Bioinformatics Advance Access originally published online on July 10, 2008
Bioinformatics 2008 24(18):2044-2050; doi:10.1093/bioinformatics/btn352
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Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli
1Department of Bioengineering, Stanford University, 318 Campus Drive, Stanford, CA 94305-5444 and 2Program in Biomedical Informatics, 251 Campus Drive, Stanford, CA 94305-5479, USA
*To whom correspondence should be addressed.
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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. Flux-balance analysis (FBA) has been useful for large-scale analysis of metabolic networks, and methods have been developed to incorporate transcriptional regulation (regulatory FBA, or rFBA). Of current interest is the integration of these approaches with detailed models based on ordinary differential equations (ODEs).
Results: We developed an approach to modeling the dynamic behavior of metabolic, regulatory and signaling networks by combining FBA with regulatory Boolean logic, and ordinary differential equations. We use this approach (called integrated FBA, 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 Escherichia 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 three internal metabolites and three 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 for the wild-type simulations. We conclude that iFBA is a significant improvement over the individual rFBA and ODE modeling paradigms.
Availability: All MATLAB files used in this study are available at http://www.simtk.org/home/ifba/.
Contact: covert{at}stanford.edu
Supplementary information:Supplementary data are available at Bioinformatics online.
Associate Editor: Limsoon Wong
Received on April 14, 2008; revised on June 17, 2008; accepted on July 8, 2008
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