An objective function exploiting suboptimal solutions in metabolic networks

BMC Systems Biology

Pamela A Silver¹  Tami D Lieberman²  Edwin H Wintermute² 

[1] Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA;Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA

关键词: Networks;    Metabolic flux analysis;    Variability;    Metabolism;   

Others  :  1142071
DOI  :  10.1186/1752-0509-7-98

 received in 2013-06-03, accepted in 2013-09-30,  发布年份 2013

【 摘 要 】

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.

【 授权许可】

   
2013 Wintermute et al.; licensee BioMed Central Ltd.

【 图 表 】

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