A scalable algorithm to explore the Gibbs energy landscape of genome-scale metabolic networks

Abstract

The integration of various types of genomic data into predictive models of biological networks is one of the main challenges currently faced by computational biology. Constraint-based models in particular play a key role in the attempt to obtain a quantitative understanding of cellular metabolism at genome scale. In essence, their goal is to frame the metabolic capabilities of an organism based on minimal assumptions that describe the steady states of the underlying reaction network via suitable stoichiometric constraints, specifically mass balance and energy balance (i.e. thermodynamic feasibility). The implementation of these requirements to generate viable configurations of reaction fluxes and/or to test given flux profiles for thermodynamic feasibility can however prove to be computationally intensive. We propose here a fast and scalable stoichiometry-based method to explore the Gibbs energy landscape of a biochemical network at steady state. The method is applied to the problem of reconstructing the Gibbs energy landscape underlying metabolic activity in the human red blood cell, and to that of identifying and removing thermodynamically infeasible reaction cycles in the Escherichia coli metabolic network (iAF1260). In the former case, we produce consistent predictions for chemical potentials (or log-concentrations) of intracellular metabolites; in the latter, we identify a restricted set of loops (23 in total) in the periplasmic and cytoplasmic core as the origin of thermodynamic infeasibility in a large sample ($10^6$) of flux configurations generated randomly and compatibly with the prior information available on reaction reversibility.

arXiv comment11 pages, 6 figures, 1 table; for associated supporting material see http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002562

References in corpus (4)

• Identifying essential genes in E. coli from a metabolic optimization principle
• Von Neumann's expanding model on random graphs
• The solution space of metabolic networks: producibility, robustness and fluctuations
• Typical properties of optimal growth in the Von Neumann expanding model for large random economies

Cited by in corpus (8)

• Energy metabolism and glutamate-glutamine cycle in the brain: a stoichiometric modeling perspective
• Counting and correcting thermodynamically infeasible flux cycles in genome-scale metabolic networks
• An analytic approximation of the feasible space of metabolic networks
• Removing both Internal and Unrealistic Energy-Generating Cycles in Flux Balance Analysis
• On the Clausius formulation of the second law in stationary chemical networks through the theorems of the alternative
• A weighted belief-propagation algorithm to estimate volume-related properties of random polytopes
• The free lunch of a scale-free metabolism
• Identifying all irreducible conserved metabolite pools in genome-scale metabolic networks: a general method and the case of Escherichia coli