   HYXN + R1P &lt;-&gt; INS + PI   DEOD4   2.4.2.1       Purine nucleotide phosphorylase   punA4   HYXN + R1P &lt;-&gt; INS + PI   PUNA4   2.4.2.1       Purine nucleotide phosphorylase   deoD5   AD + R1P &lt;-&gt; PI + ADN   DEOD5   2.4.2.1       Purine nucleotide phosphorylase   punA5   AD + R1P &lt;-&gt; PI + ADN   PUNA5   2.4.2.1       Purine nucleotide phosphorylase   deoD6   GN + R1P &lt;-&gt; PI + GSN   DEOD6   2.4.2.1       Purine nucleotide phosphorylase   punA6   GN + R1P &lt;-&gt; PI + GSN   PUNA6   2.4.2.1       Purine nucleotide phosphorylase   deoD7   XAN + R1P &lt;-&gt; PI + XTSN   DEOD7   2.4.2.1       Purine nucleotide phosphorylase   punA7   XAN + R1P &lt;-&gt; PI + XTSN   PUNA7   2.4.2.1       Purine nucleotide phosphorylase   deoD8   DU + PI &lt;-&gt; URA + DR1P   DEOD8   2.4.2.1       Purine nucleotide phosphorylase   punA8   DU + PI &lt;-&gt; URA + DR1P   PUNA8   2.4.2.1       Ribonucleoside-diphosphate reductase   nrdE1   ADP + RTHIO -&gt; DADP + OTHIO   NRDA1   1.17.4.1       Ribonucleoside-diphosphate reductase   nrdE2   GDP + RTHIO -&gt; DGDP + OTHIO   NRDA2   1.17.4.1       Ribonucleoside-diphosphate reductase   nrdE3   CDP + RTHIO -&gt; DCDP + OTHIO   NRDA3   1.17.4.1       Ribonucleoside-diphosphate reductase   nrdE4   UDP + RTHIO -&gt; DUDP + OTHIO   NRDA4   1.17.4.1       Ribonucleoside-triphosphate reductase   nrdE5   ATP + RTHIO -&gt; DATP + OTHIO   NRDD1   1.17.4.2       Ribonucleoside-triphosphate reductase   nrdE6   GTP + RTHIO -&gt; DGTP + OTHIO   NRDD2   1.17.4.2       Ribonucleoside-triphosphate reductase   nrdE7   CTP + RTHIO -&gt; DCTP + OTHIO   NRDD3   1.17.4.2       Ribonucleoside-triphosphate reductase   nrdE8   UTP + RTHIO -&gt; OTHIO + DUTP   NRDD4   1.17.4.2       Ribonucleoside-triphosphate reductase   yosNP1   ADP + RTHIO -&gt; DADP + OTHIO   yosNP1   1.17.4.2       Ribonucleoside-triphosphate reductase   yosNP2   GDP + RTHIO -&gt; DGDP + OTHIO   yosNP2   1.17.4.2       Ribonucleoside-triphosphate reductase   yosNP3   CDP + RTHIO -&gt; DCDP + OTHIO   yosNP3   1.17.4.2       Ribonucleoside-triphosphate reductase   yosNP4   UDP + RTHIO -&gt; DUDP + OTHIO   yosNP4   1.17.4.2       Ribonucleoside-triphosphate reductase   yosNP5   ATP + RTHIO -&gt; DATP + OTHIO   yosNP5   1.17.4.2       Ribonucleoside-triphosphate reductase   yosNP6   GTP + RTHIO -&gt; DGTP + OTHIO   yosNP6   1.17.4.2       Ribonucleoside-triphosphate reductase   yosNP7   CTP + RTHIO -&gt; DCTP + OTHIO   yosNP7   1.17.4.2       Ribonucleoside-triphosphate reductase   yosNP8   UTP + RTHIO -&gt; OTHIO + DUTP   yosNP8   1.17.4.2       Thymidilate synthetase   thyA   DUMP + METTHF -&gt; DHF + DTMP   THYA   2.1.1.45   E       Thymidilate synthetase   thyB   DUMP + METTHF -&gt; DHF + DTMP   THYA2   2.1.1.45       Thymidine (deoxyuridine) kinase   tdk1   DU + ATP -&gt; DUMP + ADP   TDK1   2.7.1.21       Thymidine (deoxyuridine) kinase   tdk2   DT + ATP -&gt; ADP + DTMP   TDK2   2.7.1.21       Thymidine (deoxyuridine)   deoD9   DT + PI &lt;-&gt; THY + DR1P   DEOA2   2.4.2.4       phosphorylase       Thymidine (deoxyuridine)   punA9   DT + PI &lt;-&gt; THY + DR1P   PUNA9   2.4.2.4       phosphorylase       Uracil phosphoribosyltransferase   upp   URA + PRPP -&gt; UMP + PPI   UPP   2.4.2.9       Uridine kinase   udk2   URI + GTP -&gt; GDP + UMP   UDK1   2.7.1.48       Uridylate kinase   pyrH1   UMP + ATP &lt;-&gt; UDP + ADP   PYRH1   2.1.4.—       Uridylate kinase   pyrH2   DUMP + ATP &lt;-&gt; DUDP + ADP   PYRH2   2.1.4.—       Xanthine-guanine   hrpT1   XAN + PRPP -&gt; XMP + PPI   GPT1   2.4.2.22       phosphoribosyltransferase       Xanthine-guanine   hrpT2   HYXN + PRPP -&gt; PPI + IMP   GPT2   2.4.2.22       phosphoribosyltransferase       Xanthine-guanine   hrpT3   GN + PRPP -&gt; PPI + GMP   GPT3   2.4.2.22       phosphoribosyltransferase       One Carbon Metabolism       Glycine cleavage system (Multi-   gcvPA   GLY + THF + NAD -&gt; METTHF +   GCV   1.4.4.2,       component system)       NADH + CO2 + NH3       2.1.2.10       Formyl tetrahydrofolate deformylase   ykkE   FTHF -&gt; FOR + THF   PURU   3.5.1.10   R       Methenyl tetrahydrofolate   folD2   METHF &lt;-&gt; FTHF   FOLD2   3.5.4.9   E       cyclehydrolase       Methylene tetrahydrofolate reductase   METF   METTHF + NADH -&gt; NAD + MTHF   METF   1.7.99.5   E       Methylene THF dehydrogenase   folD1   METTHF + NADP &lt;-&gt; METHF + NADPH   FOLD1   1.5.1.5   E       Membrane Lipid Biosynthesis       Acetyl-CoA carboxyltransferase   accA   ACCOA + ATP + CO2 &lt;-&gt; MALCOA +   ACCA   6.4.1.2,   E               ADP + PI       6.3.4.14       Acetyl-CoA-ACP transacylase   fabHAB0   ACACP + COA &lt;-&gt; ACCOA + ACP   FABH   2.3.1.41   E       Isovaleryl-CoA ACP transacylase   3MBACP   3MBACP + COA &lt;-&gt; 3MBCOA + ACP   3MBACP       E       2-Methylbutyryl-CoA ACP transacylase   2MBACP   2MBACP + COA &lt;-&gt; 2MBCOA + ACP   2MBACP       E       Isobutyryl-CoA ACP transacylase   ISBACP   ISBACP + COA &lt;-&gt; ISBCOA + ACP   ISBACP       E       Acyltransferase   PLS2   GL3P + 0.022 C140IACP + 0.046   PLS2       E               C140NACP + 0.386 C150IACP + 0.654               C150AACP + 0.00001 C161IACP + 0.094               C160IACP + 0.00001 C161NACP + 0.202               C160NACP + 0.00001 C171IACP +               0.00001 C171AACP + 0.154 C170IACP +               0.362 C170AACP + 0.074 C180NACP -&gt;               1.994 ACP + PA       β-Ketoacyl-ACP synthase III   fabHAB1   ISBACP + 5 MALACP + 10 NADPH -&gt; 10   fabHAB1       E               NADP + C140IACP + 5 CO2 + 5 ACP       β-Ketoacyl-ACP synthase III   fabHAB2   ACACP + 6 MALACP + 12 NADPH -&gt; 12   fabHAB2       E               NADP + C140NACP + 6 CO2 + 6 ACP       β-Ketoacyl-ACP synthase III   fabHAB3   3MBACP + 5 MALACP + 10 NADPH -&gt;   fabHAB3       E               10 NADP + C150IACP + 5 CO2 + 5 ACP       β-Ketoacyl-ACP synthase III   fabHAB4   2MBACP + 5 MALACP + 10 NADPH -&gt;   fabHAB4       E               10 NADP + C150AACP + 5 CO2 + 5 ACP       β-Ketoacyl-ACP synthase III   fabHAB5   ISBACP + 6 MALACP + 11 NADPH -&gt; 11   fabHAB5       E               NADP + C161IACP + 6 CO2 + 6 ACP       β-Ketoacyl-ACP synthase III   fabHAB6   ISBACP + 6 MALACP + 12 NADPH -&gt; 12   fabHAB6       E               NADP + C160IACP + 6 CO2 + 6 ACP       β-Ketoacyl-ACP synthase III   fabHAB7   ACACP + 7 MALACP + 13 NADPH -&gt; 13   fabHAB7       E               NADP + C161NACP + 7 CO2 + 7 ACP       β-Ketoacyl-ACP synthase III   fabHAB8   ACACP + 7 MALACP + 14 NADPH -&gt; 14   fabHAB8       E               NADP + C160NACP + 7 CO2 + 7 ACP       β-Ketoacyl-ACP synthase III   fabHAB9   3MBACP + 6 MALACP + 11 NADPH -&gt;   fabHAB9       E               11 NADP + C171IACP + 6 CO2 + 6 ACP       β-Ketoacyl-ACP synthase III   fabHAB10   2MBACP + 6 MALACP + 11 NADPH -&gt;   fabHAB10       E               11 NADP + C171AACP + 6 CO2 + 6 ACP       β-Ketoacyl-ACP synthase III   fabHAB11   3MBACP + 6 MALACP + 12 NADPH -&gt;   fabHAB11       E               12 NADP + C170IACP + 6 CO2 + 6 ACP       β-Ketoacyl-ACP synthase III   fabHAB12   2MBACP + 6 MALACP + 12 NADPH -&gt;   fabHAB12       E               12 NADP + C170AACP + 6 CO2 + 6 ACP       β-Ketoacyl-ACP synthase III   fabHAB13   ACACP + 8 MALACP + 16 NADPH -&gt; 16   fabHAB13       E               NADP + 1 C180NACP + 8 CO2 + 8               ACP       Cardiolipin synthase   ywnE   2 PG &lt;-&gt; CL + GL   CLS   2.7.8.—   E       CDP-Diacylglycerol synthetase   cdsA   PA + CTP &lt;-&gt; CDPDG + PPI   CDSA   2.7.7.41   E       Malonyl-CoA-ACP transacylase   fabD   MALCOA + ACP &lt;-&gt; MALACP + COA   FADD1   2.3.1.39   E       Phosphatidylglycerol phosphate   PGPA   PGP -&gt; PI + PG   PGPA   3.1.3.27   E       phosphatase A       Phosphatidylglycerol phosphate   pgsA   CDPDG + GL3P &lt;-&gt; CMP + PGP   PGSA   2.7.8.5   E       synthase       Phosphatidylserine decarboxylase   psd   PS -&gt; PE + CO2   PSD   4.1.1.65   E       Phosphatidylserine synthase   pssA   CDPDG + SER &lt;-&gt; CMP + PS   PSSA   2.7.8.8   E       Fatty Acid Metabolism       3-Hydroxyacyl-CoA dehydrogenase   yusL1   HACOA + NAD &lt;-&gt; OACOA + NADH   FADBS   1.1.1.35       3-Hydroxyacyl-CoA dehydrogenase   yusL2   3H2MBCOA + NAD -&gt;   FADBS2   1.1.1.35               2MAACOA + NADH       3-Ketoacyl-CoA thiolase   yusK1   OACOA + COA -&gt; ACOA + ACCOA   FADA   2.3.1.16       3-Ketoacyl-CoA thiolase   yusK2   2MAACOA + COA -&gt; ACCOA + PPCOA   FADA2   2.3.1.16       Acetyl-CoA C-acetyltransferase   mmgA   2 ACCOA &lt;-&gt; COA + AACCOA   ATOB   2.3.1.9       Acyl-CoA dehydrogenase   acdA   ACOA + FAD -&gt; 23DACOA + FADH   FADE   1.3.99.3       Acyl-CoA synthetase   IcfA   ATP + LCCA + COA &lt;-&gt; AMP +   FADD   6.2.1.3   E               PPI + ACOA       Isoprenoid Biosynthesis       Farnesyl pyrophosphate synthetase   yqiD1   DMPP + IPPP -&gt; GPP + PPI   ISPA1   2.5.1.1   E       Geranyltranstransferase   yqiD2   GPP + IPPP -&gt; FPP + PPI   ISPA2   2.5.1.10   E       Isoprenyl pyrophosphate isomerase   ypgA   IPPP -&gt; DMPP   IPPPISO   5.3.3.2   E       Isoprenyl-pyrophosphate synthesis   dxr —     T3P1 + PYR + 2 NADPH + ATP -&gt;   IPPPSYN   8 rxns   E       pathway       IPPP + ADP + 2 NADP + CO2       Octoprenyl pyrophosphate synthase (5   ISPB   5 IPPP + FPP -&gt; OPP + 5 PPI   ISPB   2.5.1.—   E       reactions)       Undecaprenyl pyrophosphate synthase   UDPPSYN   8 IPPP + FPP -&gt; UDPP + 8 PPI   UDPPSYN   2.5.1.31       (8 reactions)       Quinone Biosynthesis       Menaquinone       Isochorismate synthase 1   dhbC   CHOR -&gt; ICHOR   MENF   5.4.99.6   E       Isochorismate synthase 1   menF   CHOR -&gt; ICHOR   MENF2   5.4.99.6       1,4-Dihydroxy-2-naphthoate   menA   DHNA + OPP -&gt; DMK + PPI + CO2   MENA   2.5.1.—   E       octaprenyltransferase       α-Ketoglutarate decarboxylase   menD1   AKG + TPP -&gt; SSALTPP + CO2   MEND1   4.1.1.71   E       Naphthoate synthase   menB   OSBCOA -&gt; DHNA + COA   MENB   4.1.3.36   E       O-Succinylbenzoate-CoA synthase   menC   SHCHC -&gt; OSB   MENC   4.2.1.—   E       O-Succinylbenzoic acid-CoA ligase   menE   OSB + ATP + COA -&gt; OSBCOA +   MENE   6.2.1.26   E               AMP + PPI       S-Adenosylmethionine-2-DMK   MENG   DMK + SAM -&gt; Q + SAH   MENG   2.1.1.—   E       methyltransferase       SHCHC synthase   menD2   ICHOR + SSALTPP -&gt; PYR +   MEND2   4.1.3.—   E               TPP + SHCHC       Enterochelin Biosynthesis       2,3-Dihydo-2,3-dihydroxybenzoate   dhbA   23DHDHB + NAD &lt;-&gt; 23DHB + NADH   ENTA   1.3.1.28       dehydrogenase       ATP-dependent activation of 2,3-   dhbE   23DHB + ATP &lt;-&gt; 23DHBA + PPI   ENTE   6.—.—.—       dihydroxybenzoate       ATP-dependent serine activating   ENTF   SER + ATP &lt;-&gt; SERA + PPI   ENTF   2.7.7.—       enzyme       Enterochelin synthetase   ENTD   3 SERA + 3 23DHBA -&gt; ENTER + 6 AMP   ENTD   6.—.—.—       Isochorismatase   dhbB   ICHOR &lt;-&gt; 23DHDHB + PYR   ENTB   3.3.2.1       Riboflavin Biosynthesis       3,4 Dihydroxy-2-butanone-4-phosphate   ribA2   RL5P -&gt; DB4P + FOR   RIBB   3.5.4.25   E       synthase       6,7-Dimethyl-8-ribityllumazine synthase   ribA3   DB4P + A6RP -&gt; D8RL + PI   RIBE   3.5.4.25   E       FAD synthetase   ribC1   FMN + ATP -&gt; FAD + PPI   RIBF2   2.7.7.2   E       GTP cyclohydrolase II   ribA1   GTP -&gt; D6RP5P + FOR + PPI   RIBA   3.5.4.25   E       Pryimidine deaminase   ribD   D6RP5P -&gt; A6RP5P + NH3   RIBD1   3.5.4.26   E       Pyrimidine phosphatase   PMDPHT   A6RP5P2 -&gt; A6RP + PI   PMDPHT       E       Pyrimidine reductase   ribT   A6RP5P + NADPH -&gt; A6RP5P2 + NADP   RIBD2   1.1.1.193   E       Riboflavin kinase   ribC2   RIBFLV + ATP -&gt; FMN + ADP   RIBF1   2.7.1.26   E       Riboflavin kinase   ribR —     RIBFLV + ATP -&gt; FMN + ADP   RIBF1b       Riboflavin synthase   ribE   2 D8RL -&gt; RIBFLV + A6RP   RIBC   2.5.1.9   E       Folate Biosynthesis       6-Hydroxymethyl-7,8 dihydropterin   folK   AHHMP + ATP -&gt; AMP + AHHMD   FOLK   2.7.6.3   E       pyrophosphokinase       Aminodeoxychorismate lyase   pabC   ADCHOR -&gt; PYR + PABA   PABC   4.—.—.—   E       Aminodeoxychorismate synthase   pabA1   CHOR + GLN -&gt; ADCHOR + GLU   PABA   4.1.3.—   E       Dihydrofolate reductase   dfrA   DHF + NADPH -&gt; NADP + THF   FOLA   1.5.1.3   E       Dihydrofolate synthetase   folC   DHPT + ATP + GLU -&gt; ADP + PI + DHF   FOLC   6.3.2.12   E       Dihydroneopterin aldolase   folB   DHP -&gt; AHHMP + GLAL   DHDNPA   4.1.2.25   E       Dihydropteroate synthase   sul   PABA + AHHMD -&gt; PPI + DHPT   FOLP   2.5.1.15   E       GTP cyclohydrolase I   mtrA   GTP -&gt; FOR + AHTD   FOLE   3.5.4.16   E       Nucleoside triphosphatase   phoA1   AHTD -&gt; DHP + 3 PI   MUTT   3.1.3.1   E       Nucleoside triphosphatase   phoB1   AHTD -&gt; DHP + 3 PI   MUTTa   3.1.3.1       Coenzyme A Biosynthesis       ACP Synthase   acpS   COA -&gt; PAP + ACP   ACPS   2.7.8.7       Aspartate decarboxylase   panD   ASP -&gt; CO2 + bALA   PAND   4.1.1.11   E       DephosphoCoA kinase   ytaG   DPCOA + ATP -&gt; ADP + COA   DPHCOAK   2.7.1.24   E       Ketopantoate reductase   ylbQ   AKP + NADPH -&gt; NADP + PANT   PANE   1.1.1.169   E       Ketopentoate hydroxymethyl   panB   OIVAL + METTHF -&gt; AKP + THF   PANB   2.1.2.11   E       transferase       Pantoate-β-alanine ligase   panC   PANT + bALA + ATP -&gt; AMP +   PANC   6.3.2.1   E               PPI + PNTO       Pantothenate kinase   coaA   PNTO + ATP -&gt; ADP + 4PPNTO   COAA   2.7.1.33   E       Phospho-pantethiene   PATRAN   4PPNTE + ATP -&gt; PPI + DPCOA   PATRAN   2.7.7.3   E       adenylyltransferase       Phosphopantothenate-cysteine   PCDCL   4PPNCYS -&gt; CO2 + 4PPNTE   PCDCL   4.1.1.36   E       decarboxylase       Phosphopantothenate-cysteine ligase   PCLIG   4PPNTO + CTP + CYS -&gt; CMP +   PCLIG   6.3.2.5   E               PPI + 4PPNCYS       NAD Biosynthesis       Aspartate oxidase   nadB   ASP + FAD -&gt; FADH + ISUCC   NADB   1.4.3.—       Deamido-NAD ammonia ligase   nadE   NAAD + ATP + NH3 -&gt;   NADE   6.3.5.1               NAD + AMP + PPI       NAD kinase   NADF   NAD + ATP -&gt; NADP + ADP   NADF   2.7.1.23       NADP phosphatase   NADPHPS   NADP -&gt; NAD + PI   NADPHPS   3.1.2.—       NAMN adenylyl transferase   yqeJ1   NAMN + ATP -&gt; PPI + NAAD   NADD1   2.7.7.18       NAMN adenylyl transferase   yqeJ2   NMN + ATP -&gt; NAD + PPI   NADD2   2.7.7.18       Quinolate phosphoribosyl transferase   nadC   QA + PRPP -&gt; NAMN + CO2 + PPI   NADC   2.4.2.19       Quinolate synthase   nadA   ISUCC + T3P2 -&gt; PI + QA   NADA   1.4.3.16       PNC IV       DNA ligase   ligA   NAD -&gt; NMN + AMP   LIG   6.5.1.2       Tetrapyrrole Biosynthesis       1,3-Dimethyluroporphyrinogen III   CYSG2   PC2 + NAD -&gt; NADH + SHCL   CYSG2       E       dehydrogenase       Coproporphyrinogen oxidase, aerobic   hemN   O2 + CPP -&gt; 2 CO2 + PPHG   HEMF   1.3.3.3   E       Coproporphyrinogen oxidase, aerobic   hemZ   O2 + CPP -&gt; 2 CO2 + PPHG   HEMF2       Ferrochelatase   hemH   PPIX -&gt; PTH   HEHH   4.99.1.1   E       Glutamate-1-semialdehyde   gsaB   GSA -&gt; ALAV   HEML   5.4.3.8   E       aminotransferase       Glutamate-1-semialdehyde   hemL   GSA -&gt; ALAV   HEML2       aminotransferase       Glutamyl-tRNA reductase   hemA   GTRNA + NADPH -&gt; GSA + NADP   HEMA   1.2.1.—   E       Glutamyl-tRNA synthetase   gltX   GLU + ATP -&gt; GTRNA + AMP + PPI   GLTX   6.1.1.17   E       Heme O synthase   ctaO   PTH + FPP -&gt; HO + PPI   CYOE       E       Hydroxymethylbilane synthase   hemC   4 PBG -&gt; HMB + 4 NH3   HEMC   4.1.3.8   E       Porphobilinogen synthase   hemB   8 ALAV -&gt; 4 PBG   HEMB   4.2.1.24   E       Protoporphyrinogen oxidase   hemY   O2 + PPHG -&gt; PPIX   HEMG   1.3.3.4   E       Siroheme ferrochelatase   CYSG3   SHCL -&gt; SHEME   CYSG3   4.99.1.—   E       Uroporphyrin-III C-methyltransferase 1   nasF   SAM + UPRG -&gt; SAH + PC2   HEMX   2.1.1.107   E       Uroporphyrin-III C-methyltransferase 2   ylnD   SAM + UPRG -&gt; SAH + PC2   CYSG1   2.1.1.107       Uroporphyrin-III C-methyltransferase 2   ylnF   SAM + UPRG -&gt; SAH + PC2   CYSG1a       Uroporphyrinogen decarboxylase   hemE   UPRG -&gt; 4 CO2 + CPP   HEME   4.1.1.37   E       Uroporphyrinogen III synthase   hemD   HMB -&gt; UPRG   HEMD   4.2.1.75   E       Heme A Synthase   ctaA   HO -&gt; HEMEA   HEMAS       E       Biotin Biosynthesis       8-Amino-7-oxononanoate synthase   bioF   ALA + CHCOA &lt;-&gt; CO2 + COA + AONA   BIOF   2.3.1.47       Adenosylmethionine-8-amino-7-   bioA   SAM + AONA &lt;-&gt; SAMOB + DANNA   BIOA   2.6.1.62       oxononanoate aminotransferase       Adenosylmethionine-8-amino-7-   yodT   SAM + AONA &lt;-&gt; SAMOB + DANNA   BIOA2       oxononanoate aminotransferase       Biotin synthase   bioB   DTB + CYS &lt;-&gt; BT   BIOB   2.8.1.—       Dethiobiotin synthase   bioD   CO2 + DANNA + ATP &lt;-&gt;   BIOD   6.3.3.3               DTB + PI + ADP       Thiamin (Vitamin B1) Biosynthesis       HMP kinase   HMPK   AHM + ATP -&gt; AHMP + ADP   THIN   2.7.1.49   E       HMP-phosphate kinase   thiD   AHMP + ATP -&gt; AHMPP + ADP   THID   2.7.4.7   E       Hypothetical Thimin Rxn   dxs   T3P1 + PYR -&gt; DTP   UNKRXN1       E       Thiamin kinase   THIK   THMP + ADP &lt;-&gt; THIAMIN + ATP   THIK   2.7.1.89   E       Thiamin phosphate kinase   thiL   THMP + ATP &lt;-&gt; TPP + ADP   THIL   2.7.4.16       Thiamin phosphate synthase   thiE   THZP + AHMPP -&gt; THMP + PPI   THIB   2.5.1.3   E       thiC protein   thiC   AIR -&gt; AHM   THIC       E       thiF protein   thiF   DTP + TYR + CYS -&gt; THZ + CO2   THIFb       E       thiG protein   thiG   DTP + TYR + CYS -&gt; THZ + CO2   THIGb       THZ kinase   thiM   THZ + ATP -&gt; THZP + ADP   THIM   2.7.1.50   E       Cell Envelope Biosynthesis       Glutamine fructose-6-phosphate   glmS   F6P + GLN -&gt; GLU + GA6P   GLMS   2.6.1.16   E       Transaminase       N-Acetylglucosamine-1-phosphate-   gcaD   UTP + GA1P + ACCOA -&gt; UDPNAG +   GLMU   2.7.7.23   E       uridyltransferase       PPI + COA       Phosphoglucosamine mutase   GLMM   GA6P &lt;-&gt; GA1P   GLMM       E       Techoic Acid Synthesis       Techoic Acid Syn (TagA to O)   tagAO1   PEPTIDO + UDPNAG + UDPNAMS + 30   TASYN1       E               CDPGLYC + 10 UDPG + 10 DALA -&gt;               GLYTC1 + UMP + UDP + 30 CMP + 10               UDP       Techoic Acid Syn (TagA to O)   tagAO2   PEPTIDO + UDPNAG + UDPNAMS + 30   TASYN2       E               UDPNAGAL + 30 UDPG -&gt; GLYTC2 +               UMP + UDP + 30 UMP + 30 UDP       UDP-N-acetylglucosamine 2-epimerase   yvyH   UDPNAG &lt;-&gt; UDPNAMS   UDP2E   5.1.3.14   E       Glycerol-3-phosphate   tagD   CTP + T3P1 -&gt; CDPGLYC + PPI   GLY3PCT   2.7.7.39   E       cytidylyltransferase       Teichuronic Acid Synthesis       Biosynthesis of teichuronic acid (UDP-   tuaD   UDPG -&gt; UDPGCU   UDPGDH       E       glucose 6-dehydrogenase)       UDP-N-acetylglucosamine 4-epimerase   UDPNA4E   UDPNAG -&gt; UDPNAGAL   UDPNA4E       E       Teichuronic Acid Syn (tau A to H)   tuaAH   PEPTIDO + 30 UDPNAGAL + 30   TUASYN       E               UDPGCU -&gt; 30 UMP + 30 UDP + TEICHU       LPS sugar biosynthesis       Diacylglycerol kinase   dgkA   DGR + ATP -&gt; ADP + PA   DAGKIN   2.7.1.107       Murein biosyntheis       D-ala: D-ala ligases   ddl   2 DALA + ATP &lt;-&gt; AA + ADP + PI   DDLA   6.3.2.4   E       D-Alanine-D-alanine adding enzyme   murF   UNAGD + ATP + AA -&gt; UNAGDA +   MURF   6.3.2.15   E               ADP + PI       Glutamate racemase   racE   GLU &lt;-&gt; DGLU   MURI   5.1.1.3       Glutamate racemase   yrpC   GLU &lt;-&gt; DGLU   MURI2   5.1.1.3       N-Acetylglucosaminyl transferase   murG   UNPTDO + UDPNAG -&gt; UDP + PEPTIDO   MURG   2.7.8.13   E       Phospho-N-   mraY   UNAGDA -&gt; UMP + PI + UNPTDO   MRAY   2.7.8.13   E       acetylmuramoylpentapeptide       transferase       UDP-N-acetylglucosamine-   murB   UDPNAGEP + NADPH -&gt; UDPNAM +   MURB   1.1.1.158   E       enolpyruvate dehydrogenase       NADP       UDP-N-acetylglucosamine-   murAA   UDPNAG + PEP -&gt; UDPNAGEP + PI   MURA   2.5.1.7   E       enolpyruvate transferase       UDP-N-acetylglucosamine-   murAB   UDPNAG + PEP -&gt; UDPNAGEP + PI   MURA2   2.5.1.7       enolpyruvate transferase       UDP-N-acetylmuramate-alanine ligase   murC   UDPNAM + ALA + ATP -&gt; ADP +   MURC   6.3.2.8   E               PI + UDPNAMA       UDP-N-acetylmuramoylalanine-D-   murD   UDPNAMA + DGLU + ATP -&gt;   MURD   6.3.2.9   E       glutamate ligase       UDPNAMAG + ADP + PI       UDP-N-acetylmuramoylalanyl-D-   murE   UDPNAMAG + ATP + MDAP -&gt;   MURE   6.3.2.13   E       glutamate 2,6-diaminopimelate ligase       UNAGD + ADP + PI       Membrane Transport       Carbohydrates       Arabinose (low affinity)   araE   ARABxt + HEXT &lt;-&gt; ARAB   ARABUP1       Fructose   fruA   FRUxt + PEP -&gt; F1P + PYR   FRUPTS       Fructose   levD   FRUxt + PEP -&gt; F1P + PYR   FRUPTS2       Glucitol   gutP   GLTxt + PEP -&gt; GLT6P + PYR   GLTUP       Gluconate   gntP   GLCNxt + HEXT -&gt; GLCN   GLCNUP2       Gluconate   yojA   GLCNxt + HEXT -&gt; GLCN   GLCNUP2       Glucosamine   gamP   GLAMxt + PEP -&gt; GA6P + PYR   GAUP       Glucose   ptsG   GLCxt + PEP -&gt; G6P + PYR   GLCPTS       E       Glycerol   glpF   GLxt &lt;-&gt; GL   GLUP       Maltose   malP   MLTxt + PEP -&gt; MLT6P + PYR   MALUP1       Mannitol   mtlA   MNTxt + PEP -&gt; MNT6P + PYR   MNTUP       Mannose   manP   MANxt + PEP -&gt; MAN1P + PYR   MANNUP       N-Acetylglucosamine   nagP   NAG + PEP -&gt; NAGP + PYR   NAGUP       Ribose   rbsA   RIBxt + ATP -&gt; RIB + ADP + PI   RIBUP       Sucrose   sacP   SUCxt + PEP -&gt; SUC6P + PYR   SUCUP       Trehalose   treP   TRExt + PEP -&gt; TRE6P + PYR   TREUP       Inositol   iolF   INOSTxt + HEXT -&gt; INOSIT   INOSUP       Amino Acids       Alanine   alsT   LAxt + HEXT -&gt; ALA   ALAUP2       Arginine   ARGUP   ARGxt + ATP -&gt; ARG + ADP + PI   ARGUP       Arginine   ARGUP2   ARGxt + HEXT &lt;-&gt; ARG   ARGUP2       Asparagine (high Affinity)   ASNUP2   ASNxt + ATP -&gt; ASN + ADP + PI   ASNUP2       Asparagine (low Affinity)   ASNUP1   ASNxt + HEXT &lt;-&gt; ASN   ASNUP1       Aspartate   ASPUP1   ASPxt + HEXT -&gt; ASP   ASPUP1       Aspartate   ASPUP2   ASPxt + ATP -&gt; ASP + ADP + PI   ASPUP2       Branched chain amino acid transport   BCAAUP1   BCAAxt + HEXT &lt;-&gt; BCAA   BCAAUP1       Dipeptide   dppB   DIPEPxt + ATP -&gt; DIPEP + ADP + PI   DPEPUP       -Aminobutyrate transport   gabP   GABAxt + ATP -&gt; GABA + ADP + PI   GABAUP       Glutamate   gltT   GLUxt + HEXT &lt;-&gt; GLU   GLUUP1       Glutamate   gltP   GLUxt + HEXT &lt;-&gt; GLU   GLUUP2       Glutamate   GLUUP3   GLUxt + ATP -&gt; GLU + ADP + PI   GLUUP3       Glutamine   glnH   GLNxt + ATP -&gt; GLN + ADP + PI   GLNUP       Histidine   ytmN   HISxt + ATP -&gt; HIS + ADP + PI   HISUP       Histidine   hutM   HISxt + HEXT &lt;-&gt; HIS   HISUP2       Isoleucine   ILEUP   ILExt + ATP -&gt; ILE + ADP + PI   ILEUP       Leucine   LEUUP   LEUxt + ATP -&gt; LEU + ADP + PI   LEUUP       Oligopeptide   appA   OPEPxt + ATP -&gt; OPEP + ADP + PI   OPEPUP       Oligopeptide   oppA   OPEPxt + ATP -&gt; OPEP + ADP + PI   OPEPUP2       Ornithine   ORNUP   ORNxt + ATP -&gt; ORN + ADP + PI   ORNUP       Ornithine   ORNUP2   ORNxt + HEXT &lt;-&gt; ORN   ORNUP2       Peptide   PEPUP   PEPTxt + ATP -&gt; PEPT + ADP + PI   PEPUP       Phenlyalanine   PHEUP   PHExt + HEXT &lt;-&gt; PHE   PHEUP       Proline   opuE   PROxt + HEXT &lt;-&gt; PRO   PROUP       Proline   opuB2   PROxt + ATP -&gt; PRO + ADP + PI   PROUP2       Threonine   THRUP1   THRxt + ATP -&gt; THR + ADP + PI   THRUP1       Threonine   THRUP2   THRxt + HEXT &lt;-&gt; THR   THRUP2       Tyrosine   TYRUP   TYRxt + HEXT &lt;-&gt; TYR   TYRUP       Valine   VALUP   VALxt + ATP -&gt; VAL + ADP + PI   VALUP       Purines &amp; Pyrimidines       Adenine   yxlA81   ADxt + HEXT -&gt; AD   ADUP       C-system   yxlA1   ADNxt + HEXT -&gt; ADN   NCCUP1       C-system   nupC6   URIxt + HEXT -&gt; URI   NCCUP2       C-system   nupC1   CYTDxt + HEXT -&gt; CYTD   NCCUP3       C-system   nupC3   DTxt + HEXT -&gt; DT   NCCUP4       C-system   yxlA2   DAxt + HEXT -&gt; DA   NCCUP5       C-system   nupC2   DCxt + HEXT -&gt; DC   NCCUP6       C-system   nupC4   DUxt + HEXT -&gt; DU   NCCUP7       Cytosine   nupC7   CYTSxt + HEXT -&gt; CYTS   CYTSUP       G-system   yxlA5   GSNxt + HEXT -&gt; GSN   NCGUP2       G-system   yxlA7   XTSNxt + HEXT -&gt; XTSN   NCGUP6       G-system   yxlA3   DGxt + HEXT -&gt; DG   NCGUP9       G-system (transports all nucleosides)   yxlA6   INSxt + HEXT -&gt; INS   NCGUP5       Guanine   pbuG1   GNxt &lt;-&gt; GN   GNUP       Hypoxanthine   pbuG2   HYXNxt &lt;-&gt; HYXN   HYXNUP       Nucleosides and deoxynucleoside   yxlA4   DINxt + HEXT -&gt; DIN   NCUP8       Uracil   pyrP   URAxt + HEXT -&gt; URA   URAUP       Xanthine   pbuX   XANxt &lt;-&gt; XAN   XANUP       Metabolic By-Products       Acetate transport   ACUP   ACxt + HEXT &lt;-&gt; AC   ACUP       Acetoin transport   ACTNUP   ACTNxt + HEXT &lt;-&gt; ACTN   ACTNUP       Diacetyl transport   DIACTUP   DIACTxt + HEXT &lt;-&gt; DIACT   DIACTUP       2,3-Butanediol transport   BUTNUP   BUTNxt + HEXT &lt;-&gt; BUTN   BUTNUP       Ethanol transport   ETHUP   ETHxt + HEXT &lt;-&gt; ETH   ETHUP       Lactate transport   lctP   LACxt + HEXT &lt;-&gt; LAC   LACUP1       Pyruvate transport   PYRUP   PYRxt + HEXT &lt;-&gt; PYR   PYRUP       Other Compounds       α-Ketoglutarate   dctB5   AKGxt + HEXT &lt;-&gt; AKG   AKGUP       α-Ketoglutarate/malate translocator   yflS   MALxt + AKG &lt;-&gt; MAL + AKGxt   AKMALUP       Ammonia transport   nrgA   NH3xt + HEXT &lt;-&gt; NH3   NH3UP       E       ATP drain flux for constant   ATPM   ATP -&gt; ADP + PI   ATPM       maintanence requirements       Carbon dioxide transport   CO2TX   CO2xt &lt;-&gt; CO2   CO2TX       E       Citrate   yraO   CITxt + HEXT -&gt; CIT   CITUP       Dicarboxylates   dctB2   SUCCxt + HEXT &lt;-&gt; SUCC   SUCCUP2       Dicarboxylates   dctB1   FUMxt + HEXT &lt;-&gt; FUM   FUMUP       Dicarboxylates   dctB3   MALxt + HEXT &lt;-&gt; MAL   MALUP3       Dicarboxylates   dctB4   ASPxt + HEXT &lt;-&gt; ASP   ASPUP       Glycerol-3-phosphate   glpT   GL3Pxt + HEXT -&gt; GL3P   GL3PUPa       Na/H antiporter   nhaC   NAxt + &lt;-&gt; NA + HEXT   NAUP1       Na/H antiporter   mrpA   NAxt + &lt;-&gt; NA + HEXT   NAUP2       Na/H antiporter   yhaU   NAxt + &lt;-&gt; NA + HEXT   NAUP3       Na/H antiporter   yjbQ   NAxt + &lt;-&gt; NA + HEXT   NAUP4       Nitrate transport   nasA   NO3xt + HEXT -&gt; NO3   NO3UP       Nitrate extrusion   narK   NO2xt + HEXT &lt;-&gt; NO2   NO2UP       Nitrite transport   ywcJ   NO2xt + HEXT -&gt; NO2   NO2UP2       Oxygen transport   O2TX   O2xt &lt;-&gt; O2   O2TX       E       Pantothenate   ywcA   PNTOxt + HEXT &lt;-&gt; PNTO   PANTOUP       Phosphate transport   pstA   PIxt + ATP -&gt; ADP + 2 PI   PIUP1       Phosphate transport   pit   PIxt + HEXT &lt;-&gt; PI   PIUP2       R       Potassium transport   trkA   Kxt + HEXT &lt;-&gt; K   POTUP2       Sulfate transport   cysP   H2SO4xt + HEXT -&gt; H2SO4   H2SO4UP2       E       Urea transport   pucJ   UREAxt + 2 HEXT &lt;-&gt; UREA   UREATX           FNADH   NAD -&gt; NADH   FNADH           FNADPH   NADP -&gt; NADPH   FNADPH           FATP   ADP + PI -&gt;ATP   FATP       Miscellaneous Reactions       beta-phosphoglucomutase/glucose-1-   pgcM   2 G1P -&gt; GLC + G16DP   BS001   5.4.2.6       phosphate phosphodismutase       unknown; similar to 2′,3′-cyclic-   yfkN   23CAMP -&gt; 3AMP   BS002       nucleotide 2°-phosphodiesterase       2-keto-3-deoxygluconate kinase   kdgK   2D3D6PG + ATP -&gt; ADP + 2KD6PG   BS003   2.7.1.45       2-keto-3-deoxygluconate   kduD   2DGLCN + NAD -&gt; 3D2DGLCN + NADH   BS004   1.1.1.125       oxidoreductase       methylmalonate-semialdehyde   mmsA   2M3OP + COA + NAD -&gt; PPCOA +   BS005   1.2.1.27       dehydrogenase       CO2 + NADH       unknown; similar to phosphoglycolate   yhcW   2PGLYC + H2O -&gt; GLYC + PI   BS006   3.1.3.18       phosphatase       naringenin-chalcone synthase   bcsA   3 MALCOA + CMRCOA -&gt; 4 COA +   BS007   2.3.1.74               NARGC + 3 CO2       assimilatory nitrite reductase (subunit)   nasD   3 NADPH + NO2 -&gt; 3 NADP + NH3   BS008   1.6.6.4       unknown; similar to 3-   yqeC   3H2MP + NAD -&gt; 2M3OP + NADH   BS009       hydroxyisobutyrate dehydrogenase       3-hydroxybutyryl-CoA dehydrogenase   mmgB   3HBCOA + NADP -&gt; AACCOA + NADPH           BS010   1.1.1.157       5-keto-4-deoxyuronate isomerase   kduI   4D5HSUR &lt;-&gt; 3DG25DS   BS011   5.3.1.17       unknown; similar to 4-   yoaI   4HPHAC + NADH + O2 -&gt; 34DHPHAC +   BS012       hydroxyphenylacetate-3-hydroxylase       NAD       unknown; similar to p-nitrophenyl   yutF   4NPPI + H2O -&gt; 4NPH + PI   BS013   3.1.3.41       phosphatase       unknown; similar to 5-dehydro-4-   ycbC   5D4DGLCR -&gt; 25DXP + H2O + CO2   BS014   4.2.1.41       deoxyglucarate dehydratase       6-phospho-beta-glucosidase   bglA   6PGG -&gt; GLC + G6P   BS015   3.2.1.86       6-phospho-beta-glucosidase   licH   6PGG -&gt; GLC + G6P   BS016   3.2.1.86       unknown; similar to N-   yvcN   ACCOA + HXARA -&gt; COA + ACARA   BS017       hydroxyarylamine O-acetyltransferase       probable maltose O-acetyltransferase   maa   ACCOA + MALT-&gt; COA + ACMALT   BS018   2.3.1.79       unknown; similar to serine O-   yvfD   ACCOA + SER -&gt; COA + OASER   BS019       acetyltransferase       alpha-acetolactate decarboxylase   alsD   ACLAC -&gt; CO2 + ACTN   BS020   4.1.1.5       acetoin dehydrogenase E1 component   acoA   ACTN + NAD -&gt; DIACT + NADH   BS021       (TPP-dependent alpha subunit)       acetoin dehydrogenase   acuA   ACTN + NAD -&gt; DIACT + NADH   BS022       Butanediol Dehydrogenase   BUTDH   ACTN + NADH &lt;-&gt; BUTN + NAD   BS023   1.1.1.4       ADP-ribose pyrophosphatase   nudF   ADPRIB -&gt; R5P + AMP   BS024   3.6.1.13       unknown; similar to purine-cytosine   yxlA8   ADxt + HEXT -&gt; AD   BS025       permease       allantoinase   pucH   ALLTN -&gt; ALLTT   BS026   3.5.2.5       tagaturonate reductase   uxaB   ALTRN + NAD -&gt; TAGATU +NADH   BS027   1.1.1.58       unknown; similar to diadenosine   yjbP   APPPPA -&gt; 2 ADP   BS028   3.6.1.41       tetraphosphatase       probable branched-chain fatty-acid   buk   ATP + BUT -&gt; ADP + BUTP   BS029   2.7.2.7       kinase (butyrate kinase)       6-carboxyhexanoate-CoA ligase   bioW   ATP + CHX -&gt; AMP + PPI + CHCOA   BS030   6.2.1.14       deoxyadenosine/deoxycytidine kinase   dck1   ATP + DA -&gt; ADP + DAMP   BS031       deoxyadenosine/deoxycytidine kinase   dck3   ATP + DC -&gt; ADP + DCMP   BS032       deoxyadenosine/deoxycytidine kinase   dck2   ATP + DG -&gt; ADP + DGMP   BS033       deoxyguanosine kinase   dgk1   ATP + DG -&gt; GDP + DAMP   BS034       deoxyguanosine kinase   dgk2   ATP + DIN -&gt; IDP + DAMP   BS035       unknown; similar to fructokinase   ydhR —     ATP + FRUC -&gt; ADP + F6P   BS036   2.7.1.4       unknown; similar to fructokinase   ydjE   ATP + FRUC -&gt; ADP + F6P   BS037   2.7.1.4       GTP pyrophosphokinase (stringent   relA   ATP + GTP -&gt; GDPTP + AMP   BS038   2.7.6.5       response)       unknown; similar to GTP   yjbM   ATP + GTP -&gt; GDPTP + AMP   BS039       pyrophosphokinase       unknown; similar to GTP-   ywaC   ATP + GTP -&gt; GDPTP + AMP   BS040       pyrophosphokinase       unknown; similar to propionyl-CoA   yngE   ATP + PPCOA + CO2 -&gt; ADP +   BS041   6.4.1.3       carboxylase       PI + SMMCOA       unknown; similar to propionyl-CoA   yqjD   ATP + PPCOA + CO2 -&gt; ADP +   BS042       carboxylase       PI + SMMCOA       unknown; similar to pyruvate, water   yvkC   ATP + PYR -&gt; AMP + PEP + PI   BS043       dikinase       unknown; similar to benzaldehyde   yfmT   BENALD + NADP -&gt; BENZ + NADPH   BS044       dehydrogenase       unknown; similar to aryl-alcohol   ycsN   BENOH + NAD-&gt; BENALD + NADH   BS045   1.1.1.90       dehydrogenase       probable phosphate butyryltransferase   ptb   BUTCOA + PI &lt;-&gt; COA + BUTP   BS046   2.3.1.19       unknown; similar to ribonucleoside-   yosN   CDP + RTHIO -&gt; DCDP + OTHIO   BS047   1.17.4.1       diphosphate reductase (alpha subunit)       unknown; similar to CDP-glucose 4,6-   yfnG   CDPGLC -&gt; CDP46GLC + H2O   BS048   4.2.1.45       dehydratase       choline ABC transporter (choline-   opuB3   CHOLxt + ATP -&gt; CHOL + ADP + PI   BS049       binding protein)       glycine betaine/carnitine/choline ABC   opuC2   CHOLxt + ATP -&gt; CHOL + ADP + PI   BS050       transporter (membrane protein)       para-aminobenzoate synthase   pabA2   CHOR + GLN -&gt; AN + PYR + GLU   BS051   4.1.3.—       glutamine amidotransferase (subunit B)/       anthranilate synthase (subunit II)       deoxyadenosine/deoxycytidine kinase   dck5   CTP + DC -&gt; CDP + DCMP   BS052       unknown; similar to glucose-1-   yfnH   CTP + G1P -&gt; PPI + CDPGLC   BS053   2.7.7.33       phosphate cytidylyltransferase       unknown; similar to cysteine   yubC   CYS + O2 -&gt; 3SALA   BS054       dioxygenase       uridine kinase   udk4   CYTD + ATP -&gt; ADP + CMP   BS055   2.7.1.48       uridine kinase   udk6   CYTD + CTP -&gt; CDP + CMP   BS056       pyrimidine-nucleoside phosphorylase   pdp1   CYTD + R1P -&gt; CYTS + PI   BS057   2.4.2.2       probable D-alanine aminotransferase   dat   DALA + AKG -&gt; PYR + DGLU   BS058   2.6.1.21       pyrimidine-nucleoside phosphorylase   pdp3   DT + R1P-&gt; THY + PI   BS059       deoxyadenosine/deoxycytidine kinase   dck6   DTTP + DC -&gt; DTDP + DCMP   BS060       alcohol dehydrogenase (assume   adhB   ETH + NAD -&gt; ACAL + NADH   BS061   1.1.1.1;       ethanol dehydrogenase)               1.2.1.1       NADP-dependent alcohol   adhA   ETH + NADP -&gt; ACAL + NADPH   BS062   1.1.1.2       dehydrogenase       unknown; similar to formaldehyde   yycR —     FORMALD + NAD + H2O -&gt;   BS063       dehydrogenase       FORMATE + NADH       unknown; similar to formate transporter   yrhG   FORxt + HEXT &lt;-&gt; FOR   BS064       glucuronate isomerase   uxaC2   GALUR &lt;-&gt; FRCUR   BS065   5.3.1.12       glucose 1-dehydrogenase   gdh   GLC + NAD -&gt; G15LAC + NADH   BS066   1.1.1.47       unknown; similar to glucose 1-   ycdF   GLC + NAD -&gt; G15LAC + NADH   BS067       dehydrogenase       unknown; similar to glucose 1-   yhdF   GLC + NAD -&gt; G15LAC + NADH   BS068       dehydrogenase       unknown; similar to glucose 1-   ykuF   GLC + NAD -&gt; G15LAC + NADH   BS069       dehydrogenase       unknown; similar to glucose 1-   ykvO   GLC + NAD -&gt; G15LAC + NADH   BS070       dehydrogenase       unknown; similar to glucose 1-   ywfD   GLC + NAD -&gt; G15LAC + NADH   BS071       dehydrogenase       unknown; similar to glucose 1-   yxbG   GLC + NAD -&gt; G15LAC + NADH   BS072       dehydrogenase       unknown; similar to glucose 1-   yxnA   GLC + NAD -&gt; G15LAC + NADH   BS073       dehydrogenase       unknown; similar to gluconate 5-   yxjF   GLCN + NADP -&gt; 5DHGLCN + NADPH   BS074   1.1.1.30       dehydrogenase       unknown; similar to glycerate   yvcT   GLCR + NAD -&gt; HPYR + NADH   BS075   1.1.1.215       dehydrogenase       unknown; similar to glucarate   ycbF   GLCR -&gt; 5D4DLCR + H2O   BS076   4.2.1.40       dehydratase       glucuronate isomerase   uxaC1   GLCUR &lt;-&gt; FRCUR   BS077   5.3.1.12       unknown; similar to glucosamine-   ybcM   GLN + F6P -&gt; GLU + GLCAM6P   BS078   2.6.1.16       fructose-6-phosphate aminotransferase       unknown; similar to glutamine-fructose-   yurP   GLN + F6P -&gt; GLU + GLCAM6P   BS079       6-phosphate transaminase       unknown; similar to 1-pyrroline-5-   ycgN   GLUGSAL + NAD -&gt; GLU + NADH   BS080   1.5.1.12       carboxylate dehydrogenase       unknown; similar to glycine oxidase   yurR —     GLY + O2 -&gt; GLX + NH3 + H2O2   BS081       glycine betaine ABC transporter (ATP-   opuA   GLYBETxt + ATP -&gt; GLYBET +   BS082       binding protein)       ADP + PI       choline ABC transporter (ATP-binding   opuB1   GLYBETxt + ATP -&gt; GLYBET +   BS083       protein)       ADP + PI       glycine betaine/carnitine/choline ABC   opuC1   GLYBETxt + ATP -&gt; GLYBET +   BS084       transporter (ATP-binding protein)       ADP + PI       glycerophosphoryl diester   glpQ   GLYPD + H2O -&gt; ALC + GL3P   BS085   3.1.4.46       phosphodiesterase       guanine deaminase   guaD   GN -&gt; XAN + NH3   BS086   3.5.4.3       deoxyadenosine/deoxycytidine kinase   dck4   GTP + DC -&gt; GDP + DCMP   BS087       unknown; similar to carbonic anhydrase   ybcF   H2CO3 -&gt; CO2 + H2O   BS088       unknown; similar to carbonic anhydrase   ytiB   H2CO3 -&gt; CO2 + H2O   BS089       unknown; similar to carbonic anhydrase   yvdA   H2CO3 -&gt; CO2 + H2O   BS090       unknown; similar to epoxide hydrolase   yfhM   H2O + EPOX -&gt; GLYCOL   BS091   3.3.2.3       unknown; similar to sulfite oxidase   yuiH   H2SO3 + O2 + H2O -&gt; H2SO4 + H2O2   BS092       unknown; similar to hippurate hydrolase   ykuR —     HIPP -&gt; BENZ + GLY   BS093       heptaprenyl diphosphate synthase   hepS   HXPP + IPPP -&gt; PPI + HTPP   BS094       component I       unknown; similar to L-iditol 2-   ydjL   IDITOL + NAD -&gt; SORB + NADH   BS095       dehydrogenase       iron-uptake system (binding protein)   feuA   IRONxt + ATP -&gt; IRON + ADP + Pi   BS096       (ABC Transport)       2-keto-3-deoxygluconate permease   kdgT   K3DGCxt + HEXT -&gt; K3DGC   BS097       lysine 2,3-aminomutase   kamA   LYS &lt;-&gt; DMHEX   BS098       6-phospho-alpha-glucosidase   malA   MAL6P -&gt; GLC + G6P   BS099   3.2.1.122       malate-H+/Na+-lactate antiporter   mleN   MALxt + Hxt + NA + LAC &lt;-&gt;   BS100               MAL + NAxt + LACxt       Na+/malate symporter   maeN   MALxt + NAxt &lt;-&gt; MAL + NA   BS101       unknown; similar to D-mannonate   yjmF   MANNT + NAD &lt;-&gt; FRCUR + NADH   BS102   1.1.1.57       oxidoreductase       altronate hydrolase   uxaA   MANNT -&gt; K3DGC + H2O   BS103   4.2.1.7       D-mannonate hydrolase   uxuA   MANNT -&gt; K3DGC + H2O   BS104   4.2.1.7       manganese ABC transporter   mntA   MNxt + ATP -&gt; MN + ADP + PI   BS105       (membrane protein)       Na+ ABC transporter (extrusion) (ATP-   natA   NA + ATP -&gt; NAxt + ADP + Pi   BS106       binding protein)       ornithine acetyltransferase/amino-acid   argJ   NAARON + GLU -&gt; ORN + NAGLU   BS107   2.3.1.35;       acetyltransferase               2.3.1.1       unknown; similar to acetylornithine   ylmB2   NAGMET -&gt; AC + MET   BS108       deacetylase       unknown; similar to nitric-oxide   yojN   NADP + N2O + H2O -&gt; 2 NO + NADPH   BS109       reductase (assume acceptor = NADP)       nitrate reductase (alpha subunit)   narG   NADPH + NO3 -&gt; NADP + NO2   BS110   1.7.99.4       assimilatory nitrate reductase (electron   nasB   NADPH + NO3 -&gt; NADP + NO2   BS111   1.6.6.4       transfer subunit)       FMN-containing NADPH-linked   nfrA   NADPH + RIBFLV -&gt; RIBFLVRD +   BS112   1.—.—.—       nitro/flavin reductase       NADP       unknown; similar to NADPH-flavin   ycnD   NADPH + RIBFLV -&gt; RIBFLVRD +   BS113       oxidoreductase       NADP       oxalate decarboxylase   oxdC   OAA -&gt; PYR + CO2   BS114   4.1.1.3       unknown; similar to   yqiQ   PEP &lt;-&gt; 3PNPYR   BS115   5.4.2.9       phosphoenolpyruvate mutase       unknown; similar to proline   yusM   PRO + FAD -&gt; GLUGSAL + FADH   BS116       dehydrogenase       unknown; similar to pyruvate oxidase   ydaP   PYR + PI + O2 + H2O -&gt; ACTP +   BS117   1.2.3.3               CO2 + H2O2       unknown; similar to ribulose-   ykrW   R15BP + CO2 -&gt; 2 3PG   BS118   4.1.1.39       bisphosphate carboxylase       unknown; similar to retinol   yusZ   retinol + NAD &lt;-&gt; retinal + NADH   BS119   1.1.1.105       dehydrogenase       unknown; similar to methylglyoxalase   yurT   SLGT -&gt; RGT + MTHGXL   BS120       unknown; similar to mandelate   yitF   SMAND &lt;-&gt; RMAND   BS121       racemase       unknown; similar to sorbitol-6-   yuxG   SORB6P + NAD -&gt; F6P + NADH   BS122   1.1.1.140       phosphate 2-dehydrogenase       sorbitol dehydrogenase   gutB   SORBT + NAD -&gt; SORB + NADH   BS123   1.1.1.14       squalene-hopene cyclase   sqhC   SQU -&gt; HOP   BS124       levansucrase   sacB   SUC + 26FRUCT -&gt; GLC + 26FRUCT   BS125   2.4.1.10       sucrase-6-phosphate hydrolase   sacA   SUC6P -&gt; SUC + PI   BS126   3.2.1.26       serine hydroxymethyltransferase   glyA   THF + SER &lt;-&gt; GLY + METTHF   BS127   2.1.2.1       pyrimidine-nucleoside transport protein   nupC5   THYxt + HEXT -&gt; THY   BS128       UDP-glucose diacylglycerol   ugtP   UDPG + DGR -&gt; UDP + GLCDG   BS129       glucosyltransferase       1,4-alpha-glucan branching enzyme   glgB   UDPGLC -&gt; UDP + GLYCOGEN   BS130   2.4.1.18       uricase   pucL   URATE + O2 -&gt; ALLTN   BS131   1.7.3.3       uridine kinase   udk1   URI + ATP -&gt; ADP + UMP   BS132   2.7.1.48       uridine kinase   udk3   URI + CTP -&gt; CDP + UMP   BS133   2.7.1.48       pyrimidine-nucleoside phosphorylase   pdp2   URI + R1P -&gt; URA + PI   BS134   2.4.2.2       xanthine dehydrogenase   pucABCDE   XAN + NAD -&gt; URATE + NADH   BS135       xanthine phosphoribosyltransferase   xpt   XAN + PRPP -&gt; RXAN5P + PPI   BS136   2.4.2.—                  
 
     [0190]                                   Abbreviation   Metabolite                  13DPG   1,3-bis-Phosphoglycerate       23CAMP   nucleoside 2′,3′-cyclic phosphate       23DACOA   2,3-dehydroacyl-CoA       23DHB   2,3-Dihydroxybenzoate       23DHBA   2,3-Dihydroxybenzoyl-adenylate       23DHDHB   2,3-Dihydo-2,3-dihydroxybenzoate       25DXP   2,5-Dioxopentanoate       26FRUCT   β-2,6-fructan       2A3O   2-Amino-3-oxobutanoate       2D3D6PG   2-Dehydro-3-deoxy-6-phospho-D-gluconate       2DGLCN   2-Deoxy-D-gluconate       2KD6PG   2-keto-3-deoxy-6-phospho-gluconate       2M3OP   2-methyl-3-oxopropanoate (Methylmalonate semialdehyde)       2MAACOA   2-Methyl-acetoacetyl-CoA       2MBACP   2-Methylbutanoyl-ACP       2MBCOA   2-Methylbutanoyl-CoA       2MBECOA   trans-2-Methyl-but-2-enoyl-CoA       2PG   2-Phosphoglycerate       2PGLYC   2-phosphoglycolate       34DHPHAC   3,4-dihydroxyphenylacetate       3AMP   nucleoside 3′-phosphate       3D2DGLCN   3-Dehydro-2-deoxy-D-gluconate       3DDAH7P   3-Deoxy-d-arabino heptulosonate-7-phosphate       3DG25DS   3-Deoxy-D-glycero-2,5-dexodiulosonate       3H2MBCOA   (S)-3-Hydroxy-2-methyl-CoA       3H2MP   3-hydroxy-2-methylpropanoate       3HBCOA   (S)-3-Hydroxy-isobutyryl-ACP       3HIBCOA   (S)-3-Hydroxy-isobutyryl-CoA       3HMGCOA   (S)-3-Hydroxy-3-methylglutaryl-CoA       3M2ECOA   3-Methylbut-2-enoyl-CoA       3MBACP   3-Methylbutanoyl-ACP       3MBCOA   3-Methylbutanoyl-CoA       3MGCOA   3-Methylglutaconyl-CoA       3PG   3-Phosphoglycerate       3PNPYR   3-phosphonopyruvate       3PSER   3-Phosphoserine       3PSME   3-Phosphate-shikimate       3SALA   3-sulfinoalanine       4D5HSUR   4-Deoxy-L-threo-5-hexosulose uronate       4HPHAC   4-hydroxyphenylacetate       4IMZP   4-imidazolone-5-propanoate       4NPH   4-nitrophenol       4NPPI   4-nitrophenyl phosphate       4PPNCYS   4′-Phosphopantothenoylcysteine       4PPNTE   4′-Phosphopantetheine       4PPNTO   4′-Phosphopantothenate       5D4DGLCR   5-Dehydro-4-deoxy-D-glucarate       5DHGLCN   5-dehydro-D-gluconate       5MTA   5-Methylthioadenosine       5MTR   5-Methylthio-D-ribose       5MTR1P   5-Methylthio-5-deoxy-D-ribulose 1-phosphate       5MTRP   S5-Methyl-5-thio-D-ribose       6PGG   6-phospho-β-D-glucosyl-(1,4)-D-glucose       A6RP   5-Amino-6-ribitylamino-2,4(1H,3H)-pyrimidinedione       A6RP5P   5-Amino-6-(ribosylamino)-2,4-(1H,3H)-pyrimidinedione 5′-phosphate       A6RP5P2   5-Amino-2,6-dioxy-4-(5′-phosphoribitylamino)pyrimidine       AA   D-Alanyl-D-alanine       AAC   Acetoacetate       AACCOA   Acetoacetyl-CoA       ABUT   2-Aceto-2-hydroxy butyrate       AC   Acetate       ACACP   Acetyl-ACP       ACAL   Acetaldehyde       ACARA   N-acetoxyarylamine       ACCOA   Acetyl-CoA       ACLAC   Acetolactate       ACMALT   acetyl-maltose       ACOA   Acyl-CoA       ACP   Acyl carrier protein       ACTN   Acetoin       ACTNxt   Acetoin external       ACTP   Acetyl-phosphate       AD   Adenine       ADCHOR   4-Amino-4-deoxychorismate       ADN   Adenosine       ADNxt   Adenosine external       ADP   Adenosine diphosphate       ADPGLC   ADP-Glucose       ADPRIB   ADPRibose       AGM   Agmatine       AHHMD   2-Amino-4-hydroxy-6-hydroxymethyl dihydropteridine-pp       AHHMP   2-Amino-4-hydroxy-6-hydroxymethyl dihydropteridine       AHM   4-Amino-5-hydroxymethyl-2-methylpyrimidine       AHMP   4-Amino-5-hydroxymethyl-2-methylpyrimidine-phosphate       AHMPP   4-Amino-5-hydroxymethyl-2-methylpyrimidine-pyrophosphate       AHTD   2-Amino-4-hydroxy-6-(erythro-1-2-3-trihydroxypropyl) dihydropteridine-p       AICAR   5-Phosphate-ribosyl-5-amino-4-imidazole carboxamide       AIR   5-Phosphoribosyl-5-aminoimidazole       AKG   α-Ketoglutarate       AKP   α-Ketopantoate       ALA   Alanine       ALAV   D-Aminolevulinate       ALC   Alcohol       ALLTN   Allantoin       ALLTT   Allantoate       ALTRN   D-altronate       AMP   Adenosine monophosphate       AN   Antranilate       AONA   8-Amino-7-oxononanoate       APPPPA   diadenosine tetraphospate       APS   Adenylyl sulfate       ARAB   Arabinose       ARG   Arginine       ARGSUCC   L-Arginio succinate       ASER   O-Acetylserine       ASN   Asparagine       ASP   Aspartate       ASPSA   Aspartic beta-semialdehyde       ASUC   Adenilsuccinate       ATP   Adenosine triphosphate       bALA   β-Alanine       BASP   β-Aspartyl phosphate       BCAA   Branched chain amino acid       bDG6P   β-D-Glucose 6-Phosphate       BENALD   Benzaldehyde       BENOH   Benzyl alcohol       BENZ   Benzoate       BT   Biotin       BUT   Butyrate       BUTCOA   Butanoyl-CoA       BUTN   Butanediol       BUTP   Butanoyl phosphate       C140IACP   Iso-C14:0-ACP       C140NACP   C14:0-ACP       C150AACP   Anteiso-C15:0-ACP       C150IACP   Iso-C15:0-ACP       C160IACP   Iso-C16:0-ACP       C160NACP   C16:0-ACP       C161IACP   Anteiso-C16:1-ACP       C161NACP   C16:1-ACP       C170AACP   Anteiso-C17:0-ACP       C170IACP   Iso-C17:0-ACP       C171AACP   Anteiso-C17:1-ACP       C171IACP   Iso-C17:1-ACP       C180NACP   C18:0-ACP       CAASP   Carbamoyl aspartate       CADV   Cadaverine       CAIR   5-Phosphoribosyl-5-aminoimidazole-4-carboxylate       CAP   Carbamoyl phosphate       CBHCAP   3-Carboxy-3-hydroxy-isocaproate       CDP   Cytidine diphosphate       CDP46GLC   CDP-4-dehydro-6-deoxy-D-glucose       CDPDG   CDP-1,2-Diacylglycerol       CDPGLC   CDP-Glucose       CDPGLYC   CDPglycerol       CH3SH   Methanethiol       CHCOA   6-Carboxyhexanoyl-coa       CHOL   Choline       CHOR   Chorisimate       CHX   6-Carboxyhexanoate       CIT   Citrate       CITR   L-Citrulline       CL   Cardiolypin       CMP   Cytidine monophosphate       CMRCOA   4-coumaroyl-CoA       CO2   Carbon dioxide       COA   Coenzyme A       CPAD5P   1-O-Carboxyphenylamino 1-deoxyribulose-5-phosphate       CPP   Coproporphyrinogen III       CTP   Cytidine triphosphate       CYS   Cysteine       CYTD   Cytidine       CYTS   Cytosine       D23PIC   2,3-Dihydro dipicolinate       D26PIM   L,I-2,6-Diamino pimelate       D6PGC   D-6-Phosphate-gluconate       D6PGL   D-6-Phosphate-glucono-delta-lactone       D6RP5P   2,5-Diamino-6-(ribosylamino)-4-(3H)-pyrimidinone 5&#39;-phosphate       D8RL   6,7-Dimethyl-8-(1-D-ribityl)lumazine       DA   Deoxyadenosine       DADP   Deoxyadenosine diphosphate       DALA   D-Alanine       DAMP   Deoxyadenosine monophosphate       DANNA   7,8-Diaminononanoate       DATP   Deoxyadenosine triphosphate       DB4P   3,4-Dihydroxy-2-butanone-4-phosphate       DC   Deoxycytidine       DCDP   Deoxycytidine diphosphate       DCMP   Deoxycytidine monophosphate       DCTP   Deoxycytidine triphosphate       DG   Deoxyguanosine       DGDP   Deoxyguanosine diphosphate       DGLU   D-Glutamate       DGMP   2-Deoxy-guanosine-5-phosphate       DGR   D-1,2-Diacylglycerol       DGTP   Deoxyguanosine triphosphate       DHF   Dihydrofolate       DHMVA   2,3-Dihydroxy-3-methyl-valerate       DHNA   1,4-Dihydroxy-2-naphthoic acid       DHP   Dihydroneopterin       DHPT   7,8-Dihydropteroate       DHSK   Dehydroshikimate       DHVAL   Dihydroxy-isovalerate       DIACT   Diacetyl       DIMGP   D-Erythro imidazoleglycerol-phosphate       DIN   Deoxyinosine       DIPEP   Dipeptide       DKMPP   2,3-Diketo-5-methylthio-1-phosphopentane       DMHEX   (3S)-3,6-diaminohexanoate       DMK   Demethylmenaquinone       DMPP   Dimethylallyl pyrophosphate       DOROA   Dihydroorotic acid       DPCOA   Dephosphocoenzyme A       DQT   3-Dehydroquinate       DR1P   Deoxyribose 1-Phosphate       DR5P   Deoxyribose 5-Phosphate       DSAM   Decarboxylated adenosylmethionine       DSER   D-Serine       DT   Thymidine       DTB   Dethiobiotin       DTDP   Thymidine diphosphate       DTMP   Thymidine monophosphate       DTP   1-Deoxy-d-threo-2-pentulose       DTTP   Thymidine triphosphate       DU   Deoxyuridine       DUDP   Deoxyuridine diphosphate       DUMP   Deoxyuridine monophosphate       DUTP   Deoxyuridine triphosphate       E4P   Erythrose 4-phosphate       ENTER   Enterochelin       EPOX   Epoxide       ETH   Ethanol       F1P   Fructose 1-Phosphate       F6P   Fructose 6-phosphate       FAD   Flavin adenine dinucleotide       FADH   Flavin adenine dinucleotide reduced       FAM   formamide       FDP   Fructose 1,6-diphosphate       FGAM   5-Phosphoribosyl-n-formylgycineamidine       FGAR   5-Phosphoribosyl-n-formylglycineamide       FMN   flavin mononucleotide       FOR   Formate       FORMALD   Formaldehyde       FPP   trans, trans Farnesyl pyrophosphate       FRCUR   D-fructuronate       FRU   Fructose       FTHF   10-formyl-tetrahydrofolate       FUM   Fumarate       G15LAC   D-glucono-1,5-lactone       G16DP   Glucose 1,6-diphosphate       G1P   Glucose 1-phosphate       G6P   Glucose 6-phosphate       GA1P   Glucosamine 1-phosphate       GA6P   D-Glucosamine       GABA   4-Aminobutanoate       GAL1P   Galactose 1-Phosphate       GALUR   D-galacturonate       GAR   5-Phosphate-ribosyl glycineamide       GDP   Guanosine diphosphate       GDPTP   guanosine 3&#39;-diphosphate 5&#39;-triphosphate       GL   Glycerol       GL3P   Glycerol 3-phosphate       GLAC   Galactose       GLAL   D-Glyceraldehyde       GLC   α-D-Glucose       GLCAM6P   glucosamine 6-phosphate       GLCDG   3-D-glucosyl-1,2-diacylglycerol       GLCN   Gluconate       GLCR   (R)-glycerate       GLCUR   D-glucuronate       GLN   Glutamine       GLT6P   Glucitol 6-Phosphate       GLU   Glutamate       GLUGSAL   1-pyrroline-5-carboxylate       GLUP   Glutamyl phosphate       GLX   Glyoxylate       GLY   Glycine       GLYBET   Glycine Betaine       GLYC   glycolate       GLYCOGEN   Glycogen       GLYCOL   Glycol       GLYPD   glycerophosphodiester       GLYTC1   D-alanyl glycerol teichoic acid       GLYTC2   glucosyl glycerol teichoic acid       GMP   Guanosine monophosphate       GN   Guanine       GPP   trans Geranyl pyrophosphate       GSA   Glutamate-1-semialdehyde       GSN   Guanosine       GTP   Guanosine triphosphate       GTRNA   L-Glutamyl-tRNA(glu)       H2CO3   Carbonate       H2O   Water       H2O2   Hydrogen Peroxide       H2S   Hydrogen sulfide       H2SO3   Sulfite       H2SO4   Sulfate       HACOA   Hydroxyacyl-CoA       HCYS   Homocysteine       HEMEA   Heme A       HEXT   External H+       HIPP   hippurate       HIS   Histidine       HISOL   Histidinol       HISOLP   L-Histidinol-phosphate       HMB   Hydroxymethylbilane       HO   Heme O       HOP   Hopene       HPHPYR   para-Hydroxy phenyl pyruvate       HPYR   hydroxypyruvate       HSER   Homoserine       HTPP   heptaprenyl diphosphate       HXARA   N-Hydroxyarylamine       HXPP   hexaprenyl diphosphate       HYXN   Hypoxanthine       ICHOR   Isochorismate       ICIT   Isocitrate       IDITOL   L-Iditol       IDP   Inosine diphosphate       IGP   Indole glycerol phosphate       ILE   Isoleucine       IMACP   Imidazole acetyl-phosphate       IMP   Inosine monophosphate       INOSIT   Inositol       INS   Inosine       IPPMAL   3-Isopropylmalate       IPPP   Isopentyl pyrophosphate       IRON   IRON       ISBACP   Isobutyryl-ACP       ISBCOA   Isobutyryl-CoA       ISUCC   α-iminosuccinate       ITP   Inosine triphosphate       K   Potassium       K3DGC   2-keto-3-deoxygluconate       KMB   α-keto-g-methiobutyrate       LAC   D-Lactate       LACAL   Lactaldehyde       LCCA   Long-chain carboxylic acid       LCTS   Lactose       LEU   Leucine       LLAC   L-Lactate       LLCT   L-Cystathionine       LRL5P   L-Ribulose 5-phosphate       LYS   L-Lysine       MAL   Malate       MAL6P   Maltose 6-phosphate       MALACP   Malonyl-ACP       MALCOA   Malonyl-CoA       MALT   Maltose       MAN1P   Mannose 1-Phosphate       MAN6P   Mannose 6-Phosphate       MANNT   Mannonate       MCCOA   Methacrylyl-CoA       MDAP   Meso-diaminopimelate       MELI   Melibiose       MET   Methionine       METHF   5,10-Methenyl tetrahydrofolate       METTHF   5,10-Methylene tetrahydrofolate       MLT6P   Maltose 6-phosphate       MN   Manganese       MNT6P   Mannitol 6-Phosphate       MTHF   5-Methyl tetrahydrofolate       MTHGXL   Methylglyoxal       N2O   Nitrous Oxide       NA   Sodium       NAAD   Nicotinic acid adenine dinucleotide       NAARON   N-α-Acetyl omithine       NACMET   N-acetylmethionine       NAD   Nicotinamide adenine dinucleotide       NADH   Nicotinamide adenine dinucleotide reduced       NADP   Nicotinamide adenine dinucleotide phosphate       NADPH   Dihydronicotinamide adenine dinucleotide phosphate reduced       NAG   N-Acetylglucosamine       NAGLU   N-Acetyl glutamate       NAGLUSAL   N-Acetyl glutamate semialdehyde       NAGLUYP   N-Acetyl glutamyl-phosphate       NAGP   N-Acetylglucosamine (6-phosphate)       NAMN   Nicotinic acid mononucleotide       NARGC   naringenin-chalcone       NCAIR   5&#39;-Phosphoribosyl-5-carboxyaminoimidazole       NFGLU   N-formimidoyl-L-glutamate       NH3   Ammonia       NMN   Nicotinamide mononucleotide       NO   Nitric Oxide       NO2   Nitrite       NO3   Nitrate       NPRAN   N-5-phosphoribosyl-antranilate       NS26DP   N-Succinyl-I,I-2,6-diaminopimelate       NS2A6O   N-Succinyl-2-amino-6-ketopimelate       O2   Oxygen       OA   Oxaloacetate       OACOA   3-Oxoacyl-CoA       OASER   O-acetyl-L-serine       OBUT   Oxobutyrate       OICAP   2-Oxoisocaproate       OIVAL   3-Methyl-2-oxobutanoate (2-Oxoisovalerate)       OMP   Orotidylate       OMVAL   3-Methyl-2-oxopentanoate (OMVAL)       OPEP   Oligopeptide       OPP   trans Octaprenyl pyrophosphate       ORN   Ornithine       OROA   Orotic acid       OSB   O-Succinylbenzoic acid       OSBCOA   O-Succinylbenzoyl-CoA       OSLHSER   O-Succinyl-I-homoserine       OTHIO   Thioredoxin (oxidized form)       PA   Phosphatidyl acid       PABA   para-Aminobenzoic acid       PANT   Pantoate       PAP   Adenosine-3&#39;,5&#39;-diphosphate       PAPS   3-Phosphoadenylyl sulfate       PBG   Probilinogen III       PC2   Percorrin 2       PE   Phosphatidyl ethanolamine       PEP   Phosphoenolpyruvate       PEPT   Peptide       PEPTIDO   Peptidoglycan       PG   Phosphatidyl glycerol       PGP   L-1-Phoshatidyl-glycerol-phosphate       PHE   Phenylalanine       PHEN   Prephenate       PHP   3-Phosphohydroxypyruvate       PHPYR   Phenyl pyruvate       PHSER   O-Phospho-I-homoserine       PI   Phosphate (inorganic)       PIP26DX   Delta-piperidine-2,6-dicarboxylate       PNTO   Pantothenate       PPCOA   propanoyl-CoA       PPHG   Protoporphyrinogen       PPI   Pyrophosphate       PPIX   Protoporphyrin IX       PRAM   5-Phosphate-β-D-ribosyl amine       PRBAMP   Phosphoribosyl-AMP       PRBATP   Phosphoribosyl-ATP       PRFICA   5-Phosphate-ribosyl-formamido-4-imidazole carboxamide       PRFP   Phosphoribosyl-formimino-AICAR-phosphate       PRLP   Phosphoribulosyl-formimino-AICAR-phosphate       PRO   Proline       PRPP   Phosphoribosyl pyrophosphate       PS   Phosphatidyl serine       PTH   Protoheme       PTRC   Putrescine       PYR   Pyruvate       Q   Menaquinone       QA   Quinolinate       QH2   Ubiquinol       R15BP   D-ribulose 1,5-bisphosphate       R1P   Ribose 1-phosphate       R5P   Ribose 5-phosphate       RBL   Ribulose       retinal   Retinal       retinol   Retinol       RGT   Reduced glutathione       RIB   Ribose       RIBFLV   Riboflavin       RIBFLVRD   Riboflavin reduced       RL5P   Ribulose 5-phosphate       RMAND   (R)-mandelate       RML   Rhamnulose       RML1P   Rhamnulose 1-phosphate       RMMCOA   (R)-methylmalonyl-CoA       RMN   Rhamnose       RTHIO   Thioredoxin (reduced form)       RXAN5P   (9-D-ribosylxanthine)-5&#39;-phosphate       S7P   sedo-Heptulose       SAH   S-Adenosyl homocystine       SAICAR   5-Phosphoribosyl-4-(N-succinocarboxyamide)-5-amino-imidazole       SAM   S-Adenosyl methionine       SAMOB   S-Adenosyl-4-methylthio-2-oxobutanoate       SER   Serine       SERA   L-Seryl-adenylate       SHCHC   2-Succinyl-6-hydroxy-2,4-cyclohexadiene-1-carboxylate       SHCL   Sirohydrochlorin       SHEME   Siroheme       SLGT   (R)-S-lactoylglutathione       SMAND   (S)-mandelate       SME   Shikimate       SME5P   Shikimate-5-phosphate       SMMCOA   (S)-methylmalonyl-CoA       SORB   Sorbose       SORB6P   D-sorbitol 6-phosphate       SORBT   Sorbitol       SPMD   Spermidine       SQU   Squalene       SSALTPP   Succinate semialdehyde —thiamine pyrophosphate       SUC   Sucrose       SUC6P   Surose 6-phosphate       SUCC   Succinate       SUCCOA   Succinate CoA       SUCCSAL   Succinate semialdehyde       T3P1   Glyceraldehyde 3-phosphate       T3P2   Dihydroxyacetone-phosphate       TAGATU   D-tagaturonate       TEICHU   Teichuronic Acid       THF   Tetrahydrofolate       THIAMIN   Thiamin       THMP   Thiamine-phosphate       THR   Threonine       THY   Thymine       THZ   4-Methyl-5-(beta-hydroxyethyl)thiazole       THZP   4-Methyl-5-(beta-hydroxyethyl)thiazole phosphate       TPP   Thiamine-pyrophosphate       TRE6P   Trehalose 6-phosphate       TRP   Tryptophan       TYR   Tyrosine       UDP   Uridine diphosphate       UDPG   UDP Glucose       UDPGAL   UDP Galactose       UDPGCU   UDP-Glucouronate       UDPNAG   UDP N-acetyl glucosamine       UDPNAGAL   UDP-N-acetyl-Galactosamine       UDPNAGEP   UDP-N-acetyl-3-O-(1-carboxyvinyl)-D-glucosamine       UDPNAM   UDP-N-acetyl-D-muramate       UDPNAMA   UDP-N-acetylmuramoyl-L-alanine       UDPNAMAG   UDP-N-acetylmuramoyl-L-alanyl-D-glutamate       UDPNAMS   UDP-N-acetyl-Mannosamine       UDPP   Undecaprenyl pyrophosphate       UMP   Uridine monophosphate       UNAGD   UDP-N-acetylmuramoyl-L-alanyl-D-glutamyl-meso-2,6-diaminoheptanedioate       UNAGDA   UDP-N-acetylmuramoyl-L-alanyl-D-glutamyl-meso-2,6-diaminoheptanedioate-D-alanyl-D-alanine       UNPTDO   UDP-N-acetylmuramoyl-L-alanyl-D-glutamyl-meso-2,6-diaminoheptanedioate-D-alanyl-D-alanine-           diphosphoundecaprenol       UPRG   Uroporphyrinogen III       URA   Uracil       URATE   Urate       URCAN   urocanate       UREA   Urea       URI   Uridine       UTP   Uridine triphosphate       VAL   Valine       X5P   Xylulose-5-phosphate       XAN   Xanthine       XMP   Xantosine monophosphate       XTSN   Xanthosine       XUL   Xylulose       XYL   Xylose