Metabolic ensemble modeling for strain engineers

Biotechnology Journal

Volumn 7, Issue 3, 2012, Pages 343-353

  Tan, Yikun ^a   Liao, James C ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Dynamic model; E. coli strain engineering; Metabolic Engineering; Metabolic flux; Synthetic biology

Indexed keywords

BIOLOGICAL BEHAVIOR; E. COLI; MATHEMATICAL MODELING; METABOLIC FLUX; METABOLIC MODELS; MODEL VALIDATION; PARAMETER SCREENING; SYNTHETIC BIOLOGY; SYSTEMS BIOLOGY; TIME COURSE;

DIAGNOSIS; DYNAMIC MODELS; ESCHERICHIA COLI; METABOLISM; PARAMETER ESTIMATION; STRAIN;

METABOLIC ENGINEERING;

ENZYME;

BACTERIAL STRAIN; ENZYME KINETICS; ESCHERICHIA COLI; FERMENTATION; MATHEMATICAL PARAMETERS; METABOLIC ENGINEERING; METABOLIC ENSEMBLE MODELING; MICHAELIS CONSTANT; MOLECULAR MODEL; ORDINARY DIFFERENTIAL EQUATION; PARAMETERS; PRIORITY JOURNAL; REVIEW; STEADY STATE; SYNTHETIC BIOLOGY; SYSTEMS BIOLOGY;

COMPUTER SIMULATION; ESCHERICHIA COLI; HUMANS; METABOLIC ENGINEERING; METABOLIC NETWORKS AND PATHWAYS; MODELS, THEORETICAL; SYSTEMS BIOLOGY;

ESCHERICHIA COLI;

EID: 84857702241     PISSN: 18606768     EISSN: 18607314     Source Type: Journal    
DOI: 10.1002/biot.201100186     Document Type: Review

References (45)

1. Kohn, M. C., Achs, M. J., Garfinkel, D., Computer simulation of metabolism in pyruvate-perfused rat heart. I. Model construction. Am. J. Physiol. 1979, 237, R153-158.
2. Garfinkel, D., Kohn, M. C., Achs, M. J., Phifer, J., Roman, G. C., Construction of more reliable complex metabolic models without repeated solution of their constituent differential equations. Math. Comput. Simulation 1978, 20, 18-27.
3. Otto, M., Heinrich, R., Kühn, B., Jacobasch, G., A mathematical model for the influence of fructose 6 phosphate, ATP, potassium, ammonium and magnesium on the phosphofructokinase from rat erythrocytes. Eur. J. Biochem. 1974, 49, 169-178.
4. Rapoport, T. A., Heinrich, R., Rapoport, S. M., The regulatory principles of glycolysis in erythrocytes in vivo and in vitro. A minimal comprehensive model describing steady states, quasi-steady states and time-dependent processes. Biochem. J. 1976, 154, 449-469.
5. Shuler, M., Leung, S., Dick, C., A mathematical model for the growth of a single bacterial cell. Ann. N. Y. Acad. Sci. 1979, 326, 35-52.
6. Domach, M., Leung, S., Cahn, R., Cocks, G., Shuler, M., Computer model for glucose limited growth of a single cell of Escherichia coli B/r A. Biotechnol. Bioeng. 1984, 26, 203-216.
7. Shu, J., Shuler, M., A mathematical model for the growth of a single cell of E. coli on a glucose/glutamine/ammonium medium. Biotechnol. Bioeng. 1989, 33, 1117-1126.
8. Browning, S. T., Shuler, M. L., Towards the development of a minimal cell model by generalization of a model of Escherichia coli: use of dimensionless rate parameters. Biotechnol. Bioeng. 2001, 76, 187-192.
9. Castellanos, M., Wilson, D. B., Shuler, M. L., A modular minimal cell model: Purine and pyrimidine transport and metabolism. Proc. Natl. Acad. Sci. USA 2004, 101, 6681.
10. Teusink, B., Passarge, J., Reijenga, C. A., Esgalhado, E. 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. Theobald, U., Mailinger, W., Baltes, M., Rizzi, M., Reuss, M., In vivo analysis of metabolic dynamics in Saccharomyces cerevisiae: I. Experimental observations. Biotechnol. Bioeng. 1997, 55, 305-316.
12. Rizzi, M., Baltes, M., Theobald, U., Reuss, M., In vivo analysis of metabolic dynamics in Saccharomyces cerevisiae: II. Mathematical model. Biotechnol. Bioeng. 1997, 55, 592-608.
13. Chassagnole, C., Noisommit-Rizzi, N., Schmid, J. W., Mauch, K., Reuss, M., Dynamic modeling of the central carbon. Biotechnol. Bioeng. 2002, 79, 53-73.
14. Gneiting, T., Raftery, A. E., Weather forecasting with ensemble methods. Science 2005, 310, 248.
15. Murphy, J. M., Sexton, D. M. H., Barnett, D. N., Jones, G. S. et al., Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature 2004, 430, 768-772.
16. Lai, K., Yu, L., Wang, S., Wei, H., A novel nonlinear neural network ensemble model for financial time series forecasting. Comput. Sci. ICCS 2006 2006, 790-793.
17. Leigh, W., Purvis, R., Ragusa, J. M., Forecasting the NYSE composite index with technical analysis, pattern recognizer, neural network, and genetic algorithm: A case study in romantic decision support. Decision Support Systems 2002, 32, 361-377.
18. Taylor, J. W., Buizza, R., Using weather ensemble predictions in electricity demand forecasting. Int. J. Forecast. 2003, 19, 57-70.
19. Kuepfer, L., Peter, M., Sauer, U., Stelling, J., Ensemble modeling for analysis of cell signaling dynamics. Nat. Biotechnol. 2007, 25, 1001-1006.
20. Luan, D., Szlam, F., Tanaka, K. A., Barie, P. S., Varner, J. D., Ensembles of uncertain mathematical models can identify network response to therapeutic interventions. Mol. BioSyst. 2010, 6, 2272-2286.
21. Sachs, K., Perez, O., Pe'er, D., Lauffenburger, D. A., Nolan, G. P., Causal protein-signaling networks derived from multiparameter single-cell data. Science 2005, 308, 523.
22. Schaber, J., Flöttmann, M., Li, J., Tiger, C. F. et al., Automated ensemble modeling with modelMaGe: Analyzing feedback mechanisms in the Sho1 branch of the HOG pathway. PloS one 2011, 6, e14791.
23. Wang, L., Hatzimanikatis, V., Metabolic engineering under uncertainty. I: Framework development. Metab. Eng. 2006, 8, 133-141.
24. Contador, C. A., Rizk, M. L., Asenjo, J. A., Liao, J. C., Ensemble modeling for strain development of L-lysine-producing Escherichia coli. Metab. Eng. 2009, 11, 221-233.
25. Dean, J. T., Rizk, M. L., Tan, Y., Dipple, K. M., Liao, J. C., Ensemble modeling of hepatic fatty acid metabolism with a synthetic glyoxylate shunt. Biophys. J. 2010, 98, 1385-1395.
26. Rizk, M. L., Laguna, R., Smith, K. M., Tabita, F. R., Liao, J. C., Redox homeostasis phenotypes in RubisCO deficient Rhodobacter sphaeroides via ensemble modeling. Biotechnol. Prog. 2011, 27, 15-22.
27. Tan, Y., Rivera, J. G., 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.
28. Tran, L. M., Rizk, M. L., Liao, J. C., Ensemble modeling of metabolic networks. Biophys. J. 2008, 95, 5606-5617.
29. Dietterich, T., Ensemble methods in machine learning. Multiple classifier systems 2000, 1-15.
30. Bauer, E., Kohavi, R., An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine learning 1999, 36, 105-139.
31. Steuer, R., Gross, T., Selbig, J., Blasius, B., Structural kinetic modeling of metabolic networks. Proc. Natl. Acad. Sci. USA 2006, 103, 11868.
32. Warner, T. T., Sheu, R. S., Bowers, J. F., Sykes, R. I. et al., Ensemble simulations with coupled atmospheric dynamic and dispersion models: Illustrating uncertainties in dosage simulations.. J. Appl. Meteor. 2010, 41, 488-504.
33. Domach, M., Shuler, M., A finite representation model for an asynchronous culture of E. coli. Biotechnol. Bioeng. 1984, 26, 877-884.
34. Bennett, B. D., Kimball, E. H., Gao, M., Osterhout, R. et al., Absolute metabolite concentrations and implied enzyme active site occupancy in Escherichia coli. Nat. Chem. Biol. 2009, 5, 593-599.
35. Reinecke, M., Stephanopoulos, G., Flow injection analysis for simultaneous quantification of prolactin concentration and glycosylation macroheterogeneity in cell culture samples. Cytotechnology 2000, 34, 237-242.
36. Lange, H., Eman, M., Van Zuijlen, G., Visser, D. et al., Improved rapid sampling for in vivo kinetics of intracellular metabolites in Saccharomyces cerevisiae. Biotechnol. Bioeng. 2001, 75, 406-415.
37. Luo, B., Groenke, K., Takors, R., Wandrey, C., Oldiges, M., Simultaneous determination of multiple intracellular metabolites in glycolysis, pentose phosphate pathway and tricarboxylic acid cycle by liquid chromatography-mass spectrometry. J. Chromatogr. A 2007, 1147, 153-164.
38. 13C labeled extracts. A new method for accurate quantification of changes in concentrations of intracellular metabolites. Biotechnol. Bioeng. 2004, 85, 620-628.
39. Rizk, M. L., Liao, J. C., Ensemble modeling for aromatic production in Escherichia coli. PLoS One 2009, 4, e6903.
40. Stephanopoulos, G., Aristidou, A. A., Nielsen, J. H., Nielsen, J., Metabolic Engineering: Principles and Methodologies, Academic Press, New York 1998.
41. 13C-labeled gluconate tracing as a direct and accurate method for determining the pentose phosphate pathway split ratio in Penicillium chrysogenum. Appl. Environ. Microbiol. 2006, 72, 4743-4754.
42. De Jong, H., Modeling and simulation of genetic regulatory systems: A literature review. J. Comput. Biol. 2002, 9, 67-103.
43. Visser, D., Heijnen, J. J., Dynamic simulation and metabolic re-design of a branched pathway using linlog kinetics. Metab. Eng. 2003, 5, 164-176.
44. Nikerel, I. E., van Winden, W., van Gulik, W., Heijnen, J., A method for estimation of elasticities in metabolic networks using steady state and dynamic metabolomics data and linlog kinetics. BMC bioinformatics 2006, 7, 540.
45. Delgado, J., Liao, J. C., Inverse flux analysis for reduction of acetate excretion in Escherichia coli. Biotechnol. Prog. 1997, 13, 361-367.