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EcoSal Plus

Domain 10: Bioinformatics and Systems Biology

Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide

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  • Authors: Jeffrey D. Orth1, R. M. T. Fleming2, and Bernhard Ø. Palsson3
  • Editor: Peter D. Karp4
  • VIEW AFFILIATIONS HIDE AFFILIATIONS
    Affiliations: 1: Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093–0412; 2: Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093–0412; 3: Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093–0412; 4: SRI International,Menlo Park, CA
  • Received 03 June 2009 Accepted 08 September 2009 Published 01 February 2010
  • Address correspondence to Bernhard Ø. Palsson palsson@ucsd.edu.
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  • Abstract:

    Biochemical network reconstructions have become popular tools in systems biology. Metabolicnetwork reconstructions are biochemically, genetically, and genomically (BiGG) structured databases of biochemical reactions and metabolites. They contain information such as exact reaction stoichiometry, reaction reversibility, and the relationships between genes, proteins, and reactions. Network reconstructions have been used extensively to study the phenotypic behavior of wild-type and mutant stains under a variety of conditions, linking genotypes with phenotypes. Such phenotypic simulations have allowed for the prediction of growth after genetic manipulations, prediction of growth phenotypes after adaptive evolution, and prediction of essential genes. Additionally, because network reconstructions are organism specific, they can be used to understand differences between organisms of species in a functional context.There are different types of reconstructions representing various types of biological networks (metabolic, regulatory, transcription/translation). This chapter serves as an introduction to metabolic and regulatory network reconstructions and models and gives a complete description of the core metabolic model. This model can be analyzed in any computational format (such as MATLAB or Mathematica) based on the information given in this chapter. The core model is a small-scale model that can be used for educational purposes. It is meant to be used by senior undergraduate and first-year graduate students learning about constraint-based modeling and systems biology. This model has enough reactions and pathways to enable interesting and insightful calculations, but it is also simple enough that the results of such calculations can be understoodeasily.

  • Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1

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/content/journal/ecosalplus/10.1128/ecosalplus.10.2.1
2010-02-01
2017-03-28

Abstract:

Biochemical network reconstructions have become popular tools in systems biology. Metabolicnetwork reconstructions are biochemically, genetically, and genomically (BiGG) structured databases of biochemical reactions and metabolites. They contain information such as exact reaction stoichiometry, reaction reversibility, and the relationships between genes, proteins, and reactions. Network reconstructions have been used extensively to study the phenotypic behavior of wild-type and mutant stains under a variety of conditions, linking genotypes with phenotypes. Such phenotypic simulations have allowed for the prediction of growth after genetic manipulations, prediction of growth phenotypes after adaptive evolution, and prediction of essential genes. Additionally, because network reconstructions are organism specific, they can be used to understand differences between organisms of species in a functional context.There are different types of reconstructions representing various types of biological networks (metabolic, regulatory, transcription/translation). This chapter serves as an introduction to metabolic and regulatory network reconstructions and models and gives a complete description of the core metabolic model. This model can be analyzed in any computational format (such as MATLAB or Mathematica) based on the information given in this chapter. The core model is a small-scale model that can be used for educational purposes. It is meant to be used by senior undergraduate and first-year graduate students learning about constraint-based modeling and systems biology. This model has enough reactions and pathways to enable interesting and insightful calculations, but it is also simple enough that the results of such calculations can be understoodeasily.

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Figure 1

The genome-scale metabolic reconstruction process can be summarized in four major phases, each of the latter phases building off the previous one. The fifth phase is use of the complete reconstruction for practical purposes (see “Uses of Metabolic Models”). Characteristic of the reconstruction process is the iterative refinement of reconstruction content that is driven by experimental data and occurs in phases 2 to 4. For each phase, specific data types are necessary and these range from high-throughput data types, e.g., metabolomics, to detailed studies characterizing individual components, e.g., biochemical data for a particular reaction. For example, the genome annotation can provide a parts list of a cell, while genetic data can provide information about the contribution of each gene product toward a phenotype when removed or mutated. The product generated from each reconstruction phase can be utilized and applied to examine a growing number of questions with the final product having the broadest applications. Figure adapted from reference 7 .

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Figure 2

The Δ in silico model predicts that loss of glucose-6-phosphate isomerase results in use of the pentose phosphate pathway as the primary route of glucose catabolism, as has been observed experimentally ( 54 ).

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Figure 3

The outer gray box represents the boundary between the model and the environmental source of substrates and sink for waste metabolites. The outer and inner blue boxes represent the outer and inner surface of the cytoplasmic membrane. The periplasmic space is outside the scope of the core model. Cytosolic metabolites are represented by orange circles and extracellular metabolites are represented by yellow circles.

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Figure 4

Genes are represented by blue boxes and designated by their locus name, translated peptides are represented by purple boxes, functional proteins are represented by red ovals, and reactions are labeled with blue boxes. For isozymes, two different proteins are connected to the same reaction. For proteins with multiple peptide subunits, the peptides are connected with an “&” above the protein. For complexes of multiple functional proteins, the proteins are connected with an “&” above the reaction. The genomic context of some of these genes is highlighted. Certain genes for the same reaction, e.g., and , are encoded by genes in operons widely separated on the genome. Operons are represented by shaded rectangles around one or more genes. Genes are represented by rectangles with one side pointed to denote the direction of the sense strand. Other operons contain multiple genes that encode protein subunits in a large protein. In this case, the same operon that codes for the proteins ( 75 ) also codes for two proteins of the 2-oxoglutarate dehydrogenase enzyme complex, . Genome context figures created by use of the Pathway Tools Genome Browser from EcoCyc ( 29 , 76 ).

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Figure 5

There is also the concomitant production of relatively small amounts of ATP and NADH. Gluconeogenesis refers to the production of sugars by reversing the flux through thermodynamically reversible reactions in the glycolytic pathway while bypassing reactions that are effectively thermodynamically irreversible.

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Figure 6

The irreversible, decarboxylating, oxidative pathway runs along the top, and below is the reversible set of rearrangements that constitute the nonoxidative pathway.

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Figure 7

The oxidative pathway of the TCA cycle runs counterclockwise in the lower part of the cycle, from oxaloacetate (oaa) through 2-oxoglutarate (akg). Continuing counterclockwise from 2-oxoglutarate, the full TCA cycle can totally oxidize acetyl-CoA, or it can function as two separate oxidative and reductive pathways. The reactions in the center of the cycle, and , are part of the glyoxylate cycle, discussed in Glyoxylate Cycle, Gluconeogenesis, and Anaplerotic Reactions.

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Figure 8

Acetate enters the glyoxylate cycle via acetyl-CoA, accoa, at two points: lower center left entering the citrate synthase reaction, , and in the center of the TCA cycle, entering the malate synthase reaction, . Tricarboxylic acid intermediates are then converted into gluconeogenic precursors via either malic enzyme, and , or phosphoenolpyruvate carboxykinase, . Gluconeogenesis then synthesizes 6-carbon sugars with the aid of reactions such as phosphoenolpyruvate synthase, , which effectively reverses an otherwise thermodynamically unfavorable glycolytic reaction. Phosphoenolpyruvate carboxylase, , is an anaplerotic reaction that uses glycolytic intermediates to replenish the precursors drained from the TCA cycle for biosynthesis.

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Figure 9

The electron transport chain (right) generates an electrochemical potential between the cytoplasm and periplasm. Oil-soluble ubiquinone-8 (q8) and ubiquinol-8 (q8h2) freely diffuse within the lipid membrane separating the periplasm and cytoplasm. Enzyme complexes that involve proton translocation straddle the cytoplasmic membrane and are in contact with periplasm and cytoplasm. ATP synthase, , catalyzes the synthesis of ATP from ADP by using the electrochemical potential between the cytoplasm and the periplasm (middle).

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Figure 10

It can be converted to -lactate (lac-D) and excreted, or it can be converted to formate (for), acetate (ac), acetaldehyde (acald), or ethanol (etoh).

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Figure 11

Glutamate dehydrogenase (NADP), , catalyzes direct ammonia assimilation by NADPH-specific reductive amination of 2-oxoglutarate (akg) to -glutamate (glu-L). Indirect ammonia assimilation is by a cyclic pair of sequential reactions, beginning with glutamine synthetase, , which catalyzes the addition of an amino moiety to glutamate to form glutamine (gln-L). Then, glutamate synthase (NADPH), , catalyzes the transfer of this amino moiety to 2-oxoglutarate, generating two molecules of glutamate. The net result is amination of 2-oxoglutarate to glutamate, as in the direct pathway.

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Figure 12

The total amount of ATP produced is exactly equal to the amount of ATP consumed, so [ATP]/ = 0.

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Figure 13

This is a single reaction that consumes and produces metabolites from many different pathways in the model. In order to grow, must be able to produce all of the compounds that are consumed. The metabolites that are consumed enter the reaction from the left, and the by-products that are produced leave the reaction to the right. The biomass reaction drains the precursors and energy/redox carriers 3-phospho--glycerate, acetyl-CoA, ATP, -erythrose 4-phosphate, -fructose 6-phosphate, glyceraldehyde 3-phosphate, -glucose 6-phosphate, -glutamine, -glutamate, HO, NAD, NADPH, oxaloacetate, phosphoenolpyruvate, pyruvate, and α--ribose 5-phosphate from the network while producing ADP, 2-oxoglutarate, coenzyme A, H, NADH, NADP, and phosphate.

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Figure 14

For the sake of clarity, reaction abbreviations are displayed rather than the gene(s) that code for the associated protein or protein complex. The Boolean state of in silico reactions, true = flux, or false = zero flux, may be determined by traversing the activation or inhibition links, beginning at an environmental metabolite stimulus (yellow circles). An anoxic environment is represented by o2[e] = false. Oxygen inactivates Fnr, represented by an inhibition link from o2[e] to Fnr. When o2[e] is false, then Fnr is true, indicating that Fnr is active in the absence of oxygen. Active Fnr then induces or represses the expression of genes for various reactions. Induction of the genes for fumarase, , by Fnr, is represented by an activation link from Fnr to . Repression of the genes for oxidative phosphorylation by Fnr is represented by the inactivation links from Fnr to NADH dehydrogenase, , and cytochrome oxidase, , i.e., activation assigns the target node the same Boolean status as the source node, whereas inhibition sets the Boolean status of the target to the opposite of the source. The central role of the global transcriptional regulators ArcA, Fnr, Crp, and FruR is evident from the number of reactions under their control. Boolean regulatory rules also encode combinatorial regulation of the same gene by multiple transcription factors. For details, see Table 16 .

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Figure 15

When the metabolites glucose, (glc-D[e]), lactate (lac-D[e]), or malate (mal-L[e]) are absent from the media, the conditions and are activated, regulating a variety of metabolic reactions.

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Figure 16

Extracellular ammonium (nh4[e]) activates the low- and high-level nitrogen responses, and , which, along with extracellular glutamate (glu-L[e]), inhibit the reactions glutamate dehydrogenase, , and glutamate synthase, . Glutaminase, , is also activated by extracellular ammonium. Extracellular glucose (glc-D[e]) through inhibits glutamine synthetase, , and glutaminase, .

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Figure 17

Transcription factors are indicated by blue diamonds. The genes and both code for subunits of the pyruvate dehydrogenase complex, . The gene codes for the phosphate transporter, .

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Figure 18

(A) Metabolic engineering, e.g., predicting the effect from a loss-of-function mutation as part of in silico strain design to overproduce desired products. (B) Biological discovery involves prospective use of biochemical and genetic information included in the metabolic network along with additional data types to drive discovery, e.g., predicting genes responsible for orphan reactions. (C) Phenotypic studies, e.g., computational analyses of gene, metabolite, and reaction essentiality. (D) Network analysis, e.g., finding coupled reaction fluxes across different growth conditions. (E) Evolutionary studies have used metabolic models to interpret adaptive evolution events, horizontal gene transfer, and minimal metabolic network evolution. From reference 158 .

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Figure 19

Reactions with thick blue arrows have nonzero fluxes, and reactions with thin black arrows carry zero flux.

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Figure 20

Triangles next to reaction names represent the genes associated with each reaction. Red genes and reactions are upregulated under anaerobic conditions, and green genes and reactions are downregulated.

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Figure 21

A high consistency score is given when the microarray data are in good agreement with the network topology necessary for the secretion of the product of interest. Each row has been normalized so that consistency scores within rows can be compared, but comparisons cannot be made between rows. Arrays 1 to 37 are wild type regulatory gene-deletion strains of grown on glucose under anaerobic conditions. Arrays 38 to 65 represent strains in which metabolic genes were knocked out followed by anaerobic growth on glucose. Arrays 66 to 77 are from regulatory gene-deletion mutants and wild-type strains grown anaerobically on glucose with nitrate used as the terminal electron acceptor. Arrays 78 to 98 and 148 to 170 are glucose aerobic wild-type and gene deletions of both regulatory proteins and metabolic enzymes. Arrays 99 to 147 are from wild-type strains that were under aerobically in lactate. Consistency scores are higher for production of most of the anaerobic fermentation products when using microarray data from anaerobic conditions, indicating that these conditions are more likely to lead to production of these products.

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Figure 22

Black envelopes are wild type, and red envelopes are for knockout strains. Growth is possible at any point inside the production envelope, but over time will adapt to the highest possible growth rate ( 26 ).

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Tables

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Table 1

Glycolysis reactions

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Table 2

Glycolysis metabolites

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Table 3

Pentose phosphate shunt reactions

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Table 4

Pentose phosphate metabolites

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Table 5

Tricarboxylic acid cycle reactions

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Table 6

Tricarboxylic acid cycle metabolites

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Table 7

Glyoxylate cycle, anaplerotic reactions, and gluconeogenesis reactions

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Table 8

Glyoxylate metabolite

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Table 9

Electron transport chain, oxidative phosphorylation, and transfer of reducing equivalents reactions

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Table 10

Electron transport chain, oxidative phosphorylation, and transfer of reducing equivalents metabolites

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Table 11

Fermentation reactions

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Table 12

Fermentation metabolites

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Table 13

Nitrogen metabolism reactions

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Table 14

Nitrogen metabolites

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Table 15

Twenty-three different metabolites consumed or produced to simulate growth in the biomass reaction

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Table 16

Regulatory rules for metabolic genes in the core model

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Table 17

Regulatory rules for transcriptional regulators and regulatory conditions

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Table 18

Maximum cofactor production from glucose, aerobically

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Table 19

Example COBRA Toolbox commands for performing flux balance analysis

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1
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Table 20

Orphan reactions in the core reconstruction

Citation: Orth J, Fleming R, Palsson B. 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Metabolic Model as an Educational Guide, EcoSal Plus 2010; doi:10.1128/ecosalplus.10.2.1

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