This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Price, N. D. Articles by Palsson, B. O. Search for Related Content PubMed PubMed Citation Articles by Price, N. D. Articles by Palsson, B. O.

Uniform Sampling of Steady-State Flux Spaces: Means to Design Experiments and to Interpret Enzymopathies

Department of Bioengineering, University of California at San Diego, La Jolla, California

Correspondence: Address reprint requests to Bernhard O. Palsson, Dept. of Bioengineering, University of California, 9500 Gilman Dr., La Jolla, CA 92093-0412. E-mail:palsson{at}ucsd.edu.

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.

This article has been cited by other articles:

				N. D. Price, I. Thiele, and B. O. Palsson Candidate States of Helicobacter pylori's Genome-Scale Metabolic Network upon Application of "Loop Law" Thermodynamic Constraints Biophys. J., June 1, 2006; 90(11): 3919 - 3928. [Abstract] [Full Text] [PDF]

				I. Thiele, N. D. Price, T. D. Vo, and B. O. Palsson Candidate Metabolic Network States in Human Mitochondria: IMPACT OF DIABETES, ISCHEMIA, AND DIET J. Biol. Chem., March 25, 2005; 280(12): 11683 - 11695. [Abstract] [Full Text] [PDF]

				I. Famili, R. Mahadevan, and B. O. Palsson k-Cone Analysis: Determining All Candidate Values for Kinetic Parameters on a Network Scale Biophys. J., March 1, 2005; 88(3): 1616 - 1625. [Abstract] [Full Text] [PDF]