Journal
BIOPHYSICAL JOURNAL
Volume 95, Issue 12, Pages 5606-5617Publisher
CELL PRESS
DOI: 10.1529/biophysj.108.135442
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Funding
- Department of Energy [DE-FG02-07ER64490]
- National Institute of General Medical Sciences [R01GM076143]
- University of California, Los Angeles
- Department of Energy Institute for Genomics and Proteomics
- UCLA Chancellor's Dissertation Year Fellowship
- National Institutes of Health Biotechnology
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Complete modeling of metabolic networks is desirable, but it is difficult to accomplish because of the lack of kinetics. As a step toward this goal, we have developed an approach to build an ensemble of dynamic models that reach the same steady state. The models in the ensemble are based on the same mechanistic framework at the elementary reaction level, including known regulations, and span the space of all kinetics allowable by thermodynamics. This ensemble allows for the examination of possible phenotypes of the network upon perturbations, such as changes in enzyme expression levels. The size of the ensemble is reduced by acquiring data for such perturbation phenotypes. If the mechanistic framework is approximately accurate, the ensemble converges to a smaller set of models and becomes more predictive. This approach bypasses the need for detailed characterization of kinetic parameters and arrives at a set of models that describes relevant phenotypes upon enzyme perturbations.
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