Dynamic metabolic models in context: biomass backtracking

Integrative Biology

Volumn 7, Issue 8, 2015, Pages 940-951

  Tummler, Katja ^a   Kühn, Clemens ^a   Klipp, Edda ^a  

^a HUMBOLDT UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ARTICLE; BASIC RESEARCH; BIOMASS; DRUG DEVELOPMENT; DYNAMICS; GENOME; INFORMATION; METABOLISM; NONHUMAN; PRIORITY JOURNAL; STOICHIOMETRY; ALGORITHM; ANIMAL; BIOLOGICAL MODEL; CHROMOSOMAL MAPPING; COMPUTER SIMULATION; HUMAN; PHYSIOLOGY; PROCEDURES; SIGNAL TRANSDUCTION; WORKFLOW;

PROTEOME;

ALGORITHMS; ANIMALS; CHROMOSOME MAPPING; COMPUTER SIMULATION; HUMANS; METABOLIC NETWORKS AND PATHWAYS; MODELS, BIOLOGICAL; PROTEOME; SIGNAL TRANSDUCTION; WORKFLOW;

EID: 84938697313     PISSN: 17579694     EISSN: 17579708     Source Type: Journal    
DOI: 10.1039/c5ib00050e     Document Type: Article

References (54)

1. J. Carrera et al., An integrative, multi-scale, genome-wide model reveals the phenotypic landscape of Escherichia coli Mol. Syst. Biol. 2014 10 735
2. J. A. H. Wodke et al., Dissecting the energy metabolism in Mycoplasma pneumoniae through genome-scale metabolic modeling Mol. Syst. Biol. 2013 9 653
3. E. Petelenz-Kurdziel et al., Quantitative Analysis of Glycerol Accumulation, Glycolysis and Growth under Hyper Osmotic Stress PLoS Comput. Biol. 2013 9 e1003084
4. O. Warburg On the Origin of Cancer Cells Science 1956 123 309 314
5. J. Keasling Manufacturing molecules through metabolic engineering Science 2010 330 1355 1358
6. E. Klipp B. Nordlander R. Krüger P. Gennemark S. Hohmann Integrative model of the response of yeast to osmotic shock Nat. Biotechnol. 2005 23 975 982
7. J. H. Van Heerden et al., Lost in transition: start-up of glycolysis yields subpopulations of nongrowing cells Science 2014 343 1245114
8. F. Hynne S. Danø P. G. Sørensen Full-scale model of glycolysis in Saccharomyces cerevisiae Biophys. Chem. 2001 94 121 163
9. C. Chassagnole N. Noisommit-Rizzi J. W. Schmid K. Mauch M. Reuss Dynamic modeling of the central carbon metabolism of Escherichia coli Biotechnol. Bioeng. 2002 79 53 73
10. B. Teusink et al., Can yeast glycolysis be understood in terms of in vitro kinetics of the constituent enzymes? Testing biochemistry Eur. J. Biochem. 2000 267 5313 5329
11. F. Achcar E. J. Kerkhoven B. M. Bakker M. P. Barrett R. Breitling Dynamic modelling under uncertainty: the case of Trypanosoma brucei energy metabolism PLoS Comput. Biol. 2012 8 e1002352
12. V. K. Singh I. Ghosh Kinetic modeling of tricarboxylic acid cycle and glyoxylate bypass in Mycobacterium tuberculosis, and its application to assessment of drug targets Theor. Biol. Med. Modell. 2006 3 27
13. M. C. Reed et al., A mathematical model of glutathione metabolism Theor. Biol. Med. Modell. 2008 5 8
14. H. L. Messiha E. Kent N. Malys K. M. Carroll N. Swainston P. Mendes K. Smallbone Enzyme characterisation and kinetic modelling of the pentose phosphate pathway in yeast PeerJ PrePrints 2014 10.7287/peerj.preprints.146v4
15. A. M. Feist M. J. Herrgård I. Thiele J. L. Reed B. Ø. Palsson Reconstruction of biochemical networks in microorganisms Nat. Rev. Microbiol. 2009 7 129 143
16. J. D. Orth I. Thiele B. Ø. Palsson What is flux balance analysis? Nat. Biotechnol. 2010 28 245 248
17. A. Bordbar J. M. Monk Z. A. King B. O. Palsson Constraint-based models predict metabolic and associated cellular functions Nat. Rev. Genet. 2014 15 107 120
18. R. Mahadevan J. S. Edwards F. J. Doyle Dynamic flux balance analysis of diauxic growth in Escherichia coli Biophys. J. 2002 83 1331 1340
19. A. M. Willemsen et al., MetDFBA: incorporating time-resolved metabolomics measurements into dynamic flux balance analysis Mol. BioSyst. 2014 11 137 145
20. A. Chowdhury R. A. Zomorrodi D. C. Maranas k-OptForce: integrating kinetics with flux balance analysis for strain design PLoS Comput. Biol. 2014 10 e1003487
21. N. J. Stanford et al., Systematic reconstruction of kinetic models from genome-scale metabolic networks PLoS One 2013 8 e79195
22. P. Jouhten M. Wiebe M. Penttilä Dynamic flux balance analysis of the metabolism of Saccharomyces cerevisiae during the shift from fully respirative or respirofermentative metabolic states to anaerobiosis FEBS J. 2012 279 3338 3354
23. V. Chelliah C. Laibe N. Le Novère BioModels Database: A Repository of Mathematical Models of Biological Processes Methods Mol. Biol. 2013 1021 189 199
24. M. Hucka et al., The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models Bioinformatics 2003 19 524 531
25. S. A. Becker et al., Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox Nat. Protoc. 2007 2 727 738
26. M. S. Keating J. B. Bornstein A. Finney M. Hucka SBMLToolbox: an SBML toolbox for MATLAB users Bioinformatics 2006 22 1275 1277
27. J. Hastings et al., The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013 Nucleic Acids Res. 2013 41 D456 63
28. H. Ogata et al., KEGG: Kyoto Encyclopedia of Genes and Genomes Nucleic Acids Res. 1999 27 29 34
29. U. R. Ibarra S. J. Edwards O. B. Palsson Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth Nature 2002 420 186 189
30. R. Schuetz N. Zamboni M. Zampieri M. Heinemann U. Sauer Multidimensional optimality of microbial metabolism Science 2012 336 601 604
31. E. N. Lewis et al., Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models Mol. Syst. Biol. 2010 6 390
32. R. Mahadevan H. C. Schilling The effects of alternate optimal solutions in constraint-based genome-scale metabolic models Metab. Eng. 2003 5 264 276
33. W. H. Aung S. A. Henry P. L. Walker Revising the Representation of Fatty Acid, Glycerolipid, and Glycerophospholipid Metabolism in the Consensus Model of Yeast Metabolism Ind. Biotechnol. 2013 9 215 228
34. U. Schulze, Anaerobic Physiology of Saccharomyces Cerevisiae, 1995
35. K. Van Eunen J. A. Kiewiet H. V. Westerhoff B. M. Bakker Testing biochemistry revisited: how in vivo metabolism can be understood from in vitro enzyme kinetics PLoS Comput. Biol. 2012 8 e1002483
36. D. J. Orth et al., A comprehensive genome-scale reconstruction of Escherichia coli metabolism-2011 Mol. Syst. Biol. 2011 7 535
37. S. Hoops et al., COPASI-a COmplex PAthway SImulator Bioinformatics 2006 22 3067 3074
38. P. Jouhten et al., Oxygen dependence of metabolic fluxes and energy generation of Saccharomyces cerevisiae CEN.PK113-1A BMC Syst. Biol. 2008 2 60
39. T. C. Archer et al., The genome sequence of E. coli W (ATCC 9637): comparative genome analysis and an improved genome-scale reconstruction of E. coli BMC Genomics 2011 12 9
40. N. Jamshidi B. Ø. Palsson Investigating the metabolic capabilities of Mycobacterium tuberculosis H37Rv using the in silico strain iNJ661 and proposing alternative drug targets BMC Syst. Biol. 2007 1 26
41. S. C. Henry F. J. Zinner P. M. Cohoon L. R. Stevens iBsu1103: a new genome-scale metabolic model of Bacillus subtilis based on SEED annotations Genome Biol. 2009 10 R69
42. J. Anderson C. Y. Chang A. Papachristodoulou Model decomposition and reduction tools for large-scale networks in systems biology Automatica 2011 47 1165 1174
43. O. Radulescu N. A. Gorban A. Zinovyev V. Noel Reduction of dynamical biochemical reactions networks in computational biology Front. Genet. 2012 3 131
44. S. Rao A. van der Schaft K. van Eunen M. B. Bakker B. Jayawardhana A model reduction method for biochemical reaction networks BMC Syst. Biol. 2014 8 52
45. C. Colijn et al., Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production PLoS Comput. Biol. 2009 5 e1000489
46. W. M. Covert B. Ø. Palsson Transcriptional regulation in constraints-based metabolic models of Escherichia coli J. Biol. Chem. 2002 277 28058 28064
47. K. Yizhak T. Benyamini W. Liebermeister E. Ruppin T. Shlomi Integrating quantitative proteomics and metabolomics with a genome-scale metabolic network model Bioinformatics 2010 26 i255 i260
48. A. Hoppe S. Hoffmann H.-G. Holzhütter Including metabolite concentrations into flux balance analysis: thermodynamic realizability as a constraint on flux distributions in metabolic networks BMC Syst. Biol. 2007 1 23
49. A. Kümmel S. Panke M. Heinemann Putative regulatory sites unraveled by network-embedded thermodynamic analysis of metabolome data Mol. Syst. Biol. 2006 2 2006.0034
50. E. N. Lewis H. Nagarajan O. B. Palsson Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods Nat. Rev. Microbiol. 2012 10 291 305
51. R. J. Karr et al., A Whole-Cell Computational Model Predicts Phenotype from Genotype Cell 2012 150 389 401
52. M. J. Lee J. Min Lee P. E. Gianchandani A. J. Eddy A. J. Papin Dynamic analysis of integrated signaling, metabolic, and regulatory networks PLoS Comput. Biol. 2008 4 e1000086
53. E. Klipp C. R. Wade U. Kummer Biochemical network-based drug-target prediction Curr. Opin. Biotechnol. 2010 21 511 516
54. M. Schulz M. B. Bakker E. Klipp TIde: a software for the systematic scanning of drug targets in kinetic network models BMC Bioinf. 2009 10 344