Constraining the metabolic genotype–phenotype relationship using a phylogeny of in silico methods (original) (raw)

• Feist, A. M., Herrgård, M. J., Thiele, I., Reed, J. L. & Palsson, B. Ø. Reconstruction of biochemical networks in microorganisms. Nature Rev. Microbiol. 7, 129–143 (2009). This review provides the detailed concepts of metabolic network reconstruction.
  CAS Google Scholar
• Thiele, I. & Palsson, B. Ø. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nature Protoc. 5, 93–121 (2010).
  CAS Google Scholar
• Henry, C. S. et al. High-throughput generation, optimization and analysis of genome-scale metabolic models. Nature Biotech. 28, 977–982 (2010).
  Article CAS Google Scholar
• Feist, A. M. & Palsson, B. Ø. The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli. Nature Biotech. 26, 659–667 (2008).
  Article CAS Google Scholar
• Oberhardt, M. A., Palsson, B. Ø. & Papin, J. A. Applications of genome-scale metabolic reconstructions. Mol. Syst. Biol. 5, 320 (2009).
  Article PubMed PubMed Central Google Scholar
• Papp, B., Notebaart, R. A. & Pal, C. Systems-biology approaches for predicting genomic evolution. Nature Rev. Genet. 12, 591–602 (2011). A thorough review of how COBRA methods aid in the study of evolution.
  Article CAS PubMed Google Scholar
• Mahadevan, R., Palsson, B. Ø. & Lovley, D. R. In situ to in silico and back: elucidating the physiology and ecology of Geobacter spp. using genome-scale modelling. Nature Rev. Microbiol. 9, 39–50 (2011).
  Article CAS Google Scholar
• Palsson, B. Ø. Systems Biology: Properties of Reconstructed Networks (Cambridge Univ. Press, 2006).
  Book Google Scholar
• Feist, A. M. & Palsson, B. O. The biomass objective function. Curr. Opin. Microbiol. 13, 344–349 (2010). A description of how the biomass objective function is formulated.
  Article CAS PubMed PubMed Central Google Scholar
• Fell, D. A. & Small, J. R. Fat synthesis in adipose tissue. An examination of stoichiometric constraints. Biochem. J. 238, 781–786 (1986).
  Article CAS PubMed PubMed Central Google Scholar
• Watson, M. R. Metabolic maps for the Apple II. Biochem. Soc. Trans. 12, 1093–1094 (1984).
  Article CAS Google Scholar
• Edwards, J. S. & Palsson, B. O. Systems properties of the Haemophilus influenzae Rd metabolic genotype. J. Biol. Chem. 274, 17410–17416 (1999).
  Article CAS PubMed Google Scholar
• Edwards, J. S. & Palsson, B. O. The Escherichia coli MG1655 in silico metabolic genotype: its definition, characteristics, and capabilities. Proc. Natl Acad. Sci. USA 97, 5528–5533 (2000).
  Article CAS PubMed PubMed Central Google Scholar
• Reed, J. L., Famili, I., Thiele, I. & Palsson, B. O. Towards multidimensional genome annotation. Nature Rev. Genet. 7, 130–141 (2006).
  Article CAS PubMed Google Scholar
• Kim, T. Y., Kim, H. U. & Lee, S. Y. Data integration and analysis of biological networks. Curr. Opin. Biotechnol. 21, 78–84 (2010).
  Article CAS PubMed Google Scholar
• Sauer, U. Metabolic networks in motion: 13C-based flux analysis. Mol. Syst. Biol. 2, 62 (2006).
  Article PubMed PubMed Central Google Scholar
• Papin, J. A. et al. Comparison of network-based pathway analysis methods. Trends Biotechnol. 22, 400–405 (2004). An assessment of the differences between EFM and ExPa analysis.
  Article CAS PubMed Google Scholar
• Trinh, C. T., Wlaschin, A. & Srienc, F. Elementary mode analysis: a useful metabolic pathway analysis tool for characterizing cellular metabolism. Appl. Microbiol. Biotechnol. 81, 813–826 (2009).
  Article CAS PubMed Google Scholar
• Llaneras, F. & Pico, J. Which metabolic pathways generate and characterize the flux space? A comparison among elementary modes, extreme pathways and minimal generators. J. Biomed. Biotechnol. 2010, 753904 (2010).
  Article PubMed PubMed Central Google Scholar
• Stelling, J., Klamt, S., Bettenbrock, K., Schuster, S. & Gilles, E. D. Metabolic network structure determines key aspects of functionality and regulation. Nature 420, 190–193 (2002).
  Article CAS PubMed Google Scholar
• Trinh, C. T., Unrean, P. & Srienc, F. Minimal Escherichia coli cell for the most efficient production of ethanol from hexoses and pentoses. Appl. Environ. Microbiol. 74, 3634–3643 (2008).
  Article CAS PubMed PubMed Central Google Scholar
• Imielinski, M. & Belta, C. Exploiting the pathway structure of metabolism to reveal high-order epistasis. BMC Syst. Biol. 2, 40 (2008).
  Article CAS PubMed PubMed Central Google Scholar
• Wessely, F. et al. Optimal regulatory strategies for metabolic pathways in Escherichia coli depending on protein costs. Mol. Syst. Biol. 7, 515 (2011).
  Article CAS PubMed PubMed Central Google Scholar
• Schilling, C. H. & Palsson, B. Ø. Assessment of the metabolic capabilities of Haemophilus influenzae Rd through a genome-scale pathway analysis. J. Theor. Biol. 203, 249–283 (2000).
  Article CAS PubMed Google Scholar
• Yeung, M., Thiele, I. & Palsson, B. Ø. Estimation of the number of extreme pathways for metabolic networks. BMC Bioinformatics 8, 363 (2007).
  Article CAS PubMed PubMed Central Google Scholar
• Klamt, S. & Stelling, J. Combinatorial complexity of pathway analysis in metabolic networks. Mol. Biol. Rep. 29, 233–236 (2002).
  Article CAS PubMed Google Scholar
• Kaleta, C., de Figueiredo, L. F. & Schuster, S. Can the whole be less than the sum of its parts? Pathway analysis in genome-scale metabolic networks using elementary flux patterns. Genome Res. 19, 1872–1883 (2009).
  Article CAS PubMed PubMed Central Google Scholar
• Rezola, A. et al. Exploring metabolic pathways in genome-scale networks via generating flux modes. Bioinformatics 27, 534–540 (2011).
  Article CAS PubMed Google Scholar
• Ip, K., Colijn, C. & Lun, D. S. Analysis of complex metabolic behavior through pathway decomposition. BMC Syst. Biol. 5, 91 (2011).
  Article PubMed PubMed Central Google Scholar
• Chan, S. H. & Ji, P. Decomposing flux distributions into elementary flux modes in genome-scale metabolic networks. Bioinformatics 27, 2256–2262 (2011).
  Article CAS PubMed Google Scholar
• Braunstein, A., Mulet, R. & Pagnani, A. Estimating the size of the solution space of metabolic networks. BMC Bioinformatics 9, 240 (2008).
  Article CAS PubMed PubMed Central Google Scholar
• Schellenberger, J. & Palsson, B. Ø. Use of randomized sampling for analysis of metabolic networks. J. Biol. Chem. 284, 5457–5461 (2009).
  Article CAS PubMed Google Scholar
• Almaas, E., Kovacs, B., Vicsek, T., Oltvai, Z. N. & Barabasi, A. L. Global organization of metabolic fluxes in the bacterium Escherichia coli. Nature 427, 839–843 (2004).
  Article CAS PubMed Google Scholar
• Bordel, S., Agren, R. & Nielsen, J. Sampling the solution space in genome-scale metabolic networks reveals transcriptional regulation in key enzymes. PLoS Comput. Biol. 6, e1000859 (2010).
  Article CAS PubMed PubMed Central Google Scholar
• Mo, M. L., Palsson, B. Ø. & Herrgård, M. J. Connecting extracellular metabolomic measurements to intracellular flux states in yeast. BMC Syst. Biol. 3, 37 (2009).
  Article CAS PubMed PubMed Central Google Scholar
• Barrett, C. L., Herrgard, M. J. & Palsson, B. Decomposing complex reaction networks using random sampling, principal component analysis and basis rotation. BMC Syst. Biol. 3, 30 (2009).
  Article CAS PubMed PubMed Central Google Scholar
• Thiele, I., Price, N. D., Vo, T. D. & Palsson, B. Ø. Candidate metabolic network states in human mitochondria. Impact of diabetes, ischemia, and diet. J. Biol. Chem. 280, 11683–11695 (2005).
  Article CAS PubMed Google Scholar
• Price, N. D., Schellenberger, J. & Palsson, B. O. Uniformsampling of steady-state flux spaces: means to design experiments and to interpret enzymopathies. Biophys. J. 87, 2172–2186 (2004).
  Article CAS PubMed PubMed Central Google Scholar
• Lewis, N. E. et al. Large-scale in silico modeling of metabolic interactions between cell types in the human brain. Nature Biotech. 28, 1279–1285 (2010).
  Article CAS Google Scholar
• Orth, J. D., Thiele, I. & Palsson, B. Ø. What is flux balance analysis? Nature Biotech. 28, 245–248 (2010). A primer to the FBA method.
  Article CAS Google Scholar
• Varma, A. & Palsson, B. O. Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110. Appl. Environ. Microbiol. 60, 3724–3731 (1994).
  CAS PubMed PubMed Central Google Scholar
• Edwards, J. S., Ibarra, R. U. & Palsson, B. O. In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nature Biotech. 19, 125–130 (2001).
  Article CAS Google Scholar
• Lewis, N. E. et al. Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models. Mol. Syst. Biol. 6, 390 (2010).
  Article CAS PubMed PubMed Central Google Scholar
• Teusink, B., Wiersma, A., Jacobs, L., Notebaart, R. A. & Smid, E. J. Understanding the adaptive growth strategy of Lactobacillus plantarum by in silico optimisation. PLoS Comput. Biol. 5, e1000410 (2009).
  Article CAS PubMed PubMed Central Google Scholar
• Wang, Z. & Zhang, J. Impact of gene expression noise on organismal fitness and the efficacy of natural selection. Proc. Natl Acad. Sci. USA 108, E67–E76 (2011).
  Article PubMed PubMed Central Google Scholar
• Goffin, P. et al. Understanding the physiology of Lactobacillus plantarum at zero growth. Mol. Syst. Biol. 6, 413 (2010).
  Article CAS PubMed PubMed Central Google Scholar
• Lee, S., Palakornkule, C., Domach, M. M. & Grossmann, I. E. Recursive MILP model for finding all the alternate optima in LP models for metabolic networks. Comput. Chem. Eng. 24, 711–716 (2000).
  Article CAS Google Scholar
• Gudmundsson, S. & Thiele, I. Computationally efficient flux variability analysis. BMC Bioinformatics 11, 489 (2010).
  Article PubMed PubMed Central Google Scholar
• Burgard, A. P., Vaidyaraman, S. & Maranas, C. D. Minimal reaction sets for Escherichia coli metabolism under different growth requirements and uptake environments. Biotechnol. Prog. 17, 791–797 (2001).
  Article CAS PubMed Google Scholar
• Segre, D., Vitkup, D. & Church, G. M. Analysis of optimality in natural and perturbed metabolic networks. Proc. Natl Acad. Sci. USA 99, 15112–15117 (2002).
  Article CAS PubMed PubMed Central Google Scholar
• Benyamini, T., Folger, O., Ruppin, E. & Shlomi, T. Flux balance analysis accounting for metabolite dilution. Genome Biol. 11, R43 (2010).
  Article CAS PubMed PubMed Central Google Scholar
• Colijn, C. et al. Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production. PLoS Comput. Biol. 5, e1000489 (2009).
  Article CAS PubMed PubMed Central Google Scholar
• van Berlo, R. J. P. et al. Predicting metabolic fluxes using gene expression differences as constraints. IEEE/ACM Trans. Comput. Biol. Bioinform. 8, 206–216 (2011).
  Article PubMed Google Scholar
• Holzhutter, H. G. The principle of flux minimization and its application to estimate stationary fluxes in metabolic networks. Eur. J. Biochem. 271, 2905–2922 (2004).
  Article CAS PubMed Google Scholar
• Ponce de Leon, M., Cancela, H. & Acerenza, L. A strategy to calculate the patterns of nutrient consumption by microorganisms applying a two-level optimisation principle to reconstructed metabolic networks. J. Biol. Phys. 34, 73–90 (2008).
  Article PubMed PubMed Central Google Scholar
• Murabito, E., Simeonidis, E., Smallbone, K. & Swinton, J. Capturing the essence of a metabolic network: a flux balance analysis approach. J. Theor. Biol. 260, 445–452 (2009).
  Article CAS PubMed Google Scholar
• Beg, Q. K. et al. Intracellular crowding defines the mode and sequence of substrate uptake by Escherichia coli and constrains its metabolic activity. Proc. Natl Acad. Sci. USA 104, 12663–12668 (2007).
  Article CAS PubMed PubMed Central Google Scholar
• Vazquez, A., Markert, E. K. & Oltvai, Z. N. Serine biosynthesis with one carbon catabolism and the glycine cleavage system represents a novel pathway for ATP generation. PLoS ONE 6, e25881 (2011).
  Article CAS PubMed PubMed Central Google Scholar
• Zhuang, K., Vemuri, G. N. & Mahadevan, R. Economics of membrane occupancy and respiro-fermentation. Mol. Syst. Biol. 7, 500 (2011).
  Article PubMed PubMed Central Google Scholar
• Papp, B., Pal, C. & Hurst, L. D. Metabolic network analysis of the causes and evolution of enzyme dispensability in yeast. Nature 429, 661–664 (2004).
  Article CAS PubMed Google Scholar
• Deutscher, D., Meilijson, I., Kupiec, M. & Ruppin, E. Multiple knockout analysis of genetic robustness in the yeast metabolic network. Nature Genet. 38, 993–998 (2006).
  Article CAS PubMed Google Scholar
• Shlomi, T., Berkman, O. & Ruppin, E. Regulatory on/off minimization of metabolic flux changes after genetic perturbations. Proc. Natl Acad. Sci. USA 102, 7695–7700 (2005).
  Article CAS PubMed PubMed Central Google Scholar
• Kim, P. J. et al. Metabolite essentiality elucidates robustness of Escherichia coli metabolism. Proc. Natl Acad. Sci. USA 104, 13638–13642 (2007).
  Article CAS PubMed PubMed Central Google Scholar
• Chang, R. L., Xie, L., Xie, L., Bourne, P. E. & Palsson, B. Ø. Drug off-target effects predicted using structural analysis in the context of a metabolic network model. PLoS Comput. Biol. 6, e1000938 (2010).
  Article CAS PubMed PubMed Central Google Scholar
• Shen, Y. et al. Blueprint for antimicrobial hit discovery targeting metabolic networks. Proc. Natl Acad. Sci. USA 107, 1082–1087 (2010). A study in which metabolic networks are used to search for antimicrobials.
  Article CAS PubMed PubMed Central Google Scholar
• Kim, H. U. et al. Integrative genome-scale metabolic analysis of Vibrio vulnificus for drug targeting and discovery. Mol. Syst. Biol. 7, 460 (2011).
  Article CAS PubMed PubMed Central Google Scholar
• Fong, S. S. et al. In silico design and adaptive evolution of Escherichia coli for production of lactic acid. Biotechnol. Bioeng. 91, 643–648 (2005).
  Article CAS PubMed Google Scholar
• Burgard, A. P., Pharkya, P. & Maranas, C. D. Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol. Bioeng. 84, 647–657 (2003).
  Article CAS PubMed Google Scholar
• Feist, A. M. et al. Model-driven evaluation of the production potential for growth-coupled products of Escherichia coli. Metab. Eng. 12, 173–186 (2010).
  Article CAS PubMed Google Scholar
• Tepper, N. & Shlomi, T. Predicting metabolic engineering knockout strategies for chemical production: accounting for competing pathways. Bioinformatics 26, 536–543 (2010).
  Article CAS PubMed Google Scholar
• Patil, K. R., Rocha, I., Forster, J. & Nielsen, J. Evolutionary programming as a platform for in silico metabolic engineering. BMC Bioinformatics 6, 308 (2005).
  Article CAS PubMed PubMed Central Google Scholar
• Lun, D. S. et al. Large-scale identification of genetic design strategies using local search. Mol. Syst. Biol. 5, 296 (2009).
  Article PubMed PubMed Central Google Scholar
• Yousofshahi, M., Lee, K. & Hassoun, S. Probabilistic pathway construction. Metab. Eng. 13, 435–444 (2011).
  Article CAS PubMed Google Scholar
• Rodrigo, G., Carrera, J., Prather, K. J. & Jaramillo, A. DESHARKY: automatic design of metabolic pathways for optimal cell growth. Bioinformatics 24, 2554–2556 (2008).
  Article CAS PubMed Google Scholar
• Bar-Even, A., Noor, E., Lewis, N. E. & Milo, R. Design and analysis of synthetic carbon fixation pathways. Proc. Natl Acad. Sci. USA 107, 8889–8894 (2010).
  Article CAS PubMed PubMed Central Google Scholar
• Delebecque, C. J., Lindner, A. B., Silver, P. A. & Aldaye, F. A. Organization of intracellular reactions with rationally designed RNA assemblies. Science 333, 470–474 (2011).
  Article CAS PubMed Google Scholar
• Yim, H. et al. Metabolic engineering of Escherichia coli for direct production of 1,4-butanediol. Nature Chem. Biol. 7, 445–452 (2011).A detailed study in which several computational and experimental technologies are used to engineer a microorganism to synthesize 1,4-butanediol using.
  Article CAS Google Scholar
• Pharkya, P., Burgard, A. P. & Maranas, C. D. OptStrain: a computational framework for redesign of microbial production systems. Genome Res. 14, 2367–2376 (2004).
  Article CAS PubMed PubMed Central Google Scholar
• Szappanos, B. et al. An integrated approach to characterize genetic interaction networks in yeast metabolism. Nature Genet. 43, 656–662 (2011).
  Article CAS PubMed Google Scholar
• Hsiao, T. L., Revelles, O., Chen, L., Sauer, U. & Vitkup, D. Automatic policing of biochemical annotations using genomic correlations. Nature Chem. Biol. 6, 34–40 (2010).
  Article CAS Google Scholar
• Bordbar, A., Lewis, N. E., Schellenberger, J., Palsson, B. Ø. & Jamshidi, N. Insight into human alveolar macrophage and M. tuberculosis interactions via metabolic reconstructions. Mol. Syst. Biol. 6, 422 (2010).
  PubMed PubMed Central Google Scholar
• Schuster, S., Pfeiffer, T. & Fell, D. A. Is maximization of molar yield in metabolic networks favoured by evolution? J. Theor. Biol. 252, 497–504 (2008).A critical assessment of the assumptions in FBA.
  Article CAS PubMed Google Scholar
• Milne, C. B. et al. Metabolic network reconstruction and genome-scale model of butanol-producing strain Clostridium beijerinckii NCIMB 8052. BMC Syst. Biol. 5, 130 (2011).
  Article CAS PubMed PubMed Central Google Scholar
• Reed, J. L. et al. Systems approach to refining genome annotation. Proc. Natl Acad. Sci. USA 103, 17480–17484 (2006).
  Article CAS PubMed PubMed Central Google Scholar
• Orth, J. D. & Palsson, B. Ø. Systematizing the generation of missing metabolic knowledge. Biotechnol. Bioeng. 107, 403–412 (2010).
  Article CAS PubMed PubMed Central Google Scholar
• Satish Kumar, V., Dasika, M. S. & Maranas, C. D. Optimization based automated curation of metabolic reconstructions. BMC Bioinformatics 8, 212 (2007).
  Article CAS PubMed PubMed Central Google Scholar
• Kumar, V. S. & Maranas, C. D. GrowMatch: an automated method for reconciling in silico/in vivo growth predictions. PLoS Comput. Biol. 5, e1000308 (2009).
  Article CAS PubMed PubMed Central Google Scholar
• Suthers, P. F., Zomorrodi, A. & Maranas, C. D. Genome-scale gene/reaction essentiality and synthetic lethality analysis. Mol. Syst. Biol. 5, 301 (2009).
  Article CAS PubMed PubMed Central Google Scholar
• Mintz-Oron, S., Aharoni, A., Ruppin, E. & Shlomi, T. Network-based prediction of metabolic enzymes' subcellular localization. Bioinformatics 25, i247–i252 (2009).
  Article CAS PubMed PubMed Central Google Scholar
• Burgard, A. P. & Maranas, C. D. Optimization-based framework for inferring and testing hypothesized metabolic objective functions. Biotechnol. Bioeng. 82, 670–677 (2003).
  Article CAS PubMed Google Scholar
• Knorr, A. L., Jain, R. & Srivastava, R. Bayesian-based selection of metabolic objective functions. Bioinformatics 23, 351–357 (2007).
  Article CAS PubMed Google Scholar
• Schuetz, R., Kuepfer, L. & Sauer, U. Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli. Mol. Syst. Biol. 3, 119 (2007).
  Article CAS PubMed PubMed Central Google Scholar
• Gianchandani, E. P., Oberhardt, M. A., Burgard, A. P., Maranas, C. D. & Papin, J. A. Predicting biological system objectives de novo from internal state measurements. BMC Bioinformatics 9, 43 (2008).
  Article CAS PubMed PubMed Central Google Scholar
• Zomorrodi, A. R. & Maranas, C. D. Improving the iMM904 S. cerevisiae metabolic model using essentiality and synthetic lethality data. BMC Syst. Biol. 4, 178 (2010).
  Article PubMed PubMed Central Google Scholar
• Beard, D. A., Liang, S. D. & Qian, H. Energy balance for analysis of complex metabolic networks. Biophys. J. 83, 79–86 (2002).
  Article CAS PubMed PubMed Central Google Scholar
• Schellenberger, J., Lewis, N. E. & Palsson, B. Ø. Elimination of thermodynamically infeasible loops in steady-state metabolic models. Biophys. J. 100, 544–553 (2011).
  Article CAS PubMed PubMed Central Google Scholar
• Price, N. D., Thiele, I. & Palsson, B. Ø. Candidate states of Helicobacter pylori's genome-scale metabolic network upon application of “loop law” thermodynamic constraints. Biophys. J. 90, 3919–3928 (2006).
  Article CAS PubMed PubMed Central Google Scholar
• Henry, C. S., Broadbelt, L. J. & Hatzimanikatis, V. Thermodynamics-based metabolic flux analysis. Biophys. J. 92, 1792–1805 (2007).
  Article CAS PubMed Google Scholar
• Fleming, R. M., Thiele, I., Provan, G. & Nasheuer, H. P. Integrated stoichiometric, thermodynamic and kinetic modelling of steady state metabolism. J. Theor. Biol. 264, 683–692 (2010).
  Article CAS PubMed PubMed Central Google Scholar
• Kummel, A., Panke, S. & Heinemann, M. Putative regulatory sites unraveled by network-embedded thermodynamic analysis of metabolome data. Mol. Syst. Biol. 2, 2006.0034 (2006).
  Article CAS PubMed PubMed Central Google Scholar
• Jankowski, M. D., Henry, C. S., Broadbelt, L. J. & Hatzimanikatis, V. Group contribution method for thermodynamic analysis of complex metabolic networks. Biophys. J. 95, 1487–1499 (2008).
  Article CAS PubMed PubMed Central Google Scholar
• Henry, C. S., Jankowski, M. D., Broadbelt, L. J. & Hatzimanikatis, V. Genome-scale thermodynamic analysis of Escherichia coli metabolism. Biophys. J. 90, 1453–1461 (2006).
  Article CAS PubMed Google Scholar
• Hoppe, A., Hoffmann, S. & Holzhutter, H. G. Including metabolite concentrations into flux balance analysis: thermodynamic realizability as a constraint on flux distributions in metabolic networks. BMC Syst. Biol. 1, 23 (2007).
  Article CAS PubMed PubMed Central Google Scholar
• Fleming, R. M., Thiele, I. & Nasheuer, H. P. Quantitative assignment of reaction directionality in constraint-based models of metabolism: application to Escherichia coli. Biophys. Chem. 145, 47–56 (2009).
  Article CAS PubMed PubMed Central Google Scholar
• Becker, S. A. & Palsson, B. O. Context-specific metabolic networks are consistent with experiments. PLoS Comput. Biol. 4, e1000082 (2008).
  Article CAS PubMed PubMed Central Google Scholar
• Shlomi, T., Cabili, M. N., Herrgård, M. J., Palsson, B.Ø. & Ruppin, E. Network-based prediction of human tissue-specific metabolism. Nature Biotech. 26, 1003–1010 (2008).
  Article CAS Google Scholar
• Jerby, L., Shlomi, T. & Ruppin, E. Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism. Mol. Syst. Biol. 6, 401 (2010).
  Article PubMed PubMed Central Google Scholar
• Jensen, P. A. & Papin, J. A. Functional integration of a metabolic network model and expression data without arbitrary thresholding. Bioinformatics 27, 541–547 (2011).
  Article CAS PubMed Google Scholar
• Covert, M. W., Knight, E. M., Reed, J. L., Herrgard, M. J. & Palsson, B. O. Integrating high-throughput and computational data elucidates bacterial networks. Nature 429, 92–96 (2004).
  Article CAS PubMed Google Scholar
• Shlomi, T., Eisenberg, Y., Sharan, R. & Ruppin, E. A genome-scale computational study of the interplay between transcriptional regulation and metabolism. Mol. Syst. Biol. 3, 101 (2007).
  Article PubMed PubMed Central Google Scholar
• Covert, M. W., Xiao, N., Chen, T. J. & Karr, J. R. Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli. Bioinformatics 24, 2044–2050 (2008).
  Article CAS PubMed PubMed Central Google Scholar
• Lee, J. M., Gianchandani, E. P., Eddy, J. A. & Papin, J. A. Dynamic analysis of integrated signaling, metabolic, and regulatory networks. PLoS Comput. Biol. 4, e1000086 (2008).
  Article PubMed Google Scholar
• Folger, O. et al. Predicting selective drug targets in cancer through metabolic networks. Mol. Syst. Biol. 7, 501 (2011).
  Article PubMed PubMed Central Google Scholar
• Frezza, C. et al. Haem oxygenase is synthetically lethal with the tumour suppressor fumarate hydratase. Nature 477, 225–228 (2011).
  Article CAS PubMed Google Scholar
• Herrgård, M. J., Lee, B. S., Portnoy, V. & Palsson, B. Ø. Integrated analysis of regulatory and metabolic networks reveals novel regulatory mechanisms in Saccharomyces cerevisiae. Genome Res. 16, 627–635 (2006).
  Article CAS PubMed PubMed Central Google Scholar
• Chandrasekaran, S. & Price, N. D. Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis. Proc. Natl Acad. Sci. USA 107, 17845–17850 (2010).An approach for integrating regulatory networks with metabolic modelling.
  Article CAS PubMed PubMed Central Google Scholar
• Pal, C. et al. Chance and necessity in the evolution of minimal metabolic networks. Nature 440, 667–670 (2006).
  Article CAS PubMed Google Scholar
• Stolyar, S. et al. Metabolic modeling of a mutualistic microbial community. Mol. Syst. Biol. 3, 92 (2007).
  Article CAS PubMed PubMed Central Google Scholar
• Taffs, R. et al. In silico approaches to study mass and energy flows in microbial consortia: a syntrophic case study. BMC Syst. Biol. 3, 114 (2009).
  Article CAS PubMed PubMed Central Google Scholar
• Klitgord, N. & Segre, D. Environments that induce synthetic microbial ecosystems. PLoS Comput. Biol. 6, e1001002 (2010).
  Article CAS PubMed PubMed Central Google Scholar
• Wintermute, E. H. & Silver, P. A. Emergent cooperation in microbial metabolism. Mol. Syst. Biol. 6, 407 (2010).
  Article PubMed PubMed Central Google Scholar
• Zhuang, K. et al. Genome-scale dynamic modeling of the competition between Rhodoferax and Geobacter in anoxic subsurface environments. ISME J. 5, 305–316 (2011).
  Article PubMed Google Scholar
• Huthmacher, C., Hoppe, A., Bulik, S. & Holzhutter, H. G. Antimalarial drug targets in Plasmodium falciparum predicted by stage-specific metabolic network analysis. BMC Syst. Biol. 4, 120 (2010).
  Article CAS PubMed PubMed Central Google Scholar
• Yizhak, K., Tuller, T., Papp, B. & Ruppin, E. Metabolic modeling of endosymbiont genome reduction on a temporal scale. Mol. Syst. Biol. 7, 479 (2011).
  Article CAS PubMed PubMed Central Google Scholar
• Bonde, B. K., Beste, D. J., Laing, E., Kierzek, A. M. & McFadden, J. Differential producibility analysis (DPA) of transcriptomic data with metabolic networks: deconstructing the metabolic response of M. tuberculosis. PLoS Comput. Biol. 7, e1002060 (2011).
  Article CAS PubMed PubMed Central Google Scholar
• Nam, H., Conrad, T. M. & Lewis, N. E. The role of cellular objectives and selective pressures in metabolic pathway evolution. Curr. Opin. Biotechnol. 22, 595–600 (2011).
  Article CAS PubMed PubMed Central Google Scholar
• Srinivasan, K. & Mahadevan, R. Characterization of proton production and consumption associated with microbial metabolism. BMC Biotechnol. 10, 2 (2010).
  Article CAS PubMed PubMed Central Google Scholar
• Thiele, I., Jamshidi, N., Fleming, R. M. & Palsson, B. Ø. Genome-scale reconstruction of Escherichia coli's transcriptional and translational machinery: a knowledge base, its mathematical formulation, and its functional characterization. PLoS Comput. Biol. 5, e1000312 (2009).
  Article CAS PubMed PubMed Central Google Scholar
• Thiele, I., Fleming, R. M., Bordbar, A., Schellenberger, J. & Palsson, B. Ø. Functional characterization of alternate optimal solutions of Escherichia coli's transcriptional and translational machinery. Biophys. J. 98, 2072–2081 (2010).
  Article CAS PubMed PubMed Central Google Scholar
• Sigurdsson, M. I., Jamshidi, N., Steingrimsson, E., Thiele, I. & Palsson, B. Ø. A detailed genome-wide reconstruction of mouse metabolism based on human Recon 1. BMC Syst. Biol. 4, 140 (2010).
  Article CAS PubMed PubMed Central Google Scholar
• Sweetlove, L. J. & Ratcliffe, R. G. Flux-balance modelling of plant metabolism. Frontiers Plant Sci. 2, 38 (2011).
  Article CAS Google Scholar
• Gille, C. et al. HepatoNet1: a comprehensive metabolic reconstruction of the human hepatocyte for the analysis of liver physiology. Mol. Syst. Biol. 6, 411 (2010).
  Article CAS PubMed PubMed Central Google Scholar
• Kim, J. & Reed, J. L. OptORF: Optimal metabolic and regulatory perturbations for metabolic engineering of microbial strains. BMC Syst. Biol. 4, 53 (2010).
  Article CAS PubMed PubMed Central Google Scholar
• Bordbar, A. et al. A multi-tissue type genome-scale metabolic network for analysis of whole-body systems physiology. BMC Syst. Biol. 5, 180 (2011).
  Article PubMed PubMed Central Google Scholar
• Zhang, Y. et al. Three-dimensional structural view of the central metabolic network of Thermotoga maritima. Science 325, 1544–1549 (2009).
  Article CAS PubMed PubMed Central Google Scholar
• Barua, D., Kim, J. & Reed, J. L. An automated phenotype-driven approach (GeneForce) for refining metabolic and regulatory models. PLoS Comput. Biol. 6, e1000970 (2010).
  Article CAS PubMed PubMed Central Google Scholar
• Lee, K. H., Park, J. H., Kim, T. Y., Kim, H. U. & Lee, S. Y. Systems metabolic engineering of Escherichia coli for L-threonine production. Mol. Syst. Biol. 3, 149 (2007).
  Article CAS PubMed PubMed Central Google Scholar
• Alper, H., Jin, Y. S., Moxley, J. F. & Stephanopoulos, G. Identifying gene targets for the metabolic engineering of lycopene biosynthesis in Escherichia coli. Metab. Eng. 7, 155–164 (2005).
  Article CAS PubMed Google Scholar
• Kennedy, C. J., Boyle, P. M., Waks, Z. & Silver, P. A. Systems-level engineering of nonfermentative metabolism in yeast. Genetics 183, 385–397 (2009).
  Article CAS PubMed PubMed Central Google Scholar
• Xu, P., Ranganathan, S., Fowler, Z. L., Maranas, C. D. & Koffas, M. A. Genome-scale metabolic network modeling results in minimal interventions that cooperatively force carbon flux towards malonyl-CoA. Metab. Eng. 13, 578–587 (2011).
  Article CAS PubMed Google Scholar
• Chemler, J. A., Fowler, Z. L., McHugh, K. P. & Koffas, M. A. Improving NADPH availability for natural product biosynthesis in Escherichia coli by metabolic engineering. Metab. Eng. 12, 96–104 (2010).
  Article CAS PubMed Google Scholar
• Tepper, N. & Shlomi, T. Computational design of auxotrophy-dependent microbial biosensors for combinatorial metabolic engineering experiments. PLoS ONE 6, e16274 (2011).
  Article CAS PubMed PubMed Central Google Scholar