Abstract
Predicting behavior of large-scale biochemical networks represents one of the greatest challenges of bioinformatics and computational biology. Computational tools for predicting fluxes in biochemical networks are applied in the fields of integrated and systems biology, bioinformatics, and genomics, and to aid in drug discovery and identification of potential drug targets. Approaches, such as flux balance analysis (FBA), that account for the known stoichiometry of the reaction network while avoiding implementation of detailed reaction kinetics are promising tools for the analysis of large complex networks. Here we introduce energy balance analysis (EBA)--the theory and methodology for enforcing the laws of thermodynamics in such simulations--making the results more physically realistic and revealing greater insight into the regulatory and control mechanisms operating in complex large-scale systems. We show that EBA eliminates thermodynamically infeasible results associated with FBA.
Full Text
The Full Text of this article is available as a PDF (876.6 KB).
Selected References
These references are in PubMed. This may not be the complete list of references from this article.
- Alberty R. A. Equilibrium compositions of solutions of biochemical species and heats of biochemical reactions. Proc Natl Acad Sci U S A. 1991 Apr 15;88(8):3268–3271. doi: 10.1073/pnas.88.8.3268. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Edwards J. S., Ibarra R. U., Palsson B. O. In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nat Biotechnol. 2001 Feb;19(2):125–130. doi: 10.1038/84379. [DOI] [PubMed] [Google Scholar]
- Edwards J. S., Palsson B. O. Robustness analysis of the Escherichia coli metabolic network. Biotechnol Prog. 2000 Nov-Dec;16(6):927–939. doi: 10.1021/bp0000712. [DOI] [PubMed] [Google Scholar]
- Edwards J. S., Palsson B. O. The Escherichia coli MG1655 in silico metabolic genotype: its definition, characteristics, and capabilities. Proc Natl Acad Sci U S A. 2000 May 9;97(10):5528–5533. doi: 10.1073/pnas.97.10.5528. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hartwell L. H., Hopfield J. J., Leibler S., Murray A. W. From molecular to modular cell biology. Nature. 1999 Dec 2;402(6761 Suppl):C47–C52. doi: 10.1038/35011540. [DOI] [PubMed] [Google Scholar]
- Jeong H., Tombor B., Albert R., Oltvai Z. N., Barabási A. L. The large-scale organization of metabolic networks. Nature. 2000 Oct 5;407(6804):651–654. doi: 10.1038/35036627. [DOI] [PubMed] [Google Scholar]
- Qian Hong. Mesoscopic nonequilibrium thermodynamics of single macromolecules and dynamic entropy-energy compensation. Phys Rev E Stat Nonlin Soft Matter Phys. 2001 Dec 4;65(1 Pt 2):016102–016102. doi: 10.1103/PhysRevE.65.016102. [DOI] [PubMed] [Google Scholar]
- Ramakrishna R., Edwards J. S., McCulloch A., Palsson B. O. Flux-balance analysis of mitochondrial energy metabolism: consequences of systemic stoichiometric constraints. Am J Physiol Regul Integr Comp Physiol. 2001 Mar;280(3):R695–R704. doi: 10.1152/ajpregu.2001.280.3.R695. [DOI] [PubMed] [Google Scholar]
- Schilling C. H., Edwards J. S., Letscher D., Palsson B. Ø. Combining pathway analysis with flux balance analysis for the comprehensive study of metabolic systems. Biotechnol Bioeng. 2000;71(4):286–306. [PubMed] [Google Scholar]
- Schilling C. H., Palsson B. O. The underlying pathway structure of biochemical reaction networks. Proc Natl Acad Sci U S A. 1998 Apr 14;95(8):4193–4198. doi: 10.1073/pnas.95.8.4193. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Schilling C. H., Schuster S., Palsson B. O., Heinrich R. Metabolic pathway analysis: basic concepts and scientific applications in the post-genomic era. Biotechnol Prog. 1999 May-Jun;15(3):296–303. doi: 10.1021/bp990048k. [DOI] [PubMed] [Google Scholar]
- 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. 1999 Feb;17(2):53–60. doi: 10.1016/s0167-7799(98)01290-6. [DOI] [PubMed] [Google Scholar]