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

TL;DR

This paper introduces a fast, scalable algorithm to explore the Gibbs energy landscape of genome-scale metabolic networks, aiding in understanding cellular metabolism and identifying thermodynamic infeasibilities.

Contribution

The authors develop a novel stoichiometry-based method that efficiently analyzes the Gibbs energy landscape at genome scale, improving thermodynamic feasibility assessments in metabolic models.

Findings

Predicted chemical potentials of metabolites in human red blood cells.

Identified 23 thermodynamically infeasible reaction loops in E. coli.

Successfully analyzed over one million flux configurations.

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.