Genome-scale models of microbial cells: evaluating the consequences of constraints

Microbial cells operate under governing constraints that limit their range of possible functions. With the availability of annotated genome sequences, it has become possible to reconstruct genome-scale biochemical reaction networks for microorganisms. The imposition of governing constraints on a reconstructed biochemical network leads to the definition of achievable cellular functions. In recent years, a substantial and growing toolbox of computational analysis methods has been developed to study the characteristics and capabilities of microorganisms using a constraint-based reconstruction and analysis (COBRA) approach. This approach provides a biochemically and genetically consistent framework for the generation of hypotheses and the testing of functions of microbial cells.

[1]  F. Young Biochemistry , 1955, The Indian Medical Gazette.

[2]  Diffusion and chemical transformation. , 1973, Science.

[3]  I. H. Segel Enzyme Kinetics: Behavior and Analysis of Rapid Equilibrium and Steady-State Enzyme Systems , 1975 .

[4]  丸山 徹 Convex Analysisの二,三の進展について , 1977 .

[5]  Reinhart Heinrich,et al.  A metabolic osmotic model of human erythrocytes. , 1984, Bio Systems.

[6]  E. Papoutsakis Equations and calculations for fermentations of butyric acid bacteria , 1984, Biotechnology and bioengineering.

[7]  R Heinrich,et al.  A kinetic model for the interaction of energy metabolism and osmotic states of human erythrocytes. Analysis of the stationary "in vivo" state and of time dependent variations under blood preservation conditions. , 1985, Biomedica biochimica acta.

[8]  M. Domach,et al.  Simple constrained‐optimization view of acetate overflow in E. coli , 1990, Biotechnology and bioengineering.

[9]  Frederick C. Neidhardt,et al.  Physiology of the bacterial cell , 1990 .

[10]  B. Palsson,et al.  Stoichiometric interpretation of Escherichia coli glucose catabolism under various oxygenation rates , 1993, Applied and environmental microbiology.

[11]  K. Strange,et al.  Cellular and Molecular Physiology of Cell Volume Regulation , 1993 .

[12]  The Machinery of Life , 1993, Springer New York.

[13]  B. Palsson,et al.  Metabolic Capabilities of Escherichia coli II. Optimal Growth Patterns , 1993 .

[14]  B. Palsson,et al.  Metabolic Flux Balancing: Basic Concepts, Scientific and Practical Use , 1994, Bio/Technology.

[15]  B. Palsson,et al.  Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110 , 1994, Applied and environmental microbiology.

[16]  B O Palsson,et al.  Predictions for oxygen supply control to enhance population stability of engineered production strains , 1994, Biotechnology and bioengineering.

[17]  S. Schuster,et al.  ON ELEMENTARY FLUX MODES IN BIOCHEMICAL REACTION SYSTEMS AT STEADY STATE , 1994 .

[18]  A Danchin,et al.  By way of introduction: some constraints of the cell physics that are usually forgotten, but should be taken into account for in silico genome analysis. , 1996, Biochimie.

[19]  J. Liao,et al.  Pathway analysis, engineering, and physiological considerations for redirecting central metabolism. , 1996, Biotechnology and bioengineering.

[20]  H. Bonarius,et al.  Flux analysis of underdetermined metabolic networks: the quest for the missing constraints. , 1997 .

[21]  R Heinrich,et al.  Modeling the interrelations between the calcium oscillations and ER membrane potential oscillations. , 1997, Biophysical chemistry.

[22]  J. Keasling,et al.  Stoichiometric model of Escherichia coli metabolism: incorporation of growth-rate dependent biomass composition and mechanistic energy requirements. , 1997, Biotechnology and bioengineering.

[23]  M. Elowitz,et al.  Protein Mobility in the Cytoplasm ofEscherichia coli , 1999, Journal of bacteriology.

[24]  Juan Carlos Nuño,et al.  METATOOL: for studying metabolic networks , 1999, Bioinform..

[25]  R. Heinrich,et al.  Metabolic Pathway Analysis: Basic Concepts and Scientific Applications in the Post‐genomic Era , 1999, Biotechnology progress.

[26]  D. Fell,et al.  Detection of elementary flux modes in biochemical networks: a promising tool for pathway analysis and metabolic engineering. , 1999, Trends in biotechnology.

[27]  J. Edwards,et al.  Systems Properties of the Haemophilus influenzaeRd Metabolic Genotype* , 1999, The Journal of Biological Chemistry.

[28]  B. Palsson,et al.  The Escherichia coli MG1655 in silico metabolic genotype: its definition, characteristics, and capabilities. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[29]  D. Fell,et al.  A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks , 2000, Nature Biotechnology.

[30]  I. Grossmann,et al.  Recursive MILP model for finding all the alternate optima in LP models for metabolic networks , 2000 .

[31]  P Guerdoux-Jamet,et al.  Mapping the bacterial cell architecture into the chromosome. , 2000, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[32]  Bernhard O. Palsson,et al.  Metabolic flux balance analysis and the in silico analysis of Escherichia coli K-12 gene deletions , 2000, BMC Bioinformatics.

[33]  B. Palsson,et al.  Theory for the systemic definition of metabolic pathways and their use in interpreting metabolic function from a pathway-oriented perspective. , 2000, Journal of theoretical biology.

[34]  B. Palsson,et al.  Combining pathway analysis with flux balance analysis for the comprehensive study of metabolic systems. , 2000, Biotechnology and bioengineering.

[35]  B O Palsson,et al.  Metabolic modeling of microbial strains in silico. , 2001, Trends in biochemical sciences.

[36]  A. Burgard,et al.  Probing the performance limits of the Escherichia coli metabolic network subject to gene additions or deletions. , 2001, Biotechnology and bioengineering.

[37]  B. Palsson,et al.  In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data , 2001, Nature Biotechnology.

[38]  C Phalakornkule,et al.  A MILP-based flux alternative generation and NMR experimental design strategy for metabolic engineering. , 2001, Metabolic engineering.

[39]  B. Palsson,et al.  Regulation of gene expression in flux balance models of metabolism. , 2001, Journal of theoretical biology.

[40]  D. Kell,et al.  A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations , 2001, Nature Biotechnology.

[41]  D. Thorneycroft,et al.  Using gene knockouts to investigate plant metabolism. , 2001, Journal of experimental botany.

[42]  D. Bouchez,et al.  Arabidopsis gene knockout: phenotypes wanted. , 2001, Current opinion in plant biology.

[43]  R. Ellis Macromolecular crowding : obvious but underappreciated , 2022 .

[44]  A. Minton,et al.  The Influence of Macromolecular Crowding and Macromolecular Confinement on Biochemical Reactions in Physiological Media* , 2001, The Journal of Biological Chemistry.

[45]  Temple F. Smith,et al.  Overview of the Alliance for Cellular Signaling , 2002, Nature.

[46]  B. Palsson,et al.  Characterizing the metabolic phenotype: A phenotype phase plane analysis , 2002, Biotechnology and bioengineering.

[47]  Jason A. Papin,et al.  Determination of redundancy and systems properties of the metabolic network of Helicobacter pylori using genome-scale extreme pathway analysis. , 2002, Genome research.

[48]  Marc Vidal,et al.  Integrating Interactome, Phenome, and Transcriptome Mapping Data for the C. elegans Germline , 2002, Current Biology.

[49]  M. Lidstrom,et al.  Stoichiometric model for evaluating the metabolic capabilities of the facultative methylotroph Methylobacterium extorquens AM1, with application to reconstruction of C(3) and C(4) metabolism. , 2002, Biotechnology and bioengineering.

[50]  B. Palsson,et al.  Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth , 2002, Nature.

[51]  B. Palsson,et al.  Metabolic modelling of microbes: the flux-balance approach. , 2002, Environmental microbiology.

[52]  F. Doyle,et al.  Dynamic flux balance analysis of diauxic growth in Escherichia coli. , 2002, Biophysical journal.

[53]  H. Qian,et al.  Energy balance for analysis of complex metabolic networks. , 2002, Biophysical journal.

[54]  G. Church,et al.  Genome-Scale Metabolic Model of Helicobacter pylori 26695 , 2002, Journal of bacteriology.

[55]  Jason A. Papin,et al.  Extreme pathway lengths and reaction participation in genome-scale metabolic networks. , 2002, Genome research.

[56]  Daniel A Beard,et al.  Extreme pathways and Kirchhoff's second law. , 2002, Biophysical journal.

[57]  Neema Jamshidi,et al.  Description and analysis of metabolic connectivity and dynamics in the human red blood cell. , 2002, Biophysical journal.

[58]  Jason A. Papin,et al.  The genome-scale metabolic extreme pathway structure in Haemophilus influenzae shows significant network redundancy. , 2002, Journal of theoretical biology.

[59]  Jochen Förster,et al.  A functional genomics approach using metabolomics and in silico pathway analysis. , 2002, Biotechnology and bioengineering.

[60]  B. Palsson,et al.  Transcriptional regulation in constraints-based metabolic models of Escherichia coli Covert , 2002 .

[61]  S. Schuster,et al.  Metabolic network structure determines key aspects of functionality and regulation , 2002, Nature.

[62]  S. Schuster,et al.  Use of network analysis of metabolic systems in bioengineering , 2002 .

[63]  Friedrich Srienc,et al.  Metabolic pathway analysis of a recombinant yeast for rational strain development. , 2002, Biotechnology and bioengineering.

[64]  M. Snyder,et al.  "Omic" approaches for unraveling signaling networks. , 2002, Current opinion in cell biology.

[65]  G. Church,et al.  Analysis of optimality in natural and perturbed metabolic networks , 2002 .

[66]  Shankar Subramaniam,et al.  The Molecule Pages database , 2002, Nature.

[67]  Bernhard O Palsson,et al.  Network-based analysis of metabolic regulation in the human red blood cell. , 2003, Journal of theoretical biology.

[68]  A. Burgard,et al.  Optimization-based framework for inferring and testing hypothesized metabolic objective functions. , 2003, Biotechnology and bioengineering.

[69]  B. Palsson,et al.  Large-scale evaluation of in silico gene deletions in Saccharomyces cerevisiae. , 2003, Omics : a journal of integrative biology.

[70]  Bernhard O Palsson,et al.  The convex basis of the left null space of the stoichiometric matrix leads to the definition of metabolically meaningful pools. , 2003, Biophysical journal.

[71]  B. Palsson,et al.  An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR) , 2003, Genome Biology.

[72]  Bernhard Ø. Palsson,et al.  Description and Interpretation of Adaptive Evolution of Escherichia coli K-12 MG1655 by Using a Genome-Scale In Silico Metabolic Model , 2003, Journal of bacteriology.

[73]  A. Burgard,et al.  Optknock: A bilevel programming framework for identifying gene knockout strategies for microbial strain optimization , 2003, Biotechnology and bioengineering.

[74]  Upinder S. Bhalla,et al.  The Database of Quantitative Cellular Signaling: management and analysis of chemical kinetic models of signaling networks , 2003, Bioinform..

[75]  Kenneth J. Kauffman,et al.  Advances in flux balance analysis. , 2003, Current opinion in biotechnology.

[76]  B. Palsson,et al.  Constraints-based models: regulation of gene expression reduces the steady-state solution space. , 2003, Journal of theoretical biology.

[77]  A. Burgard,et al.  Exploring the overproduction of amino acids using the bilevel optimization framework OptKnock , 2003, Biotechnology and bioengineering.

[78]  Biochemical engineering in the 21st century , 2003 .

[79]  B. Palsson,et al.  Reconstructing metabolic flux vectors from extreme pathways: defining the α-spectrum , 2003 .

[80]  Derek R. Lovley,et al.  Cleaning up with genomics: applying molecular biology to bioremediation , 2003, Nature Reviews Microbiology.

[81]  Steffen Klamt,et al.  FluxAnalyzer: exploring structure, pathways, and flux distributions in metabolic networks on interactive flux maps , 2003, Bioinform..

[82]  R. Mahadevan,et al.  The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. , 2003, Metabolic engineering.

[83]  B. Palsson,et al.  Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. , 2003, Genome research.

[84]  Damien Hall,et al.  Macromolecular crowding: qualitative and semiquantitative successes, quantitative challenges. , 2003, Biochimica et biophysica acta.

[85]  B. Palsson,et al.  Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[86]  B. Palsson,et al.  Thirteen Years of Building Constraint-Based In Silico Models of Escherichia coli , 2003, Journal of bacteriology.

[87]  Jason A. Papin,et al.  Genome-scale microbial in silico models: the constraints-based approach. , 2003, Trends in biotechnology.

[88]  Tamar Schlick,et al.  Effect of DNA superhelicity and bound proteins on mechanistic aspects of the Hin-mediated and Fis-enhanced inversion. , 2003, Biophysical journal.

[89]  Jason A. Papin,et al.  Analysis of metabolic capabilities using singular value decomposition of extreme pathway matrices. , 2003, Biophysical journal.

[90]  B. Palsson,et al.  Identifying constraints that govern cell behavior: a key to converting conceptual to computational models in biology? , 2003, Biotechnology and bioengineering.

[91]  Bernhard Ø Palsson,et al.  Sequence-based analysis of metabolic demands for protein synthesis in prokaryotes. , 2003, Journal of theoretical biology.

[92]  T. Haystead,et al.  A functional proteomics approach to signal transduction. , 2003, Recent progress in hormone research.

[93]  Jason A. Papin,et al.  Metabolic pathways in the post-genome era. , 2003, Trends in biochemical sciences.

[94]  H. Qian,et al.  Stoichiometric network theory for nonequilibrium biochemical systems. , 2003, European journal of biochemistry.

[95]  Allen P. Minton,et al.  Cell biology: Join the crowd , 2003, Nature.

[96]  Michael Y. Galperin,et al.  In Silico Metabolic Model and Protein Expression of Haemophilus influenzae Strain Rd KW20 in Rich Medium. , 2004, Omics : a journal of integrative biology.

[97]  B. Palsson,et al.  Uniform sampling of steady-state flux spaces: means to design experiments and to interpret enzymopathies. , 2004, Biophysical journal.

[98]  C. Pál,et al.  Metabolic network analysis of the causes and evolution of enzyme dispensability in yeast , 2004, Nature.

[99]  Jason A. Papin,et al.  Comparison of network-based pathway analysis methods. , 2004, Trends in biotechnology.

[100]  B. Palsson,et al.  Genome-scale in silico models of E. coli have multiple equivalent phenotypic states: assessment of correlated reaction subsets that comprise network states. , 2004, Genome research.

[101]  Markus J. Herrgård,et al.  Integrating high-throughput and computational data elucidates bacterial networks , 2004, Nature.

[102]  A. Barabasi,et al.  Global organization of metabolic fluxes in the bacterium Escherichia coli , 2004, Nature.

[103]  J. Stelling,et al.  Combinatorial Complexity of Pathway Analysis in Metabolic Networks , 2004, Molecular Biology Reports.

[104]  R. Mahadevan,et al.  Using metabolic flux data to further constrain the metabolic solution space and predict internal flux patterns: the Escherichia coli spectrum , 2004, Biotechnology and bioengineering.

[105]  Harvey J. Greenberg,et al.  Reconstruction and Functional Characterization of the Human Mitochondrial Metabolic Network Based on Proteomic and Biochemical Data* , 2004, Journal of Biological Chemistry.

[106]  C. Schilling,et al.  Flux coupling analysis of genome-scale metabolic network reconstructions. , 2004, Genome research.

[107]  H. J. Greenberg,et al.  Monte Carlo sampling can be used to determine the size and shape of the steady-state flux space. , 2004, Journal of theoretical biology.

[108]  Jason A. Papin,et al.  The JAK-STAT signaling network in the human B-cell: an extreme signaling pathway analysis. , 2004, Biophysical journal.

[109]  Markus J. Herrgård,et al.  Reconstruction and validation of Saccharomyces cerevisiae iND750, a fully compartmentalized genome-scale metabolic model. , 2004, Genome research.

[110]  Stephen S Fong,et al.  Metabolic gene–deletion strains of Escherichia coli evolve to computationally predicted growth phenotypes , 2004, Nature Genetics.

[111]  Virgilio L. Lew,et al.  Volume, pH, and ion-content regulation in human red cells: Analysis of transient behavior with an integrated model , 2005, The Journal of Membrane Biology.