Bioinformatics Advance Access published online on July 10, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn352
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Integrating Metabolic, Transcriptional Regulatory and Signal Transduction Models in Escherichia coli
1Department of Bioengineering, 318 Campus Drive, Stanford, CA 94305-5444, 2Program in Biomedical Informatics, 251 Campus Drive, Stanford CA 94305-5479
*To whom correspondence should be addressed. Dr. Markus W. Covert, E-mail: covert{at}stanford.edu
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Abstract |
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Motivation: The effort to build a whole-cell model requires the development of new modeling approaches, and in particular the integration of models for different types of processes, each of which may be best described using different representation. Regulatory flux-balance analysis has been useful for large-scale analysis of metabolic networks and the associated transcriptional regulation, and of current interest is the integration of this approach with detailed kinetic models based on ordinary differential equations.
Results: We developed an approach to modeling the dynamic behavior of metabolic, regulatory and signaling networks by combining flux-balance analysis with regulatory boolean logic, and ordinary differential equations. We use this approach (called integrated flux-balance analysis, or iFBA) to create an integrated model of Escherichia coli which combines a flux-balance based, central carbon metabolic and transcriptional regulatory model with an ODE-based, detailed model of carbohydrate uptake control. We compare the predicted E. coli wild-type and single gene perturbation phenotypes for diauxic growth on glucose/lactose and glucose/glucose-6-phosphate with that of the individual models. We find that iFBA encapsulates the dynamics of 3 internal metabolites and 3 transporters inadequately predicted by rFBA. Furthermore, we find that iFBA predicts different and more accurate phenotypes than the ODE model for 85 of 334 single gene perturbation simulations, as well as the wild type simulations. We conclude that iFBA is a significant improvement over the individual rFBA and ODE models.
Availability: All MATLAB files used in this study will be available at http://www.simtk.org/home/ifba/.
Contact: covert{at}stanford.edu
Associate Editor: Dr. Limsoon Wong
Received on April 14, 2008; revised on June 17, 2008; accepted on July 8, 2008
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