Kinetic modeling of cell metabolism for microbial production

Journal of Biotechnology

Volumn 219, Issue , 2016, Pages 126-141

  Costa, Rafael S ^a   Hartmann, Andras ^a   Vinga, Susana ^a  

^a INSTITUTO SUPERIOR TÉCNICO   (Portugal)

Author keywords

Industrial biotechnology; Kinetic modeling of metabolism; Strain improvement; Systems biology; Systems metabolic engineering

Indexed keywords

BIOLOGICAL SYSTEMS; COMPLEX NETWORKS; DIFFERENTIAL EQUATIONS; KINETIC PARAMETERS; KINETIC THEORY; KINETICS; METABOLISM; ORDINARY DIFFERENTIAL EQUATIONS; PHYSIOLOGY;

INDUSTRIAL BIOTECHNOLOGY; KINETIC MODELING; STRAIN IMPROVEMENT; SYSTEMS BIOLOGY; SYSTEMS METABOLIC ENGINEERINGS;

METABOLIC ENGINEERING;

BACTERIAL STRAIN; BIOTECHNOLOGY; CELL KINETICS; CELL METABOLISM; COMPUTER PROGRAM; MATHEMATICAL MODEL; METABOLIC ENGINEERING; MICROBIAL GROWTH; NONHUMAN; PRIORITY JOURNAL; REVIEW; SYSTEMS BIOLOGY; BACTERIUM; BIOLOGICAL MODEL; FUNGUS; KINETICS; METABOLISM; PROCEDURES; SOFTWARE;

BACTERIA; FUNGI; KINETICS; METABOLIC ENGINEERING; MODELS, BIOLOGICAL; SOFTWARE; SYSTEMS BIOLOGY;

EID: 84952802704     PISSN: 01681656     EISSN: 18734863     Source Type: Journal    
DOI: 10.1016/j.jbiotec.2015.12.023     Document Type: Review

References (200)

1. Apweiler R., Martin M.J., et al. Update on activities at the Universal Protein Resource (UniProt) in 2013. Nucleic Acids Res. 2013, 41:D43-D47.
2. Audoly S., Bellu G., D'Angio L., Saccomani M.P., Cobelli C. Global identifiability of nonlinear models of biological systems. IEEE Trans. Biomed. Eng. 2001, 48:55-65.
3. Bairoch A. The ENZYME database in 2000. Nucleic Acids Res. 2000, 28:304-305.
4. Balsa-Canto E., Alonso A.A., Banga J.R. An iterative identification procedure for dynamic modeling of biochemical networks. BMC Syst. Biol. 2010, 4:11.
5. Balsa-Canto E., Banga J.R. AMIGO, a toolbox for advanced model identification in systems biology using global optimization. Bioinformatics 2011, 27:2311-2313.
6. Balsa-Canto E., Peifer M., Banga J.R., Timmer J., Fleck C. Hybrid optimization method with general switching strategy for parameter estimation. BMC Syst. Biol. 2008, 2:26.
7. Banga J.R., Balsa-Canto E. Parameter estimation and optimal experimental design. Essays Biochem. 2008, 45:195-210.
8. Bar-Even A., Noor E., Savir Y., Liebermeister W., Davidi D., Tawfik D.S., Milo R. The moderately efficient enzyme: evolutionary and physicochemical trends shaping enzyme parameters. Biochemistry 2011, 50:4402-4410.
9. Barrett T., Troup D.B., Wilhite S.E., Ledoux P., Evangelista C., Kim I.F., Tomashevsky M., Marshall K.A., Phillippy K.H., Sherman P.M., Muertter R.N., Holko M., Ayanbule O., Yefanov A., Soboleva A. NCBI GEO: archive for functional genomics data sets-10 years on. Nucleic Acids Res. 2011, 39:D1005-D1010.
10. Bassingthwaighte J., Butterworth E., Jardine B., Raymond G., Neal M. JSim, an open-source modeling system for data analysis and reproducibility in research. Faseb J. 2014, 28.
11. Bergmann F.T., Adams R., Moodie S., Cooper J., Glont M., Golebiewski M., Hucka M., Laibe C., Miller A.K., Nickerson D.P., Olivier B.G., Rodriguez N., Sauro H.M., Scharm M., Soiland-Reyes S., Waltemath D., Yvon F., Le Novere N. COMBINE archive and OMEX format: one file to share all information to reproduce a modeling project. BMC Bioinf. 2014, 15:369.
12. Biegler L.T., Damiano J.J., Blau G.E. Nonlinear parameter-estimation-a case-study comparison. AIChE J. 1986, 32:29-45.
13. Brazma A., Hingamp P., Quackenbush J., Sherlock G., Spellman P., Stoeckert C., Aach J., Ansorge W., Ball C.A., Causton H.C., Gaasterland T., Glenisson P., Holstege F.C.P., Kim I.F., Markowitz V., Matese J.C., Parkinson H., Robinson A., Sarkans U., Schulze-Kremer S., Stewart J., Taylor R., Vilo J., Vingron M. Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat. Genet. 2001, 29:365-371.
14. Brazma A., Krestyaninova M., Sarkans U. Standards for systems biology. Nat. Rev. Genet. 2006, 7:593-605.
15. Buchel F., Rodriguez N., Swainston N., Wrzodek C., Czauderna T., Keller R., Mittag F., Schubert M., Glont M., Golebiewski M., van Iersel M., Keating S., Rall M., Wybrow M., Hermjakob H., Hucka M., Kell D.B., Muller W., Mendes P., Zell A., Chaouiya C., Saez-Rodriguez J., Schreiber F., Laibe C., Drager A., Le Novere N. Path2Models: large-scale generation of computational models from biochemical pathway maps. BMC Syst. Biol. 2013, 7:116.
16. Bulik S., Grimbs S., Huthmacher C., Selbig J., Holzhuetter H.G. Kinetic hybrid models composed of mechanistic and simplified enzymatic rate laws-a promising method for speeding up the kinetic modelling of complex metabolic networks. FEBS J. 2009, 276:410-424.
17. Caspi R., Foerster H., Fulcher C.A., Kaipa P., Krummenacker M., Latendresse M., Paley S., Rhee S.Y., Shearer A.G., Tissier C., Walk T.C., Zhang P., Karp P.D. The metaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res. 2008, 36:D623-D631.
18. Chakrabarti A., Miskovic L., Soh K.C., Hatzimanikatis V. Towards kinetic modeling of genome-scale metabolic networks without sacrificing stoichiometric, thermodynamic and physiological constraints. Biotechnol. J. 2013, 8:1043-1057.
19. Chance B. The kinetics of the enzyme-substrate compound of peroxidase. J. Biol. Chem. 1943, 151:553-577.
20. Chassagnole C., Noisommit-Rizzi N., Schmid J.W., Mauch K., Reuss M. Dynamic modeling of the central carbon metabolism of E. coli. Biotechnol. Bioeng. 2002, 79:53-73.
21. Chassagnole C., Quentin E., Fell D.A., de Atauri P., Mazat J.P. Dynamic simulation of pollutant effects on the threonine pathway in E. coli. C. R. Biol. 2003, 326:501-508.
22. Chemler J.A., Koffas M.A.G. Metabolic engineering for plant natural product biosynthesis in microbes. Curr. Opin. Biotechnol. 2008, 19:597-605.
23. Chen N., Koumpouras G.C., Polizzi K.M., Kontoravdi C. Genome-based kinetic modeling of cytosolic glucose metabolism in industrially relevant cell lines: Saccharomyces cerevisiae and Chinese hamster ovary cells. Bioprocess Biosyst. Eng. 2012, 35:1023-1033.
24. Chou I.C., Voit E.O. Recent developments in parameter estimation and structure identification of biochemical and genomic systems. Math. Biosci. 2009, 219:57-83.
25. Chowdhury A., Zomorrodi A.R., Maranas C.D. k-OptForce: integrating kinetics with flux balance analysis for strain design. PLoS Comput. Biol. 2014, 10:e1003487.
26. Cintolesi A., Clomburg J.M., Rigou V., Zygourakis K., Gonzalez R. Quantitative analysis of the fermentative metabolism of glycerol in E. coli. Biotechnol. Bioeng. 2012, 109:187-198.
27. Contador C.A., Rizk M.L., Asenjo J.A., Liao J.C. Ensemble modeling for strain development of L-lysine-producing E. coli. Metab. Eng. 2009, 11:221-233.
28. Costa R.S., Machado D., Rocha I., Ferreira E.C. Critical perspective on the consequences of the limited availability of kinetic data in metabolic dynamic modeling. IET Syst. Biol. 2011, 5:157-163.
29. Costa R.S., Hartmann A., Gaspar P., Neves A.R., Vinga S. An extended dynamic model of Lactococcus lactis metabolism for mannitol and 2,3-butanediol production. Mol. Biosyst. 2014, 10:628-639.
30. Costa R.S., Machado D., Rocha I., Ferreira E.C. Hybrid dynamic modeling of E. coli central metabolic network combining Michaelis-Menten and approximate kinetic equations. Biosystems 2010, 100:150-157.
31. Costa R.S., Verissimo A., Vinga S. KiMoSys: a web-based repository of experimental data for kinetic models of biological SYStems. BMC Syst. Biol. 2014, 8. 85.
32. Cotten C., Reed J.L. Mechanistic analysis of multi-omics datasets to generate kinetic parameters for constraint-based metabolic models. BMC Bioinf. 2013, 14.
33. Courtot M., Juty N., Knupfer C., Waltemath D., Zhukova A., Drager A., Dumontier M., Finney A., Golebiewski M., Hastings J., Hoops S., Keating S., Kell D.B., Kerrien S., Lawson J., Lister A., Lu J., Machne R., Mendes P., Pocock M., Rodriguez N., Villeger A., Wilkinson D.J., Wimalaratne S., Laibe C., Hucka M., Le Novere N. Controlled vocabularies and semantics in systems biology. Mol. Syst. Biol. 2011, 7:543.
34. Covert M., Xiao N., Chen T.J., Karr J.R. Integrating metabolic, transcriptional regulatory and signal transduction models in E. coli. Bioinformatics 2009, 24:2044-2050.
35. Cronwright G.R., Rohwer J.M., Prior B.A. Metabolic control analysis of glycerol synthesis in Saccharomyces cerevisiae. Appl. Environ. Microbiol. 2002, 68:4448-4456.
36. Dada J.O., Mendes P. Design and architecture of web services for simulation of biochemical systems. In Data Integration in the Life Sciences 6th International Workshop, DILS 2009 2009.
37. Daniels B.C., Chen Y.J., Sethna J.P., Gutenkunst R.N., Myers C.R. Sloppiness, robustness, and evolvability in systems biology. Curr. Opin. Biotechnol. 2008, 19:389-395.
38. de Groot M.J.L., Prathumpai W., Visser J., Ruijter G.J.G. Metabolic control analysis of Aspergillus niger l-arabinose catabolism. Biotechnol. Prog. 2005, 21:1610-1616.
39. Degenring D., Froemel C., Dikta G., Takors R. Sensitivity analysis for the reduction of complex metabolism models. J. Process Control 2004, 14:729-745.
40. Degtyarenko K., De Matos P., Ennis M., Hastings J., Zbinden M., McNaught A., Alcantara R., Darsow M., Guedj M., Ashburner M. ChEBI: a database and ontology for chemical entities of biological interest. Nucleic Acids Res. 2008, 36:D344-D350.
41. Demin O., Goryanin I. Kinetic modelling in systems biology, Chapman & Hall/CRC. Math. Comput. Biol. Ser. 2009.
42. Demir E., Cary M.P., et al. 2010 The BioPAX community standard for pathway data sharing (vol 28, pg 935, 2010). Nat. Biotechnol. 2010, 28:1308.
43. Doerr A., Keller R., Zell A., Draeger A. SBML simulator: a Java tool for model simulation and parameter estimation in systems biology. Computation 2014, 2:246-257.
44. Draeger A., Hassis N., Supper J., Schroder A., Zell A. SBMLsqueezer: a CellDesigner plug-in to generate kinetic rate equations for biochemical networks. BMC Syst. Biol. 2008, 2:39.
45. Drager A., Zielinski D.C., Keller R., Rall M., Eichner J., Palsson B.O., Zell A. SBMLsqueezer 2: context-sensitive creation of kinetic equations in biochemical networks. BMC Syst. Biol. 2015, 9:68.
46. Edwards J.S., Palsson B.O. Metabolic flux balance analysis and the in silico analysis of E. coli K-12 gene deletions. BMC Bioinf. 2000, 1:1-10.
47. Erdrich P., Steuer R., Klamt S. An algorithm for the reduction of genome-scale metabolic network models to meaningful core models. BMC Syst. Biol. 2015, 9:48.
48. Fell D. Understanding the Ccontrol of Metabolism 1997, Portland Press, London.
49. Floettmann M., Uhlendorf J., Scharp T., Klipp E., Spiesser T.W. SensA: web-based sensitivity analysis of SBML models. Bioinformatics 2014, 30:2830-2831.
50. Funahashi A., Tanimura N., Morohashi M., Kitano H. CellDesigner: a process diagram editor for gene-regulatory and biochemical networks. Biosilico 2003, 1:159-162.
51. Gizzatkulov N.M., Goryanin I.I., Metelkin E.A., Mogilevskaya E.A., Peskov K.V., Demin O.V. DBSolve Optimum: a software package for kinetic modeling which allows dynamic visualization of simulation results. BMC Syst. Biol. 2010, 4:109.
52. Goel A., Santos F., de Vos W.M., Teusink B., Molenaar D. Standardized assay medium to measure lactococcus lactis enzyme activities while mimicking intracellular conditions. Appl. Environ. Microbiol. 2012, 78:134-143.
53. Goldberg R.N., Tewari Y.B., Bhat T.N. Thermodynamics of enzyme-catalyzed reactions-a database for quantitative biochemistry. Bioinformatics 2004, 20:2874-2877.
54. Gombert A.K., Nielsen J. Mathematical modelling of metabolism. Curr. Opin. Biotechnol. 2000, 11:180-186.
55. Gutenkunst R.N., Waterfall J.J., Casey F.P., Brown K.S., Myers C.R., Sethna J.P. Universally sloppy parameter sensitivities in systems biology models. PLoS Comput. Biol. 2007, 3:e189.
56. Haug K., Salek R.M., Conesa P., Hastings J., De Matos P., Rijnbeek M., Mahendraker T., Williams M., Neumann S., Rocca-Serra P., Maguire E., Gonzalez-Beltran A., Sansone S.A., Griffin J.L., Steinbeck C. MetaboLights-an open-access general-purpose repository for metabolomics studies and associated meta-data. Nucleic Acids Res. 2013, 41:D781-D786.
57. Heijnen J.J. Approximative kinetic formats used in metabolic network modeling. Biotechnol. Bioeng. 2005, 91:534-545.
58. Heinzle E., Matsuda F., Miyagawa H., Wakasa K., Nishioka T. Estimation of metabolic fluxes, expression levels and metabolite dynamics of a secondary metabolic pathway in potato using label pulse-feeding experiments combined with kinetic network modelling and simulation. Plant J. 2007, 50:176-187.
59. Hengl S., Kruetz C., Timmer J., Maiwald T. Data-based identifiability analysis of non-linear dynamical models. Bioinformatics 2007, 23:2612-2618.
60. Hirmajer T., Balsa-Canto E., Banga J.R. DOTcvpSB, a software toolbox for dynamic optimization in systems biology. BMC Bioinf. 2009, 10:199.
61. Hoefnagel M.H.N., Starrenburg M.J.C., Martens D.E., Hugenholtz J., Kleerebezem M., Van Swam I.I., Bongers R., Westerhoff H.V., Snoep J.L. Metabolic engineering of lactic acid bacteria, the combined approach: kinetic modelling, metabolic control and experimental analysis. Microbiology 2002, 148:1003-1013.
62. Hoops S., Sahle S., Gauges R., Lee C., Pahle J., Simus N., Singhhal M., Xu L., Mendes P., Kummer U. COPASI-a complex pathway simulator. Bioinformatics 2006, 22:3067-3074.
63. Horn F., Jackson R. General mass action kinetics. Arch. Rational Mech. Anal. 1972, 47:81-170.
64. Huang Z.M., Mou L.K., Shen Q.C., Lu S.Y., Li C.G., Liu X.Y., Wang G.Q., Li S., Geng L., Liu Y.Q., Wu J.W., Chen G.Q., Zhang J. ASD v2.0: updated content and novel features focusing on allosteric regulation. Nucleic Acids Res. 2014, 42:D510-D516.
65. Hucka M., Fineey A., Sauro H.M., et al. The systems biology markup language (SBML): a medium for representation and exchange of biochemical network modelssystems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 2003, 19:524-531.
66. Hucka M., Nickerson D.P., Bader G.D., Bergman F.T., Cooper J., Demir E., Garny A., Golebiewski M., Myers C.J., Schreiber F., Waltemath D., Le Novere N. Promoting coordinated development of community-based information standards for modeling in biology: the COMBINE initiative. Front. Bioeng. Biotechnol. 2015, 3:1-6.
67. Inoue K., Maeda K., Miyabe T., Matsuoka Y., Kurata H. ADLIVE toolbox for MATLAB: automatic dynamic modeling of biochemical networks with comprehensive system analysis. Bioprocess Biosyst. Eng. 2014, 37:1925-1927.
68. Jamshidi N., Palsson B.O. Formulating genome-scale kinetic models in the post-genome era. Mol. Syst. Biol. 2008, 4(171).
69. Jamshidi N., Palsson B.O. Mass action stoichiometric simulation models: incorporating kinetics and regulation into stoichiometric models. Biophys. J. 2010, 98:175-185.
70. Kacser H., Burns J.A. The control of flux. Symp. Soc. Exp. Biol. 1973, 27:65-104.
71. Kacser H., Burns J.A., Fell D.A. The control of flux. Biochem. Soc. Trans. 1995, 23:341-366.
72. Kaltenbach H.M., Dimopoulos S., Stelling J. Systems analysis of cellular networks under uncertainty. FEBS Lett. 2009, 583:3923-3930.
73. Kameswaran S., Biegler L.T. Simultaneous dynamic optimization strategies: recent advances and challenges. Comput. Chem. Eng. 2006, 30:1560-1575.
74. Kameswaran S., Biegler L.T. Advantages of nonlinear-programming-based methodologies for inequality path-constrained optimal control problems-A numerical study. Siam J. Sci. Comput. 2008, 30:957-981.
75. Kanehisa M., Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000, 28:27-30.
76. Karp P., Riley M., Saier M., Paulsen I.T., Paley S.M., Pellegrini-Toole A. The EcoCyc and MetaCyc databases. Nucleic Acids Res. 2000, 28:56-59.
77. Karr J.R., Phillips N.C., Covert M.W. WholeCellSimDB: a hybrid relational/HDF database for whole-cell model predictions. Database 2014, 2014. bau095.
78. Karr J.R., Sanghvi J.C., Macklin D.N., Gutschow M.V., Jacobs J.M., Bolival B., Assad-Garcia N., Glass J.I., Covert M.W. A whoLe-cell computational model predicts phenotype from genotype. Cell 2012, 150:389-401.
79. Karr J.R., Williams A.H., Zucker J.D., Raue A., Steiert B., Timmer J., Clemens Kreutz, DREAM8 parameter estimation challenge Consortium, Wilkinson S., Allgood B.A., Bot B.M., Hoff B.R., Kellen M.R., Covert M.W., Stolovitzky G.A., Meyer P. Summary of the DREAM8 parameter estimation challenge: toward parameter identification for whole-cell models. PLoS Comput. Biol. 2015, 11:e1004096.
80. Katsuyama Y., Matsuzawa M., Funa N., Horinouch S. Production of curcuminoids by E. coli carrying an artificial biosynthesis pathway. Microbiol.-Sgm 2008, 154:2620-2628.
81. Khodayari A., Zomorrodi A.R., Liao J.C., Maranas C.D. A kinetic model of E. coli core metabolism satisfying multiple sets of mutant flux data. Metab. Eng. 2014, 25:50-62. 9617.
82. Kiselev I., Kolpakov F. BioUML: plugin for population-based modeling. Virtual Biol. 2014, 2:7-15.
83. Klipp E., Nordlander B., Kruger R., Gennemark P., Hohmann S. Integrative model of the response of yeast to osmotic shock. Nat. Biotechnol. 2005, 23:975-982.
84. Kotte O., Zaugg J.B., Heinemann M. Bacterial adaptation through distributed sensing of metabolic fluxes. Mol. Syst. Biol. 2010, 6(355).
85. Kronfeld M., Planatscher H., Zell A. The EvA2 optimization framework. Learn. Intell. Optim. 2010, 6073:247-250.
86. Le Novère N., Courtot M., Laibe C. Adding semantics in kinetic models of biochemical pathways. Proceedings of the 2nd International Symposium on experimental standard conditions of enzyme characterizations 2007.
87. Le Novere N., Finney A., Hucka M., Bhalla U.S., Campagne F., Collado-Vides J., Crampin E.J., Halstead M., Klipp E., Mendes P., Nielsen P., Sauro H., Shapiro B., Snoep J.L., Spence H.D., Wanner B.L. Minimum information requested in the annotation of biochemical models (MIRIAM) mum information requested in the annotation of biochemical models (MIRIAM). Nat. Biotechnol. 2005, 23:1509-1515.
88. Le Novere N., Hucka M., Mi H.Y., Moodie S., Schreiber F., Sorokin A., Demir E., Wegner K., Aladjem M.I., Wimalaratne S.M., Bergman F.T., Gauges R., Ghazal P., Kawaji H., Li L., Matsuoka Y., Villeger A., Boyd S.E., Calzone L., Courtot M., Dogrusoz U., Freeman T.C., Funahashi A., Ghosh S., Jouraku A., Kim S., Kolpakov F., Luna A., Sahle S., Schmidt E., Watterson S., Wu G.M., Goryanin I., Kell D.B., Sander C., Sauro H., Snoep J.L., Kohn K., Kitano H. The systems biology graphical notation. Nat. Biotechnol. 2009, 27:735-741.
89. Lee D.Y., Yun C., Cho A., Hou B.K., Park S., Lee S.Y. WebCell: a web-based environment for kinetic modeling and dynamic simulation of cellular networks. Bioinformatics 2006, 22:1150-1151.
90. Lee F.C., Rangaiah G.P., Lee D.Y. Modeling and optimization of a multi-product biosynthesis factory for multiple objectives. Metab. Eng. 2010, 12:251-267.
91. Lee J.M., Gianchandani E.P., Eddy J.A., Papin J.A. Dynamic analysis of integrated signaling, metabolic, and regulatory networks. PLoS Comput. Biol. 2008, 4:e1000086.
92. Leppavuori J.T., Domach M.M., Biegler L.T. Parameter estimation in batch bioreactor simulation using metabolic models: sequential solution with direct sensitivities. Ind. Eng. Chem. Res. 2011, 50:12080-12091.
93. Li C., Donizelli M., Rodriguez N., Dharuri H., Endler L., Chelliah V., Li L., He E.U., Henry A., Stefan M.I., Snoep J.L., Hucka M., Le Novere N., Laibe C. BioModels Database: an enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst. Biol. 2010, 4:92.
94. Li P., Dada J.O., Jameson D., Spasic I., Swainston N., Carroll K., Dunn W., Khan F., Malys N., Messiha H.L., Simeonidis E., Weichart D., Winder C., Wishart J., Broomhead D.S., Goble C.A., Gaskell S.J., Kell D.B., Westerhoff H.V., Mendes P., Paton N.W. Systematic integration of experimental data and models in systems biology. BMC Bioinf. 2010, 11:582.
95. Li R.D., Li Y.Y., Lu L.Y., Ren C., Li Y.X., Liu L. An improved kinetic model for the acetone-butanol-ethanol pathway of Clostridium acetobutylicum and model-based perturbation analysis. BMC Syst. Biol. 2011, 5:S12.
96. Liebermeister W., Klipp E. Biochemical networks with uncertain parameters. Iee Proc. Syst. Biol. 2005, 152:97-107.
97. Liebermeister W., Klipp E. Bringing metabolic networks to life: convenience rate law and thermodynamic constraints. Theor. Biol. Med. Model. 2006, 3:41-54.
98. Liebermeister W., Uhlendorf J., Klipp E. Modular rate laws for enzymatic reactions: thermodynamics, elasticities and implementation. Bioinformatics 2010, 26:1528-1534.
99. Link H., Kochanowski K., Sauer U. Systematic identification of allosteric protein-metabolite interactions that control enzyme activity in vivo. Nat. Biotechnol. 2013, 31:357.
100. Lloyd C.M., Halstead M.D.B., Nielsen P.F. CeIIML: its future, present and past. Prog. Biophys. Mol. Biol. 2004, 85:433-450.
101. Loew L.M., Schaff J.C. The Virtual Cell: a software environment for computational cell biology. Trends Biotechnol. 2001, 19:401-406.
102. Loew L.M., Schaff J.C. The virtual cell: a software environment for computational cell biology. Trends Biotechnol. 2001, 19:401-406.
103. Machado D., Costa R.S., Ferreira E.C., Rocha I., Tidor B. Exploring the gap between dynamic and constraint-based models of metabolism. Metab. Eng. 2012, 14:112-119.
104. Machado D., Rodrigues L.R., Rocha I. A kinetic model for curcumin production in E. coli. Biosystems 2014, 125:16-21.
105. Maerkl S.J., Quake S.R. A systems approach to measuring the binding energy landscapes of transcription factors. Science 2007, 315:233-237.
106. Magnus J.B., Hollwedel D., Oldiges M., Takors R. Monitoring and modeling of the reaction dynamics in the valine/leucine synthesis pathway in Corynebacterium glutamicum. Biotechnol. Prog. 2006, 22:1071-1083.
107. Mahadevan R., Edwards J.S., Doyle F.J. Dynamic flux balance analysis of diauxic growth in E. coli. Biophys. J.l 2002, 83:1331-1340.
108. Maiwald T., Timmer J. Dynamical modeling and multi-experiment fitting with PottersWheel. Bioinformatics 2008, 24:2037-2043.
109. Marchisio M.A., Stelling J. Computational design tools for synthetic biology. Curr. Opin. Biotechnol. 2009, 20:479-485.
110. Marienhagen J., Bott M. Metabolic engineering of microorganisms for the synthesis of plant natural products. J. Biotechnol. 2013, 163:166-178.
111. Medema M.H., van Raaphorst R., Takano E., Breitling R. Computational tools for the synthetic design of biochemical pathways. Nat. Rev. Microbiol. 2012, 10:191-202.
112. Mi H.Y., Muruganujan A., Demir E., Matsuoka Y., Funahashi A., Kitano H., Thomas P.D. BioPAX support in CellDesigner. Bioinformatics 2011, 27:3437-3438.
113. Milo R., Jorgensen P., Moran U., Weber G., Springer M. BioNumbers-the database of key numbers in molecular and cell biology. Nucleic Acids Res. 2010, 38:D750-D753.
114. Minton A.P. How can biochemical reactions within cells differ from those in test tubes?. J. Cell Sci. 2006, 119:2863-2869.
115. Miskovic L., Hatzimanikatis V. Production of biofuels and biochemicals: in need of an ORACLE. Trends Biotechnol. 2010, 28:391-397.
116. Moles C.G., Mendes P., Banga J.R. Parameter estimation in biochemical pathways: a comparison of global optimization methods. Genome Res. 2003, 13:2467-2474.
117. Moreno-Sanchez R., Saavedra E., Rodriguez-Enriquez S., Olin-Sandoval V. Metabolic control analysis: a tool for designing strategies to manipulate metabolic pathways. J. Biomed. Biotechnol. 2002, 2008:597913.
118. Murabito E., Verma M., Bekker M., Bellomo D., Westerhoff H.V., Steuer R. Monte-Carlo modeling of the central carbon metabolism of Lactococcus lactis. Insights into metabolic regulation. PLoL One 2014, 9:e106453.
119. Nikerel I.E., van Winden W.A., van Gulik W.M., Heijnen J.J. A method for estimation of elasticities in metabolic networks using steady-state and dynamic metabolomics data and lin-log kinetics. BMC Bioinf. 2006, 7:540-563.
120. Nikerel I.E., van Winden W.A., van Gulik W.M., Heijnen J.J. A method for estimation of elasticities in metabolic networks using steady-state and dynamic metabolomics data and linlog kinetics. BMC Bioinf. 2006, 7:540-563.
121. Nikerel I.E., van Winden W.A., Verheijen P.J.T., Heijnen J.J. Model reduction and a priori kinetic parameter identifiability analysis using metabolome time series for metabolic reaction networks with linlog kinetics. Metab. Eng. 2009, 11:20-30.
122. Nikolaev E.V. The elucidation of metabolic pathways and their improvements using stable optimization of large-scale kinetic models of cellular systems. Metab. Eng. 2010, 12:26-38.
123. Nishio Y., Usada Y., Matsui K., Kurata H. Computer-aided rational design of the phosphotransferase system for enhanced glucose uptake in E. coli. Mol. Syst. Biol. 2008, 4:160-172.
124. Noor E., Bar-Even A., Flamholz A., Lubling Y., Davidi D., Milo R. An integrated open framework for thermodynamics of reactions that combines accuracy and coverage. Bioinformatics 2012, 28:2037-2044.
125. Oddone G.M., Mills D.A., Block D.E. A dynamic, genome-scale flux model of Lactococcus lactis to increase specific recombinant protein expression. Metab. Eng. 2009, 11:367-381.
126. Oh E., Lu M., Park C., Oh H.B., Lee S.Y., Lee J. Dynamic modelling of lactic acid fermentation metabolism with Lactococcus lactis. J. Microbiol. Biotechnol. 2011, 21:162-169.
127. Olivier B.G., Rohwer J.M., Hofmeyr J.H.S. Modelling cellular systems with PySCeS. Bioinformatics 2005, 21:560-561.
128. Olivier B.G., Snoep J.L. Web-based kinetic modelling using JWS online. Bioinformatics 2004, 20:2143-2144.
129. Oshiro M., Shinto H., Tashiro Y., Miwa N., Sekiguchi T., Okamoto M., Ishizaki A., Sonomoto K. Kinetic modeling and sensitivity analysis of xylose metabolism in Lactococcus lactis IO-1. J. Biosci. Bioeng. 2009, 108:376-384.
130. Palsson B.O. Properties of Reconstructed Networks 2006, Cambridge University, New York.
131. Palsson B.O., Lightfoot E.N. Mathematical-modeling of dynamics and control in metabolic networks. 1. on Michaelis-Menten kinetics. J. Theor. Biol. 1984, 111:273-302.
132. Parachin N.S., Bergdahl B., Van Niel E.W.J., Gorwa-Grauslund M.F. Kinetic modelling reveals current limitations in the production of ethanol from xylose by recombinant Saccharomyces cerevisiae. Metab. Eng. 2011, 13:508-517.
133. Parkinson H., Sarkans U., Kolesnikov N., Abeygunawardena N., Burdett T., Dylag M., Emam I., Farne A., Hastings E., Holloway E., Kurbatova N., Lukk M., Malone J., Mani R., Pilicheva E., Rustici G., Sharma A., Williams E., Adamusiak T., Brandizi M., Sklyar N., Brazma A. ArrayExpress update-an archive of microarray and high-throughput sequencing-based functional genomics experiments. Nucleic Acids Res. 2011, 39:D1002-D1004.
134. Polisetty P.K., Gatzke E.P., Voit E.O. Yield optimization of regulated metabolic systems using deterministic branch-and-reduce methods. Biotechnol. Bioeng. 2008, 99:1154-1169.
135. Pozo C., Guillen-Gosalbez G., Sorribas A., Jimenez L. A spatial branch-and-bound framework for the global optimization of kinetic models of metabolic networks. Ind. Eng. Chem. Res. 2011, 50:5225-5238.
136. Prathumpai W., Gabelgaard J.B., Wanchanthuek P., van de Vondervoort P.J.I., de Groot M.J.L., McIntyre M., Nielsen J. Metabolic control analysis of xylose catabolism in Aspergillus. Biotechnol. Prog. 2003, 19:1136-1141.
137. Price N.D., Reed J.L., Palsson B.O. Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat. Rev. Microbiol. 2004, 2:886-897.
138. Raue A., Schilling M., Bachmann J., Matteson A., Schelke M., Kaschek D., Hug S., Kreutz C., Harms B.D., Theis F.J., Klingmuller U., Timmer J. Lessons learned from quantitative dynamical modeling in systems biology. PLoS One 2013, 8:e74335.
139. Resendis-Antonio O. Filling kinetics gaps: Dynamic Modeling of metabolism where detailed kinetic information is lacking. PLoS One 2009, 4:e4967.
140. Rizk M.L., Liao J.C. Ensemble modeling for aromatic production in E. coli. PLoS One 2009, 4:e6903.
141. Rocca-Serra P., Brandizi M., Maguire E., Sklyar N., Taylor C., Begley K., Field D., Harris S., Hide W., Hofmann O., Neumann S., Sterk P., Tong W.D., Sansone S.A. ISA software suite: supporting standards-compliant experimental annotation and enabling curation at the community level. Bioinformatics 2010, 26:2354-2356.
142. Rodriguez-Fernandez M., Banga J.R. SensSB: a software toolbox for the development and sensitivity analysis of systems biology models. Bioinformatics 2010, 26:1675-1676.
143. Salgado H., Peralta-Gil M., Gama-Castro S., Santos-Zavaleta A., Muniz-Rascado L., Garcia-Sotelo J.S., Weiss V., Solano-Lira H., Martinez-Flores I., Medina-Rivera A., Salgado-Osorio G., Alquicira-Hernandez S., Alquicira-Hernandez K., Lopez-Fuentes A., Porron-Sotelo L., Huerta A.M., Bonavides-Martinez C., Balderas-Martinez Y.I., Pannier L., Olvera M., Labastida A., Jimenez-Jacinto V., Vega-Alvarado L., del Moral-Chavez V., Hernandez-Alvarez A., Morett E., Collado-Vides J. RegulonDB v8.0: omics data sets, evolutionary conservation, regulatory phrases, cross-validated gold standards and more. Nucleic Acids Res. 2013, 41:D203-D213.
144. Sansone S.A., Fan T., Goodacre R., Griffin J.L., Hardy N.W., Kaddurah-Daouk R., Kristal B.S., Lindon J., Mendes P., Morrison N., Nikolau B., Robertson D., Sumner L.W., Taylor C., van der Werf M., van Ommen B., Fiehn O. The metabolomics standards initiative. Nat. Biotechnol. 2007, 25:844-848.
145. Sansone S.A., Rocca-Serra P., Field D., Maguire E., Taylor C., Hofmann O., Fang H., Neumann S., Tong W.D., Amaral-Zettler L., Begley K., Booth T., Bougueleret L., Burns G., Chapman B., Clark T., Coleman L.A., Copeland J., Das S., de Daruvar A., De Matos P., Dix I., Edmunds S., Evelo C.T., Forster M.J., Gaudet P., Gilbert J., Goble C., Griffin J.L., Jacob D., Kleinjans J., Harland L., Haug K., Hermjakob H., Sui S.J.H., Laederach A., Liang S.G., Marshall S., McGrath A., Merrill E., Reilly D., Roux M., Shamu C.E., Shang C.A., Steinbeck C., Trefethen A., Williams-Jones B., Wolstencroft K., Xenarios I., Hide W. Toward interoperable bioscience data. Nat. Genet. 2012, 44:121-126.
146. Sauro H.M., Hucka M., Finney A., Wellock C., Bolouri H., Doyle J., Kitano H. Next generation simulation tools: the systems biology workbench and BioSPICE integration. Omics-J. Integr. Biol. 2004, 7:355-372.
147. Savageau M.A., Voit E.O. Power-law approach to model biochemical systems I. Ferment. Technol. 1982, 60:221-228.
148. Sayers E.W., Barrett T., Benson D.A., Bolton E., Bryant S.H., Canese K., Chetvernin V., Church D.M., DiCuccio M., Federhen S., Feolo M., Fingerman I.M., Geer L.Y., Helmberg W., Kapustin Y., Landsman D., Lipman D.J., Lu Z.Y., Madden T.L., Madej T., Maglott D.R., Marchler-Bauer A., Miller V., Mizrachi I., Ostell J., Panchenko A., Phan L., Pruitt K.D., Schuler G.D., Sequeira E., Sherry S.T., Shumway M., Sirotkin K., Slotta D., Souvorov A., Starchenko G., Tatusova T.A., Wagner L., Wang Y.L., Wilbur W.J., Yaschenko E., Ye J.A. Database resources of the national center for biotechnology information. Nucleic Acids Res. 2011, 39:D38-D51.
149. Schellenberger J., Park J.O., Conrad T.M., Palsson B.O. BiGG: a biochemical genetic and genomic knowledgebase of large scale metabolic reconstructions. BMC Bioinf. 2010, 11:213.
150. Schmid J.W., Mauch K., Reuss M., Gilles E.D., Kremling A. Metabolic design based on a coupled gene expression-metabolic network model of tryptophan production in E. coli. Metab. Eng. 2004, 6:364-377.
151. Schmidt H., Jirstrand M. Systems biology toolbox for MATLAB: a computational platform for research in systems biology. Bioinformatics 2006, 22:514-515.
152. Schmidt H., Madsen M.F., Dano S., Cedersund G. Complexity reduction of biochemical rate expressions. Bioinformatics 2008, 24:848-854.
153. Schnell S., Turner T.E. Reaction kinetics in intracellular environments with macromolecular crowding: simulations and rate laws. Prog. Biophys. Mol. Biol. 2004, 85:235-260.
154. Schomburg I., Chang A., Schomburg D. BRENDA, enzyme data and metabolic information. Nucleic Acids Res. 2002, 30:47-49.
155. Sedoglavic A. A probabilistic algorithm to test local algebraic observability in polynomial time. J. Symb. Comput. 2002, 33:735-755.
156. Shinto H., Tashiro Y., Yamashita M., Kobayashi G., Sekiguchi T., Hanai T., Kuriya Y., Okamoto M., Sonomoto K. Kinetic modeling and sensitivity analysis of acetone-butanol-ethanol production. J. Biotechnol. 2007, 131:45-56.
157. Shuler M.L., Leung S., Dick C.C. A Mathematical model for the growth of a single bacterial cell. Ann. N. Y. Acad. Sci. 1979, 326:35-52.
158. Smallbone K., Messiha H.L., Carroll K.M., Winder C.L., Malys N., Dunn W.B., Murabito E., Swainston N., Dada J.O., Khan F., Pir P., Simeonidis E., Spasic I., Wishart J., Weichart D., Hayes N.W., Jameson D., Broomhead D.S., Oliver S.G., Gaskell S.J., McCarthy J.E.G., Paton N.W., Westerhoff H.V., Kell D.B., Mendes P. A model of yeast glycolysis based on a consistent kinetic characterisation of all its enzymes. FEBS Lett. 2013, 587:2832-2841.
159. Smallbone K., Messiha H.L., Carroll K.M., Winder C.L., Malys N., Dunn W.B., Murabito E., Swainston N., Dada J.O., Khan F., Pir P., Simeonidis E., Spasic I., Wishart J., Weichart D., Hayes N.W., Jameson D., Broomhead D.S., Oliver S.G., Gaskell S.J., McCarthy J.E.G., Paton N.W., Westerhoff H.V., Kell D.B., Mendes P. A model of yeast glycolysis based on a consistent kinetic characterisation of all its enzymes. FEBS Lett. 2013, 587:2832-2841.
160. Smallbone K., Simeonidis E., Broomhead D.S., Kell D.B. Something from nothing-bridging the gap between constraint-based and kinetic modelling. FEBS J. 2007, 274:5576-5585.
161. Smallbone K., Simeonidis E., Swainston N., Mendes P. Towards a genome-scale kinetic model of cellular metabolism. BMC Syst. Biol. 2010, 4:6.
162. Snoep J.L. The silicon cell initiative: working towards a detailed kinetic description at the cellular level. Curr. Opin. Biotechnol. 2005, 16:336-343.
163. Somogyi E.T., Bouteiler J.-M., Glazier J.A., Konig M., Medley J.K., Swat M.H., Sauro H.M. libRoadRunner: a high performance SBML simulation and analysis library. Bioinformatics 2015.
164. Sorribas A., Hernandez-Bermejo B., Vilaprinyo E., Alves R. Cooperativity and saturation in biochemica networks: a saturable using taylor series approximations. Biotechnol. Bioeng. 2007, 97:1259-1277.
165. Stanford N.J., Lubitz T., Smallbone K., Klipp E., Mendes P., Liebermeister W. Systematic construction of kinetic models from genome-scale metabolic networks. PLoS One 2013, 8:e79195.
166. Steuer R. Computational approaches to the topology, stability and dynamics of metabolic networks. Phytochemistry 2007, 68:2139-2151.
167. Steuer R., Gross T., Selbig J., Blasius B. Structural kinetic modeling of metabolic networks. Proc. Nat. Acad. Sci. U. S. A. 2006, 103:11868-11873.
168. Sundararaj S., Guo A., Habibi-Nazhad B., Rouani M., Stothard P., Ellison M., Wishart D.S. The CyberCell Database (CCDB): a comprehensive, self-updating, relational database to coordinate and facilitate in silico modeling of E. coli CyberCell Database (CCDB): a comprehensive, self-updating, relational database to coordinate and facilitate in silico modeling of E. coli. Nucleic Acids Res. 2004, 32:D293-D295.
169. Sunnaker M., Zamora-Sillero E., Dechant R., Ludwig C., Busetto A.G., Wagner A., Stelling J. Automatic generation of predictive dynamic models reveals nuclear phosphorylation as the key Msn2 control mechanism. Sci. Signal. 2013, 6:ra41.
170. Takahashi K., Ishikawa N., Sadamoto Y., Sasamoto H., Ohta S., Shiozawa A., Miyoshi F., Naito Y., Nakayama Y., Tomita M. E-cell 2: multi-platform E-Cell simulation system. Bioinformatics 2003, 19:1727-1729.
171. Tan Y., Rivera J.G.L., Contador C.A., Asenjo J.A., Liao J.C. Reducing the allowable kinetic space by constructing ensemble of dynamic models with the same steady-state flux. Metab. Eng. 2011, 13:60-75.
172. Taylor C.F., Field D., Sansone S.A., Aerts J., Apweiler R., Ashburner M., Ball C.A., Binz P.A., Bogue M., Booth T., Brazma A., Brinkman R.R., Clark A.M., Deutsch E.W., Fiehn O., Fostel J., Ghazal P., Gibson F., Gray T., Grimes G., Hancock J.M., Hardy N.W., Hermjakob H., Julian R.K., Kane M., Kettner C., Kinsinger C., Kolker E., Kuiper M., Le Novere N., Leebens-Mack J., Lewis S.E., Lord P., Mallon A.M., Marthandan N., Masuya H., McNally R., Mehrle A., Morrison N., Orchard S., Quackenbush J., Reecy J.M., Robertson D.G., Rocca-Serra P., Rodriguez H., Rosenfelder H., Santoyo-Lopez J., Scheuermann R.H., Schober D., Smith B., Snape J., Stoeckert C.J., Tipton K., Sterk P., Untergasser A., Vandesompele J., Wiemann S. Promoting coherent minimum reporting guidelines for biological and biomedical investigations: the MIBBI project. Nat. Biotechnol. 2008, 26:889-896.
173. Taylor C.F., Paton N.W., Lilley K.S., Binz P.A., Julian R.K., Jones A.R., Zhu W.M., Apweiler R., Aebersold R., Deutsch E.W., Dunn M.J., Heck A.J.R., Leitner A., Macht M., Mann M., Martens L., Neubert T.A., Patterson S.D., Ping P.P., Seymour S.L., Souda P., Tsugita A., Vandekerckhove J., Vondriska T.M., Whitelegge J.P., Wilkins M.R., Xenarios I., Yates J.R., Hermjakob H. The minimum information about a proteomics experiment (MIAPE) minimum information about a proteomics experiment (MIAPE). Nat. Biotechnol. 2007, 25:887-893.
174. Teusink B., Passarge J., Reijenga C.A., Esgalhado E., van der Weijden C.C., Schepper M., Walsh M.C., Bakker B.M., van Dam K., Westerhoff H.V., Snoep J.L. Can yeast glycolysis be understood in terms of in vitro kinetics of the constituent enzymes? Testing biochemistryyeast glycolysis be understood in terms of in vitro kinetics of the constituent enzymes? Testing biochemistry. Eur. J. Biochem. 2000, 267:5313-5329.
175. Tipton K.F., Armstrong R.N., Bakker B.M., Bairoch A., Cornish-Bowden A., Halling P.J., Hofmeyr J.H., Leyh T.S., Kettner C., Raushel F.M., Rohwer J., Schomburg D., Steinbeck C. Standards for reporting enzyme data: the STRENDA consortium: what it aims to do and why it should be helpful. Perspect. Sci. 2014, 1:131-137.
176. Tomita M., Hashimoto K., Takahashi K., Shimizu T.S., Matsuzaki Y., Miyoshi F., Saito K., Tanida S., Yugi K., Venter J.C., et al. E-CELL: software environment for whole-cell simulation. Bioinformatics 1999, 15:72-84.
177. Torella J.P., Chait R., Kishony R. Optimal drug synergy in antimicrobial treatments. PLoS Comput. Biol. 2010, 6:e1000796.
178. Tran L.M., Rizk M.L., Liao J.C. Ensemble modeling of metabolic networks. Biophys. J. 2008, 95:5606-5617.
179. van Eunen K., Bouwman J., Daran-Lapujade P., et al. Measuring enzyme activities under standardized in vivo-like conditionsfor systems biology. FEBS J. 2010, 277:749-760.
180. van Eunen K., Kiewiet J.A.L., Westerhoff H.V., Bakker B.M. Testing biochemistry revisited: how in vivo metabolism can be understood from in vitro enzyme kinetics. PLoS Comput. Biol. 2012, 8:e1002483.
181. van Iersel M.P., Villeger A.C., Czauderna T., Boyd S.E., Bergmann F.T., Luna A., Demir E., Sorokin A., Dogrusoz U., Matsuoka Y., Funahashi A., Aladjem M.I., Mi H.Y., Moodie S.L., Kitano H., Le Novere N., Schreiber F. Software support for SBGN maps: SBGN-ML and LibSBGN. Bioinformatics 2012, 28:2016-2021.
182. van Riel N.A.W. Dynamic modelling and analysis of biochemical networks: mechanism-based models and model-based experiments. Brief. Bioinform. 2006, 7:364-374.
183. Varma A., Palsson B.O. Metabolic flux balancing: basic concepts, scientific and practical use. Nat. Biotechnol. 1994, 12:994-998.
184. Varner J.D. Large-scale prediction of phenotype: concept. Biotechnol. Bioeng. 2000, 69:664-678.
185. Vaseghi S., Baumeister A., Rizzi M., Reuss M. In vivo dynamics of the pentose phosphate pathway in Saccharomyces cerevisae. Metab. Eng. 1999, 1:128-140.
186. Vastrik I., D'Eustachio P., Schmidt E., Gopinath G., Croft D., de Bono B., Gillespie M., Jassal B., Lewis S., Matthews L., Wu G., Birney E., Stein L. Reactome: a knowledge base of biologic pathways and processes. Genome Biol. 2007, 8:R39.
187. Visser D., Schmid J.W., Mauch K., Reuss M., Heijnen J.J. Optimal re-design of primary metabolism in E. coli using linlog kinetics. Metab. Eng. 2004, 6:378-390.
188. Vital-Lopez F.G., Armaou A., Nikolaev E.V., Maranas C.D. A computational procedure for optimal engineering interventations using kinetic models of metabolism. Am. Chem. Soc. Am. Inst. Chem. Eng. 2006, 22:1507-1517.
189. Vital-Lopez F.G., Wallqvist A., Reifman J. Bridging the gap between gene expression and metabolic phenotype via kinetic models. BMC Syst. Biol. 2013, 7:63.
190. Vizcaino J.A., Cote R.G., Csordas A., Dianes J.A., Fabregat A., Foster J.M., Griss J., Alpi E., Birim M., Contell J., O'Kelly G., Schoenegger A., Ovelleiro D., Perez-Riverol Y., Reisinger F., Rios D., Wang R., Hermjakob H. The Proteomics Identifications (PRIDE) database and associated tools: status in 2013. Nucleic Acids Res. 2013, 41:D1063-D1069.
191. Waltemath D., Adams R., Beard D.A., Bergmann F.T., Bhalla U.S., Britten R., Chelliah V., Cooling M.T., Cooper J., Crampin E.J., Garny A., Hoops S., Hucka M., Hunter P., Klipp E., Laibe C., Miller A.K., Moraru I., Nickerson D., Nielsen P., Nikolski M., Sahle S., Sauro H.M., Schmidt H., Snoep J.L., Tolle D., Wolkenhauer O., Le Novere N. Minimum information about a simulation experiment (MIASE). PLoS Comput. Biol. 2011, 7:e1001122.
192. Waltemath D., Adams R., Bergmann F.T., Hucka M., Kolpakov F., Miller A.K., Moraru I.I., Nickerson D., Sahle S., Snoep J.L., Le Novere N. Reproducible computational biology experiments with SED-ML-The simulation experiment description markup language. BMC Syst. Biol. 2011, 5:198.
193. Weaver L.J., Sousa M.M.L., Wang G., Baidoo E., Petzold C.J., Keasling J.D. A kinetic-based approach to understanding heterologous mevalonate pathway function in E. Coli. Biotechnol. Bioeng. 2015, 112:111-119.
194. Willemsen A.M., Hendrickx D.M., Hoefsloot H.C.J., Hendriks M.M.W.B., Wahl S.A., Teusink B., Smilde A.K., van Kampen A.H.C. MetDFBA: incorporating time-resolved metabolomics measurements into dynamic flux balance analysis. Mol. Biosyst. 2015, 11:137-145.
195. Wishart D.S., Tzur D., Knox C., Eisner R., Guo A.C., Young N., Cheng D., Jewell K., Arndt D., Sawhney S., Fung C., Nikolai L., Lewis M., Coutouly M.A., Forsythe I., Tang P., Shrivastava S., Jeroncic K., Stothard P., Amegbey G., Block D., Hau D.D., Wagner J., Miniaci J., Clements M., Gebremedhin M., Guo N., Zhang Y., Duggan G.E., MacInnis G.D., Weljie A.M., Dowlatabadi R., Bamforth F., Clive D., Greiner R., Li L., Marrie T., Sykes B.D., Vogel H.J., Querengesser L. HMDB: the human metabolome database. Nucleic Acids Res. 2007, 35:D521-D526.
196. Wittig U., Golebiewski M., Kania R., Krebs O., Mir S., Weidemann A., Ansteins S., Saric J., Rojas I. SABIO-RK: Integration and curation of reaction kinetic data. Lecture Notes Bioinf. 2006, 4075:94-103.
197. Yu T., Lloyd C.M., Nickerson D.P., Cooling M.T., Miller A.K., Garny A., Terkildsen J.R., Lawson J., Britten R.D., Hunter P.J., Nielsen P.M.F. The Physiome model repository 2. Bioinformatics 2011, 27:743-744.
198. Yugi K., Nakayama Y., Kinoshita A., Tomita M. Hybrid dynamic/static method for large-scale simulation of metabolism. Theor. Biol. Med. Model. 2005, 2:42-53.
199. Zhang Z., Shen T., Rui B., Zhou W., Zhou X., Shang C., Xin C., Liu X., Li G., Jiang J., Li C., Li R., Han M., You S., Yu G., Yi Y., Wen H., Liu Z., Xie X. CeCaFDB: a curated database for the documentation, visualization and comparative analysis of central carbon metabolic flux distributions explored by 13C-fluxomics. Nucleic Acids Res. 2014, 11.
200. Zi Z., Klipp E. SBML-PET: a systems biology markup language-based parameter estimation tool. Bioinformatics 2006, 22:2704-2705.