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Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0

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Heirendt, Laurent, Arreckx, Sylvain, Pfau, Thomas, Mendoza, Sebastián N., Richelle, Anne, Heinken, Almut, Haraldsdóttir, Hulda S., Wachowiak, Jacek, Keating, Sarah M., Vlasov, Vanja, Magnusdóttir, Stefania, Ng, Chiam Yu, Preciat, German, Žagare, Alise, Chan, Siu H. J., Aurich, Maike K., Clancy, Catherine M., Modamio, Jennifer, Sauls, John T., Noronha, Alberto, Bordbar, Aarash, Cousins, Benjamin, El Assal, Diana C., Valcarcel, Luis V., Apaolaza, Iñigo, Ghaderi, Susan, Ahookhosh, Masoud, Ben Guebila, Marouen, Kostromins, Andrejs, Sompairac, Nicolas, Le, Hoai M., Ma, Ding, Sun, Yuekai, Wang, Lin, Yurkovich, James T., Oliveira, Miguel A. P., Vuong, Phan T., El Assal, Lemmer P., Kuperstein, Inna, Zinovyev, Andrei, Hinton, H. Scott, Bryant, William A., Aragón Artacho, Francisco J., Planes, Francisco J., Stalidzans, Egils, Maass, Alejandro, Vempala, Santosh, Hucka, Michael, Saunders, Michael A., Maranas, Costas D., Lewis, Nathan E., Sauter, Thomas, Palsson, Bernhard Ø., Thiele, Ines, Fleming, Ronan M. T.·2019·Nature Protocols
DOI 10.1038/s41596-018-0098-2
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Abstract

Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. This protocol is an update to the COBRA Toolbox v.1.0 and v.2.0. Version 3.0 includes new methods for quality-controlled reconstruction, modeling, topological analysis, strain and experimental design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, respectively. This protocol provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods. The COBRA toolbox provides quality-controlled reconstruction, modeling, topological analysis, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data.

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Research works printed but not linked: 141

  1. O’Brien, E. J., Monk, J. M. & Palsson, B. O. Using genome-scale models to predict biological capabilities. Cell 161, 971–987 (2015). · DOI · Paper page
  2. Becker, S. A. et al. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nat. Protoc. 2, 727–738 (2007). · DOI
  3. Kitano, H., Ghosh, S. & Matsuoka, Y. Social engineering for virtual ‘big science’ in systems biology. Nat. Chem. Biol. 7, 323–326 (2011). · DOI
  4. Bordbar, A., Monk, J. M., King, Z. A. & Palsson, B. O. Constraint-based models predict metabolic and associated cellular functions. Nat. Rev. Genet. 15, 107–120 (2014). · DOI · Paper page
  5. Maia, P., Rocha, M. & Rocha, I. In silico constraint-based strain optimization methods: the quest for optimal cell factories. Microbiol. Mol. Biol. Rev. 80, 45–67 (2016). · DOI
  6. Hefzi, H. et al. A consensus genome-scale reconstruction of Chinese hamster ovary cell metabolism. Cell Syst. 3, 434–443.e8 (2016). · DOI
  7. Yusufi, F. N. K. et al. Mammalian systems biotechnology reveals global cellular adaptations in a recombinant CHO cell line. Cell Syst. 4, 530–542.e6 (2017). · DOI
  8. 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). · DOI · Paper page
  9. Jamshidi, N. & Palsson, B. Ø. Systems biology of the human red blood cell. Blood Cells Mol. Dis. 36, 239–247 (2006). · DOI
  10. Yizhak, K., Gabay, O., Cohen, H. & Ruppin, E. Model-based identification of drug targets that revert disrupted metabolism and its application to ageing. Nat. Commun. 4, 2632 (2013). · DOI
  11. Shlomi, T., Cabili, M. N. & Ruppin, E. Predicting metabolic biomarkers of human inborn errors of metabolism. Mol. Syst. Biol. 5, 263 (2009). · DOI
  12. Sahoo, S., Franzson, L., Jonsson, J. J. & Thiele, I. A compendium of inborn errors of metabolism mapped onto the human metabolic network. Mol. Biosyst. 8, 2545–2558 (2012). · DOI
  13. Pagliarini, R. & di Bernardo, D. A genome-scale modeling approach to study inborn errors of liver metabolism: toward an in silico patient. J. Comput. Biol. 20, 383–397 (2013). · DOI
  14. Shaked, I., Oberhardt, M. A., Atias, N., Sharan, R. & Ruppin, E. Metabolic network prediction of drug side effects. Cell Syst. 2, 209–213 (2016). · DOI
  15. Chang, R. L., Xie, L., Xie, L., Bourne, P. E. & Palsson, B. Drug off-target effects predicted using structural analysis in the context of a metabolic network model. PLoS Comput. Biol. 6, e1000938 (2010). · DOI
  16. Kell, D. B. Systems biology, metabolic modelling and metabolomics in drug discovery and development. Drug Discov. Today 11, 1085–1092 (2006). · DOI · Paper page
  17. Duarte, N. C. et al. Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc. Natl. Acad. Sci. USA 104, 1777–1782 (2007). · DOI · Paper page
  18. Pornputtapong, N., Nookaew, I. & Nielsen, J. Human metabolic atlas: an online resource for human metabolism. Database 2015, bav068 (2015). · DOI
  19. Zielinski, D. C. et al. Systems biology analysis of drivers underlying hallmarks of cancer cell metabolism. Sci. Rep. 7, 41241 (2017). · DOI
  20. Mardinoglu, A. et al. Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease. Nat. Commun. 5, 3083 (2014). · DOI
  21. Karlstädt, A. et al. CardioNet: a human metabolic network suited for the study of cardiomyocyte metabolism. BMC Syst. Biol. 6, 114 (2012). · DOI
  22. 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). · DOI · Paper page
  23. Martins Conde Pdo, R., Sauter, T. & Pfau, T. Constraint based modeling going multicellular. Front. Mol. Biosci. 3, 3 (2016). · DOI
  24. 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). · DOI
  25. Yizhak, K. et al. Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer. Elife 3, e03641 (2014). · DOI
  26. Mardinoglu, A. et al. Integration of clinical data with a genome-scale metabolic model of the human adipocyte. Mol. Syst. Biol. 9, 649 (2013). · DOI
  27. Bordbar, A. et al. Personalized whole-cell kinetic models of metabolism for discovery in genomics and pharmacodynamics. Cell Syst. 1, 283–292 (2015). · DOI
  28. Shoaie, S. et al. Quantifying diet-induced metabolic changes of the human gut microbiome. Cell Metab. 22, 320–331 (2015). · DOI · Paper page
  29. Nogiec, C. D. & Kasif, S. To supplement or not to supplement: a metabolic network framework for human nutritional supplements. PLoS ONE 8, e68751 (2013). · DOI
  30. Heinken, A., Sahoo, S., Fleming, R. M. T. & Thiele, I. Systems-level characterization of a host-microbe metabolic symbiosis in the mammalian gut. Gut Microbes 4, 28–40 (2013). · DOI
  31. Heinken, A. et al. Functional metabolic map of Faecalibacterium prausnitzii, a beneficial human gut microbe. J. Bacteriol. 196, 3289–3302 (2014). · DOI
  32. Magnúsdóttir, S. et al. Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota. Nat. Biotechnol. 35, 81–89 (2017). · DOI · Paper page
  33. Lakshmanan, M., Koh, G., Chung, B. K. S. & Lee, D.-Y. Software applications for flux balance analysis. Brief Bioinform. 15, 108–122 (2014). · DOI
  34. Ebrahim, A., Lerman, J. A., Palsson, B. O. & Hyduke, D. R. COBRApy: constraints-based reconstruction and analysis for Python. BMC Syst. Biol. 7, 74 (2013). · DOI · Paper page
  35. Heirendt, L., Thiele, I. & Fleming, R. M. T. DistributedFBA.jl: high-level, high-performance flux balance analysis in Julia. Bioinformatics 33, 1421–1423 (2017). · DOI
  36. Latendresse, M., Krummenacker, M., Trupp, M. & Karp, P. D. Construction and completion of flux balance models from pathway databases. Bioinformatics 28, 388–396 (2012). · DOI
  37. Ince, D. C., Hatton, L. & Graham-Cumming, J. The case for open computer programs. Nature 482, 485–488 (2012). · DOI
  38. Gevorgyan, A., Bushell, M. E., Avignone-Rossa, C. & Kierzek, A. M. SurreyFBA: a command line tool and graphics user interface for constraint-based modeling of genome-scale metabolic reaction networks. Bioinformatics 27, 433–434 (2011). · DOI
  39. Thorleifsson, S. G. & Thiele, I. rBioNet: a COBRA toolbox extension for reconstructing high-quality biochemical networks. Bioinformatics 27, 2009–2010 (2011). · DOI
  40. Sauls, J. T. & Buescher, J. M. Assimilating genome-scale metabolic reconstructions with modelBorgifier. Bioinformatics 30, 1036–1038 (2014). · DOI
  41. Noronha, A. et al. ReconMap: an interactive visualization of human metabolism. Bioinformatics 33, 605–607 (2017). · DOI
  42. Gawron, P. et al. MINERVA—a platform for visualization and curation of molecular interaction networks. npj Syst. Biol. Appl. 2, 16020 (2016). · DOI
  43. Olivier, B. G., Rohwer, J. M. & Hofmeyr, J.-H. S. Modelling cellular systems with PySCeS. Bioinformatics 21, 560–561 (2005). · DOI
  44. Gelius-Dietrich, G., Desouki, A. A., Fritzemeier, C. J. & Lercher, M. J. Sybil—efficient constraint-based modelling in R. BMC Syst. Biol. 7, 125 (2013). · DOI
  45. Ma, D. et al. Reliable and efficient solution of genome-scale models of metabolism and macromolecular expression. Sci. Rep. 7, 40863 (2017). · DOI
  46. Klamt, S., Saez-Rodriguez, J. & Gilles, E. D. Structural and functional analysis of cellular networks with CellNetAnalyzer. BMC Syst. Biol. 1, 2 (2007). · DOI
  47. Klamt, S. & von Kamp, A. An application programming interface for CellNetAnalyzer. Biosystems 105, 162–168 (2011). · DOI
  48. Apaolaza, I. et al. An in-silico approach to predict and exploit synthetic lethality in cancer metabolism. Nat. Commun. 8, 459 (2017). · DOI
  49. Maranas, C. D. & Zomorrodi, A. R. Optimization Methods in Metabolic Networks (Wiley, New York, 2016). · DOI
  50. Chowdhury, A., Zomorrodi, A. R. & Maranas, C. D. Bilevel optimization techniques in computational strain design. Comp. Chem. Eng. 72, 363–372 (2015). · DOI
  51. Thiele, I. et al. Multiscale modeling of metabolism and macromolecular synthesis in E. coli and its application to the evolution of codon usage. PLoS ONE 7, e45635 (2012). · DOI
  52. Feist, A. M. et al. A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol. Syst. Biol. 3, 121 (2007). · DOI
  53. Yang, L. et al. Systems biology definition of the core proteome of metabolism and expression is consistent with high-throughput data. Proc. Natl. Acad. Sci. USA 112, 10810–10815 (2015). · DOI
  54. Aurich, M. K., Fleming, R. M. T. & Thiele, I. MetaboTools: a comprehensive toolbox for analysis of genome-scale metabolic models. Front. Physiol. 7, 327 (2016). · DOI
  55. Brunk, E. et al. Recon 3D: a resource enabling a three-dimensional view of gene variation in human metabolism. Nat. Biotechnol. 36, 272–281 (2018). · DOI · Paper page
  56. Ma, D. & Saunders, M. A. Solving multiscale linear programs using the simplex method in quadruple precision. in Numerical Analysis and Optimization, Vol. 134 (eds. Al-Baali, M., Grandinetti, L. & Purnama, A.) 223–235 (Springer International Publishing, Cham, Switzerland, 2015). · DOI
  57. Fleming, R. M. T. & Thiele, I. Mass conserved elementary kinetics is sufficient for the existence of a non-equilibrium steady state concentration. J. Theor. Biol. 314, 173–181 (2012). · DOI
  58. Gevorgyan, A., Poolman, M. G. & Fell, D. A. Detection of stoichiometric inconsistencies in biomolecular models. Bioinformatics 24, 2245–2251 (2008). · DOI
  59. Feist, A. M. & Palsson, B. O. The biomass objective function. Curr. Opin. Microbiol. 13, 344–349 (2010). · DOI · Paper page
  60. Meléndez-Hevia, E. & Isidoro, A. The game of the pentose phosphate cycle. J. Theor. Biol. 117, 251–263 (1985). · DOI
  61. Orth, J. D. & Palsson, B. Ø. Systematizing the generation of missing metabolic knowledge. Biotechnol. Bioeng. 107, 403–412 (2010). · DOI
  62. Yamada, T. et al. Prediction and identification of sequences coding for orphan enzymes using genomic and metagenomic neighbours. Mol. Syst. Biol. 8, 581 (2012). · DOI
  63. Liberal, R. & Pinney, J. W. Simple topological properties predict functional misannotations in a metabolic network. Bioinformatics 29, i154–i161 (2013). · DOI
  64. Reed, J. L. et al. Systems approach to refining genome annotation. Proc. Natl. Acad. Sci. USA 103, 17480–17484 (2006). · DOI
  65. Orth, J. D. & Palsson, B. Gap-filling analysis of the iJO1366 Escherichia coli metabolic network reconstruction for discovery of metabolic functions. BMC Syst. Biol. 6, 30 (2012). · DOI
  66. Chang, R. L. et al. Metabolic network reconstruction of Chlamydomonas offers insight into light-driven algal metabolism. Mol. Syst. Biol. 7, 518 (2011). · DOI
  67. Rolfsson, O., Palsson, B. Ø. & Thiele, I. The human metabolic reconstruction Recon 1 directs hypotheses of novel human metabolic functions. BMC Syst. Biol. 5, 155 (2011). · DOI
  68. Rolfsson, Ó., Paglia, G., Magnusdóttir, M., Palsson, B. Ø. & Thiele, I. Inferring the metabolism of human orphan metabolites from their metabolic network context affirms human gluconokinase activity. Biochem. J. 449, 427–435 (2013). · DOI
  69. Satish Kumar, V., Dasika, M. S. & Maranas, C. D. Optimization based automated curation of metabolic reconstructions. BMC Bioinformatics 8, 212 (2007). · DOI · Paper page
  70. Thiele, I., Vlassis, N. & Fleming, R. M. T. fastGapFill: efficient gap filling in metabolic networks. Bioinformatics 30, 2529–2531 (2014). · DOI
  71. Willemsen, A. M. et al. MetDFBA: incorporating time-resolved metabolomics measurements into dynamic flux balance analysis. Mol. Biosyst. 11, 137–145 (2014). · DOI
  72. Kleessen, S., Irgang, S., Klie, S., Giavalisco, P. & Nikoloski, Z. Integration of transcriptomics and metabolomics data specifies the metabolic response of Chlamydomonas to rapamycin treatment. Plant J. 81, 822–835 (2015). · DOI
  73. Bordbar, A. et al. Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics. Sci. Rep. 7, 46249 (2017). · DOI
  74. Blazier, A. S. & Papin, J. A. Integration of expression data in genome-scale metabolic network reconstructions. Front. Physiol. 3, 299 (2012). · DOI
  75. Opdam, S. et al. A systematic evaluation of methods for tailoring genome-scale metabolic models. Cell Syst. 4, 318–329.e6 (2017). · DOI
  76. Estévez, S. R. & Nikoloski, Z. Generalized framework for context-specific metabolic model extraction methods. Front. Plant Sci. 5, 491 (2014). · DOI
  77. Vlassis, N., Pacheco, M. P. & Sauter, T. Fast reconstruction of compact context-specific metabolic network models. PLoS Comput. Biol. 10, e1003424 (2014). · DOI · Paper page
  78. Becker, S. A. & Palsson, B. O. Context-specific metabolic networks are consistent with experiments. PLoS Comput. Biol. 4, e1000082 (2008). · DOI · Paper page
  79. Zur, H., Ruppin, E. & Shlomi, T. iMAT: an integrative metabolic analysis tool. Bioinformatics 26, 3140–3142 (2010). · DOI · Paper page
  80. Agren, R. et al. Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT. PLoS Comp. Biol. 8, e1002518 (2012). · DOI · Paper page
  81. Jerby, L., Shlomi, T. & Ruppin, E. Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism. Mol. Syst. Biol. 6, 401 (2010). · DOI · Paper page
  82. Wang, Y., Eddy, J. A. & Price, N. D. Reconstruction of genome-scale metabolic models for 126 human tissues using mCADRE. BMC Syst. Biol. 6, 153 (2012). · DOI
  83. Kuhar, M. J. On the use of protein turnover and half-lives. Neuropsychopharmacology 34, 1172–1173 (2008). · DOI
  84. Lajtha, A. & Sylvester, V. Handbook of Neurochemistry and Molecular Neurobiology (Springer, Boston, 2008). · DOI
  85. Schuster, S. & Hilgetag, C. On elementary flux modes in biochemical reaction systems at steady state. J. Biol. Syst. 02, 165–182 (1994). · DOI
  86. Schilling, C. H., Letscher, D. & Palsson, B. Ø. Theory for the systemic definition of metabolic pathways and their use in interpreting metabolic function from a pathway-oriented perspective. J. Theor. Biol. 203, 229–248 (2000). · DOI
  87. Klamt, S. et al. From elementary flux modes to elementary flux vectors: metabolic pathway analysis with arbitrary linear flux constraints. PLoS Comput. Biol. 13, e1005409 (2017). · DOI
  88. Bordbar, A. et al. Minimal metabolic pathway structure is consistent with associated biomolecular interactions. Mol. Syst. Biol. 10, 737 (2014). · DOI
  89. Gudmundsson, S. & Thiele, I. Computationally efficient flux variability analysis. BMC Bioinformatics 11, 489 (2010). · DOI · Paper page
  90. Haraldsdóttir, H. S., Cousins, B., Thiele, I., Fleming, R. M. T. & Vempala, S. CHRR: coordinate hit-and-run with rounding for uniform sampling of constraint-based models. Bioinformatics 33, 1741–1743 (2017). · DOI
  91. Cousins, B. & Vempala, S. Gaussian cooling and algorithms for volume and Gaussian volume. SIAM J. Comput. 47, 1237–1273 (2018). · DOI
  92. Cousins, B. & Vempala, S. A practical volume algorithm. Math. Prog. Comp. 8, 1–28 (2015).
  93. 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). · DOI · Paper page
  94. Patil, K. R., Rocha, I., Förster, J. & Nielsen, J. Evolutionary programming as a platform for in silico metabolic engineering. BMC Bioinformatics 6, 308 (2005). · DOI
  95. Lun, D. S. et al. Large-scale identification of genetic design strategies using local search. Mol. Syst. Biol. 5, 296 (2009). · DOI
  96. Ranganathan, S., Suthers, P. F. & Maranas, C. D. OptForce: an optimization procedure for identifying all genetic manipulations leading to targeted overproductions. PLoS Comput. Biol. 6, e1000744 (2010). · DOI
  97. Antoniewicz, M. R. et al. Metabolic flux analysis in a nonstationary system: fed-batch fermentation of a high yielding strain of E. coli producing 1,3-propanediol. Metab. Eng. 9, 277–292 (2007). · DOI
  98. Haraldsdóttir, H. S., Thiele, I. & Fleming, R. M. T. Comparative evaluation of open source software for mapping between metabolite identifiers in metabolic network reconstructions: application to Recon 2. J. Cheminform. 6, 2 (2014). · DOI
  99. Preciat Gonzalez, G. A. et al. Comparative evaluation of atom mapping algorithms for balanced metabolic reactions: application to Recon 3D. J. Cheminform. 9, 39 (2017). · DOI
  100. Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000). · DOI · Paper page
  101. Hastings, J. et al. The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013. Nucleic Acids Res. 41, D456–D463 (2013). · DOI · Paper page
  102. Sud, M. et al. LMSD: LIPID MAPS structure database. Nucleic Acids Res. 35, D527–D532 (2007). · DOI · Paper page
  103. Forster, M., Pick, A., Raitner, M., Schreiber, F. & Brandenburg, F. J. The system architecture of the BioPath system. In Silico Biol. 2, 415–426 (2002).
  104. Williams, A. J., Tkachenko, V., Golotvin, S., Kidd, R. & McCann, G. ChemSpider—building a foundation for the semantic web by hosting a crowd sourced databasing platform for chemistry. J. Cheminform. 2, O16 (2010). · DOI
  105. Wishart, D. S. et al. HMDB: the Human Metabolome Database. Nucleic Acids Res. 35, D521–D526 (2007). · DOI · Paper page
  106. Rahman, S. A. et al. Reaction Decoder Tool (RDT): extracting features from chemical reactions. Bioinformatics 32, 2065–2066 (2016). · DOI
  107. Kumar, A. & Maranas, C. D. CLCA: maximum common molecular substructure queries within the MetRxn Database. J. Chem. Inf. Model. 54, 3417–3438 (2014). · DOI
  108. Shimizu, Y., Hattori, M., Goto, S. & Kanehisa, M. Generalized reaction patterns for prediction of unknown enzymatic reactions. Genome Inform. 20, 149–158 (2008).
  109. Haraldsdóttir, H. S. & Fleming, R. M. T. Identification of conserved moieties in metabolic networks by graph theoretical analysis of atom transition networks. PLoS Comput. Biol. 12, e1004999 (2016). · DOI
  110. Klamt, S., Haus, U.-U. & Theis, F. Hypergraphs and cellular networks. PLoS Comput. Biol. 5, e1000385 (2009). · DOI · Paper page
  111. Fleming, R. M. T. & Thiele, I. von Bertalanffy 1.0: a COBRA toolbox extension to thermodynamically constrain metabolic models. Bioinformatics 27, 142–143 (2011). · DOI
  112. Fleming, R. M. T., 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). · DOI
  113. Haraldsdóttir, H. S., Thiele, I. & Fleming, R. M. T. Quantitative assignment of reaction directionality in a multicompartmental human metabolic reconstruction. Biophys. J. 102, 1703–1711 (2012). · DOI
  114. Noor, E., Haraldsdóttir, H. S., Milo, R. & Fleming, R. M. T. Consistent estimation of Gibbs energy using component contributions. PLoS Comput. Biol. 9, e1003098 (2013). · DOI · Paper page
  115. Fleming, R. M. T., Maes, C. M., Saunders, M. A., Ye, Y. & Palsson, B. Ø. A variational principle for computing nonequilibrium fluxes and potentials in genome-scale biochemical networks. J. Theor. Biol. 292, 71–77 (2012). · DOI
  116. Beard, D. A., Liang, S.-D. & Qian, H. Energy balance for analysis of complex metabolic networks. Biophys. J. 83, 79–86 (2002). · DOI
  117. Qian, H. & Beard, D. A. Thermodynamics of stoichiometric biochemical networks in living systems far from equilibrium. Biophys. Chem. 114, 213–220 (2005). · DOI
  118. Fleming, R. M. T., Thiele, I., Provan, G. & Nasheuer, H. P. Integrated stoichiometric, thermo- dynamic and kinetic modelling of steady state metabolism. J. Theor. Biol. 264, 683–692 (2010). · DOI
  119. Schellenberger, J., Lewis, N. E. & Palsson, B. Ø. Elimination of thermodynamically infeasible loops in steady-state metabolic models. Biophys. J. 100, 544–553 (2011). · DOI
  120. Soh, K. C. & Hatzimanikatis, V. Network thermodynamics in the post-genomic era. Curr. Opin. Microbiol. 13, 350–357 (2010). · DOI
  121. Fleming, R. M. T., Vlassis, N., Thiele, I. & Saunders, M. A. Conditions for duality between fluxes and concentrations in biochemical networks. J. Theor. Biol. 409, 1–10 (2016). · DOI
  122. ​Aragón Artacho, F.J., Fleming, R. M. T. & Vuong, P. T. Accelerating the DC algorithm for smooth functions. Math. Program. 169, 95–118 (2018). · DOI
  123. Artacho, F. J. A. & Fleming, R. M. T. Globally convergent algorithms for finding zeros of duplomonotone mappings. Optim. Lett. 9, 1–16 (2014).
  124. Ahookhosh, M., Aragón, F. J., Fleming, R. M. T. & Vuong, P. T. Local convergence of Levenberg-Marquardt methods under Hölder metric subregularity. Preprint at https://arxiv.org/abs/1703.07461 (2017). · arXiv
  125. King, Z. A. et al. Escher: a web application for building, sharing, and embedding data-rich visualizations of biological pathways. PLoS Comput. Biol. 11, e1004321 (2015). · DOI · Paper page
  126. Kuperstein, I. et al. NaviCell: a web-based environment for navigation, curation and maintenance of large molecular interaction maps. BMC Syst. Biol. 7, 100 (2013). · DOI
  127. Kostromins, A. & Stalidzans, E. Paint4net: COBRA Toolbox extension for visualization of stoichiometric models of metabolism. Biosystems 109, 233–239 (2012). · DOI
  128. Aurich, M. K. et al. Prediction of intracellular metabolic states from extracellular metabolomic data. Metabolomics 11, 603–619 (2014). · DOI
  129. Guebila, M. B. & Thiele, I. Model-based dietary optimization for late-stage, levodopa-treated, Parkinson’s disease patients. npj Syst. Biol. Appl. 2, 16013 (2016). · DOI
  130. Sun, Y., Fleming, R. M. T., Thiele, I. & Saunders, M. A. Robust flux balance analysis of multiscale biochemical reaction networks. BMC Bioinformatics 14, 240 (2013). · DOI
  131. 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). · DOI · Paper page
  132. Thiele, I., Fleming, R. M. T., 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). · DOI
  133. Ballerstein, K., von Kamp, A., Klamt, S. & Haus, U.-U. Minimal cut sets in a metabolic network are elementary modes in a dual network. Bioinformatics 28, 381–387 (2012). · DOI
  134. von Kamp, A. & Klamt, S. Enumeration of smallest intervention strategies in genome-scale metabolic networks. PLoS Comput. Biol. 10, e1003378 (2014). · DOI
  135. Fujita, K. A. et al. Integrating pathways of Parkinson’s disease in a molecular interaction map. Mol. Neurobiol. 49, 88–102 (2014). · DOI · Paper page
  136. Agren, R. et al. The RAVEN Toolbox and its use for generating a genome-scale metabolic model for Penicillium chrysogenum. PLoS Comput. Biol. 9, e1002980 (2013). · DOI
  137. Grafahrend-Belau, E., Klukas, C., Junker, B. H. & Schreiber, F. FBA-SimVis: interactive visualization of constraint-based metabolic models. Bioinformatics 25, 2755–2757 (2009). · DOI
  138. Rocha, I. et al. OptFlux: an open-source software platform for in silico metabolic engineering. BMC Syst. Biol. 4, 45 (2010). · DOI · Paper page
  139. Poolman, M. G. ScrumPy: metabolic modelling with Python. Syst. Biol. 153, 375–378 (2006). · DOI
  140. Hoppe, A., Hoffmann, S., Gerasch, A., Gille, C. & Holzhütter, H.-G. FASIMU: flexible software for flux-balance computation series in large metabolic networks. BMC Bioinformatics 12, 28 (2011). · DOI
  141. Boele, J., Olivier, B. G. & Teusink, B. FAME, the flux analysis and modeling environment. BMC Syst. Biol. 6, 8 (2012). · DOI

Cited by

See all 1,242

Metadata

Authors
Heirendt, L., Arreckx, S., Pfau, T., Mendoza, S. N., Richelle, A., Heinken, A., Haraldsdóttir, H. S., Wachowiak, J., Keating, S. M., Vlasov, V., Magnusdóttir, S., Ng, C. Y., Preciat, G., Žagare, A., Chan, S. H. J., Aurich, M. K., Clancy, C. M., Modamio, J., Sauls, J. T., Noronha, A., Bordbar, A., Cousins, B., El Assal, D. C., Valcarcel, L. V., Apaolaza, I., Ghaderi, S., Ahookhosh, M., Ben Guebila, M., Kostromins, A., Sompairac, N., Le, H. M., Ma, D., Sun, Y., Wang, L., Yurkovich, J. T., Oliveira, M. A. P., Vuong, P. T., El Assal, L. P., Kuperstein, I., Zinovyev, A., Hinton, H. S., Bryant, W. A., Aragón Artacho, F. J., Planes, F. J., Stalidzans, E., Maass, A., Vempala, S., Hucka, M., Saunders, M. A., Maranas, C. D., Lewis, N. E., Sauter, T., Palsson, B. Ø., Thiele, I., & Fleming, R. M. T.
Venue
Nature Protocols
Publisher
Springer Science and Business Media LLC
Published
2019-02-20
Volume
14
Issue
3
Pages
639-702
ISSN
  • 1750-2799 (ISSN-L)
  • 1754-2189
DOI
10.1038/s41596-018-0098-2
OpenAlex
W2762019694
Cited by
1,242
References
13
FWCI
40.96
Type
journal article
Language
en
Open access
green
Licence

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.