Abstract
The relationship between structure, function and regulation in complex cellular networks is a still largely open question1,2,3. Systems biology aims to explain this relationship by combining experimental and theoretical approaches4. Current theories have various strengths and shortcomings in providing an integrated, predictive description of cellular networks. Specifically, dynamic mathematical modelling of large-scale networks meets difficulties because the necessary mechanistic detail and kinetic parameters are rarely available. In contrast, structure-oriented analyses only require network topology, which is well known in many cases. Previous approaches of this type focus on network robustness5 or metabolic phenotype2,6, but do not give predictions on cellular regulation. Here, we devise a theoretical method for simultaneously predicting key aspects of network functionality, robustness and gene regulation from network structure alone. This is achieved by determining and analysing the non-decomposable pathways able to operate coherently at steady state (elementary flux modes). We use the example of Escherichia coli central metabolism to illustrate the method.
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References
Dandekar, T., Schuster, S., Snel, B., Huynen, M. & Bork, P. Pathway alignment: Application to the comparative analysis of glycolytic enzymes. Biochem. J. 343, 115–124 (1999)
Edwards, J. S. & Palsson, B. O. The Escherichia coli MG1655 in silico metabolic phenotype: its definition, characteristics and capabilities. Proc. Natl Acad. Sci. USA 97, 5528–5533 (2000)
Cornish-Bowden, A. & Cardenas, M. L. Complex networks of interactions connect genes to phenotypes. Trends Biochem. Sci. 26, 463–465 (2001)
Kitano, H. Systems Biology: A brief overview. Science 295, 1662–1664 (2002)
Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N. & Barabási, A.-L. The large-scale organization of metabolic networks. Nature 407, 651–654 (2000)
Edwards, J. S., Ibarra, R. U. & Palsson, B. O. In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nature Biotechnol. 19, 125–130 (2001)
Schuster, S., Dandekar, T. & Fell, D. A. Detection of elementary flux modes in biochemical networks: a promising tool for pathway analysis and metabolic engineering. Trends Biotechnol. 17, 53–60 (1999)
Schuster, S., Fell, D. A. & Dandekar, T. A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks. Nature Biotechnol. 18, 326–332 (2000)
Van Dien, S. J. & Lidstrom, M. E. Stoichiometric model for evaluating the metabolic capabilities of the facultative methylotroph Methylobacterium extorquens AM1, with application to reconstruction of C3 and C4 metabolism. Biotechnol. Bioeng. 78, 296–312 (2002)
Oh, M.-K. & Liao, J. C. Gene expression profiling by DNA microarrays and metabolic fluxes in Escherichia coli. Biotechnol. Prog. 16, 278–286 (2000)
Heinrich, R. & Schuster, S. The Regulation of Cellular Systems (Chapman & Hall, New York, 1996)
Canonaco, F. et al. Metabolic flux response to phosphoglucose isomerase knock-out in Escherichia coli and impact of overexpression of the soluble transhydrogenase UdhA. FEMS Microbiol. Lett. 204, 247–252 (2001)
Barkai, N. & Leibler, S. Robustness in simple biochemical networks. Nature 387, 913–917 (1997)
von Dassow, G., Meir, E., Munro, E. M. & Odell, G. M. The segment polarity network is a robust developmental module. Nature 406, 188–192 (2000)
Stelling, J. & Gilles, E. D. in Proc. 2nd Intl Conf. Syst. Biol. (eds Yi, T. M., Hucka, M., Morohashi, M. & Kitano, H.) 181–190 (Omnipress, Madison, WI, 2001)
Csete, M. E. & Doyle, J. C. Reverse engineering of biological complexity. Science 295, 1664–1669 (2002)
Strogatz, S. H. Exploring complex networks. Nature 410, 268–276 (2001)
ter Kuile, B. H. & Westerhoff, H. V. Transcriptome meets metabolome: hierarchical and metabolic regulation of the glycolytic pathway. FEBS Lett. 500, 169–171 (2001)
Oh, M.-K., Rohlin, L., Kao, K. C. & Liao, J. C. Global expression profiling of acetate grown Escherichia coli. J. Biol. Chem. 277, 13175–13183 (2002)
Pfeiffer, T., Schuster, S. & Bonhoeffer, S. Cooperation and competition in the evolution of ATP-producing pathways. Science 292, 504–507 (2001)
Cleveland, W. S. Visualizing Data (AT&T Bell Laboratories, Murray Hill, NJ, 1993)
Covert, M. W. & Palsson, B. O. Transcriptional regulation in constraints-based metabolic models of Escherichia coli. J. Biol. Chem. 277, 28058–28064 (2002)
Stelling, J., Kremling, A., Ginkel, M., Bettenbrock, K. & Gilles, E. D. Foundations of Systems Biology (ed. Kitano, H.) 189–212 (MIT Press, Cambridge, MA, 2001)
Schilling, C. H., Letscher, D. & Palsson, B. O. 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)
Marcotte, E. M. et al. A combined algorithm for genome-wide prediction of protein function. Nature 402, 83–86 (1999)
Holter, N. S. et al. Fundamental patterns underlying gene expression profiles: Simplicity from complexity. Proc. Natl Acad. Sci. USA 97, 8409–8414 (2000)
Ge, H., Liu, Z., Church, G. M. & Vidal, M. Correlation between transcriptome and interactome mapping data from Saccharomyces cerevisiae. Nature Genet. 29, 482–486 (2001)
Klamt, S., Stelling, J., Ginkel, M. & Gilles, E. D. FluxAnalyzer: Exploring structure, pathways, and flux distributions in metabolic networks on interactive flux maps. Bioinformatics (in the press)
Bilke, S. & Peterson, C. Topological properties of citation and metabolic networks. Phys. Rev. E 64, 036106 (2001)
Acknowledgements
We thank M. Ginkel for software optimization, J. Liao for providing us with data before publication, U. Sauer, S. Bonhoeffer and A. Cornish-Bowden for critical reading of the manuscript and suggestions. S.S. gratefully acknowledges financial support by the Deutsche Forschungsgemeinschaft.
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Stelling, J., Klamt, S., Bettenbrock, K. et al. Metabolic network structure determines key aspects of functionality and regulation. Nature 420, 190–193 (2002). https://doi.org/10.1038/nature01166
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DOI: https://doi.org/10.1038/nature01166
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