Uniform sampling of steady-state flux spaces: means to design experiments and to interpret enzymopathies.

TitleUniform sampling of steady-state flux spaces: means to design experiments and to interpret enzymopathies.
Publication TypeJournal Article
Year of Publication2004
AuthorsPrice ND, Schellenberger J, Palsson BØ
JournalBiophysical journal
Volume87
Issue4
Pagination2172-86
PubMed Date2004 Oct
ISSN0006-3495
KeywordsAnimals, Blood Proteins, Computer Simulation, Erythrocytes, Gene Expression Regulation, Enzymologic, Humans, Metabolism, Inborn Errors, Models, Biological, Models, Statistical, Multienzyme Complexes, Sample Size, Signal Transduction
Abstract

Reconstruction of genome-scale metabolic networks is now possible using multiple different data types. Constraint-based modeling is an approach to interrogate capabilities of reconstructed networks by constraining possible cellular behavior through the imposition of physicochemical laws. As a result, a steady-state flux space is defined that contains all possible functional states of the network. Uniform random sampling of the steady-state flux space allows for the unbiased appraisal of its contents. Monte Carlo sampling of the steady-state flux space of the reconstructed human red blood cell metabolic network under simulated physiologic conditions yielded the following key results: 1), probability distributions for the values of individual metabolic fluxes showed a wide variety of shapes that could not have been inferred without computation; 2), pairwise correlation coefficients were calculated between all fluxes, determining the level of independence between the measurement of any two fluxes, and identifying highly correlated reaction sets; and 3), the network-wide effects of the change in one (or a few) variables (i.e., a simulated enzymopathy or fixing a flux range based on measurements) were computed. Mathematical models provide the most compact and informative representation of a hypothesis of how a cell works. Thus, understanding model predictions clearly is vital to driving forward the iterative model-building procedure that is at the heart of systems biology. Taken together, the Monte Carlo sampling procedure provides a broadening of the constraint-based approach by allowing for the unbiased and detailed assessment of the impact of the applied physicochemical constraints on a reconstructed network.

Alternate JournalBiophys. J.
PubMed ID15454420

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