An objective function exploiting suboptimal solutions in metabolic networks

16Citations
Citations of this article
113Readers
Mendeley users who have this article in their library.

This artice is free to access.

Abstract

Background: Flux Balance Analysis is a theoretically elegant, computationally efficient, genome-scale approach to predicting biochemical reaction fluxes. Yet FBA models exhibit persistent mathematical degeneracy that generally limits their predictive power.Results: We propose a novel objective function for cellular metabolism that accounts for and exploits degeneracy in the metabolic network to improve flux predictions. In our model, regulation drives metabolism toward a region of flux space that allows nearly optimal growth. Metabolic mutants deviate minimally from this region, a function represented mathematically as a convex cone. Near-optimal flux configurations within this region are considered equally plausible and not subject to further optimizing regulation. Consistent with relaxed regulation near optimality, we find that the size of the near-optimal region predicts flux variability under experimental perturbation.Conclusion: Accounting for suboptimal solutions can improve the predictive power of metabolic FBA models. Because fluctuations of enzyme and metabolite levels are inevitable, tolerance for suboptimality may support a functionally robust metabolic network. © 2013 Wintermute et al.; licensee BioMed Central Ltd.

Cite

CITATION STYLE

APA

Wintermute, E. H., Lieberman, T. D., & Silver, P. A. (2013). An objective function exploiting suboptimal solutions in metabolic networks. BMC Systems Biology, 7. https://doi.org/10.1186/1752-0509-7-98

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free