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Dynamic Behavior of Regulatory Elements in the Hierarchical Regulatory Network of Various Carbon Sources-Grown Escherichia coli  

Lee, Sung-Gun (Department of Chemical Engineering, College of Engineering, Pusan National University)
Hwang, Kyu-Suk (Department of Chemical Engineering, College of Engineering, Pusan National University)
Kim, Cheol-Min (Department of Biochemistry, College of Medicine, Medical Research Institute, Pusan National University)
Publication Information
Journal of Microbiology and Biotechnology / v.15, no.3, 2005 , pp. 551-559 More about this Journal
Abstract
The recent rapid increase in genomic data related to many microorganisms and the development of computational tools to accurately analyze large amounts of data have enabled us to design several kinds of simulation approaches for the complex behaviors of cells. Among these approaches, dFBA (dynamic flux balance analysis), which utilizes FBA, differential equations, and regulatory events, has correctly predicted cellular behaviors under given environmental conditions. However, until now, dFBA has centered on substrate concentration, cell growth, and gene on/off, but a detailed hierarchical structure of a regulatory network has not been taken into account. The use of Boolean rules for regulatory events in dFBA has limited the representation of interactions between specific regulatory proteins and genes and the whole transcriptional regulation mechanism with environmental change. In this paper, we adopted the operon as the basic structure, constructed a hierarchical structure for a regulatory network with defined fundamental symbols, and introduced a weight between symbols in order to solve the above problems. Finally, the total control mechanism of regulatory elements (operons, genes, effectors, etc.) with time was simulated through the linkage of dFBA with regulatory network modeling. The lac operon, trp operon, and tna operon in the central metabolic network of E. coli were chosen as the basic models for control patterns. The suggested modeling method in this study can be adopted as a basic framework to describe other transcriptional regulations, and provide biologists and engineers with useful information on transcriptional regulation mechanisms under extracellular environmental change.
Keywords
Dynamic flux balance analysis; operon; transcriptional regulation;
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1 Cotter, P. A. and R. P. Gunsalus. 1992. Contribution of the Fnr and ArcA gene products in coordinate regulation of cytochrome o and d oxidase (cyoABCDE and cydAB) genes in Escherichia coli. FEMS Microbiol. Lett. 70: 31-36   PUBMED
2 Feng, G. and C. Yanofsky. 2001. Reproducing tna operon regulation in vitro in an S-30 system. J. Biol. Chem. 276: 1974-1983   DOI
3 Hong, S. H., S. Y. Moon, and S. Y. Lee. 2003. Prediction of maximum yields of metabolites and optimal pathways for their production by metabolic flux analysis. J. Microbiol. Biotechnol. 13: 571-577
4 Kazarinoff, M. N. and E. E. Snell, 1977. Essential arginine residues in tryptophanase from Escherichia coli. J. Biol. Chem. 252: 7598-7602   PUBMED
5 Lee, S. Y. and Papoutsakis. 1999. Metabolic Flux Balance Analysis, pp. 13-57. Marcel Dekker, New York, U.S.A
6 Overbeek, R., M. Fonstein, M. D'Souza, G. D. Pusch, and N. Maltsev. 1999. The use of gene clusters to infer functional coupling. Proc. Natl. Acad. Sci. USA 96: 2896-2901
7 Saier, M. H. J., T. M. Ramseier, and J. Reiszer. 1996. Regulation of carbon utilization, pp. 1325-1343. In F. C. Neidhardt (ed.), Escherichia coli and Salmonella: Cellular and Molecular Biology, vol. 1. ASM Press, Washington, D.C
8 Varma, A. and B. O. Palsson. 1994. Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild type Escherichia coli W3110. Appl. Environ. Microbol. 60: 3724-3731
9 Varma, A. and B. O. Palsson. 1994. Metabolic flux balancing: Basic concepts, scientific and practical use. Nat. Biotech. 12: 994-998   DOI   ScienceOn
10 Konan, V. K. and C. Yanofsky. 2000. Rho-dependent transcription termination in the tna operon of Escherichia coli: Roles of the boxA sequence and the rut site. J. Bacteriol. 182: 3981-3988   DOI   ScienceOn
11 Covert, M. W., E. M. Knight, J. L. Reed, M. J. Herrgard, and B. O. Palsson. 2004. Integrating high-throughput and computational data elucidates bacterial networks. Nature 429: 92-96   DOI   ScienceOn
12 Zheng, Y., J. D. Szustakowski, L. Fortnow, R. J. Roberts, and S. Kasif. 2002. Computational identification of operons in microbial genomes. Genome Res. 12: 1221-1230
13 Mahadevan, R., J. S. Edwards, and J. D. Francis. 2002. Dynamic flux balance analysis of diauxic growth in Escherichia coli. Biophys. J. 83: 1331-1340   DOI   ScienceOn
14 Stephanopoulos, G. N., A. A. Aristidou, and J. Nielsen. 1998. Metabolic Engineering: Principles and Methodologies, pp. 180-193. Academic Press, London, U.K
15 Tamames, J., G. Casari, C. Ouzounis, and A. Valencia. 1997. Conserved cluster of functionally related genes in two bacterial genomes. J. Mol. Evol. 44: 66-73   DOI   ScienceOn
16 Adhya, S. 1996. The lac and gal operons today, pp. 181-200. In E. C. C. Lin and A. Simon Lynch (eds.), Regulation of Gene Expression in Escherichia coli. Chapman & Hall, New York, U.S.A
17 Ettema, T., J. van der Oost, and M. Huynen. 2001. Modularity in the gain microbial genomes. Trends Genet. 17: 485-487   DOI   ScienceOn
18 Lee, J. W., A. Goel, M. M. Ataai, and M. M. Domach. 2002. Flux regulation patterns and energy audit of E. coli B/r and K-12. J. Microbiol. Biotechnol. 12: 248-258
19 Lee, T. H., M. Y. Kim, Y. W. Ryu, and J. H. Seo. 2001. Estimation of theoretical yield for ethanol production from D-xylose by recombinant Saccharomyces cerevisiae using metabolic pathway synthesis algorithm. J. Microbiol. Biotechnol. 11: 384-388
20 Meyers, S. and P. Friedland. 1984. Knowledge-based simulation of genetic regulation in bacteriophage lambda. Nucleic Acids Res. 12: 1-9   DOI   PUBMED   ScienceOn
21 Shen-Orr, S. S., R. Milo, S. Mangan, and U. Alon. 2002. Network motifs in the transcriptional regulation network of Escherichia coli. Nat. Genet. 31: 64-68   DOI   ScienceOn
22 Covert, M. W., C. H. Schilling, and B. O. Palsson. 2001. Regulation of gene expression in flux balance models of metabolism. J. Theor. Biol. 213: 73-88   DOI   ScienceOn
23 Covert, M. W. and B. O. Palsson. 2002. Transcriptional regulation in constraints-based metabolic models of Escherichia coli. J. Biol. Chem. 277: 28058-28064   DOI   ScienceOn
24 Huerta, A. M., H. Salgado, D. Thieffry, and J. Collado-Vides. 1998. RegulonDB: A database on transcriptional regulation in Escherichia coli. Nucleic Acids Res. 26: 55-59   DOI   ScienceOn
25 Yada, T., M. Nakao, Y. Totoki, and K. Nakai. 1999. Modeling and predicting transcriptional units of Escherichia coli genes using hidden Markov models. Bioinformatics 15: 987 -993   DOI   ScienceOn
26 Hong, S. H. and S. Y. Lee. 2000. Metabolic flux distribution in a metabolically engineered Escherichia coli strain producing succinic acid. J. Microbiol. Biotechnol. 10: 496-501
27 Schilling, C. H., J. S. Edwards, D. Letscher, and B. O. Palsson. 2000. Combining pathway analysis with flux balance analysis for the comprehensive study of metabolic systems. Biotechnol. Bioeng. 71: 286-306   DOI   ScienceOn
28 Varma, A., B. W. Boesch, and B. O. Palsson. 1993. Stoichiometric interpretation of Escherichia coli glucose catabolism under various oxygenation rates. Appl. Environ. Microbiol. 59: 2465-2473   PUBMED
29 Wong, P., S. Gladney, and J. D. Keasling. 1997. Mathematical model of the lac operon: Inducer exclusion, catabolite repression, and diauxic growth on glucose and lactose. Biotechnol. Progr. 13: 132-143   DOI   ScienceOn
30 Glick, B. R. and J. J. Pasternak. 1998. Molecular Biotechnology, pp. 35-38. 2nd Ed. ASM Press, Washington, DC, U.S.A
31 Karp, P. D., R. Monica, S. Milton, T. P. lan, M. P. Suzanne, and P. T. Alida. 2000. The EcoCyc and MetaCyc databases. Nucleic Acids Res. 28: 56-59   DOI   PUBMED
32 MacAdams, H. H. and L. Shapiro. 1995. Circuit simulation of genetic networks. Science 269: 650-656   DOI   PUBMED
33 Tomita, M., K. Hashimoto, K. Takahashi, T. S. Shimizu, Y. Matsuzaki, F. Miyoshi, K. Saito, S. Yugi, K. Tanida, J. C. Venter, and C. A. Hutchison III. 1999. E-Cell: Software environment for whole-cell simulation. Bioinformatics 15: 72-84   DOI   ScienceOn
34 Kremling, A., K. Bettenbrock, B. Laube, J. W. Lengeler, and E. D. Gilles. 2001. The organization of metabolic reaction networks: Application for diauxic growth on glucose and lactose. Metab. Eng. 3: 362-379   DOI   ScienceOn
35 Winston, P. H. 1992. Artificial Intelligence, pp. 119-137. 3th Ed. Addison Wesley, Massachusetts, U.S.A