Probabilistic thermodynamic analysis of metabolic networks

Motivation: Random sampling of metabolic fluxes can provide a comprehensive description of the capabilities of a metabolic network. However, current sampling approaches do not model thermodynamics explicitly, leading to inaccurate predictions of an organism's potential or actual metabolic operations. Results: We present a probabilistic framework combining thermodynamic quantities with steady-state flux constraints to analyze the properties of a metabolic network. It includes methods for probabilistic metabolic optimization and for joint sampling of thermodynamic and flux spaces. Applied to a model of Escherichia coli, we use the methods to reveal known and novel mechanisms of substrate channeling, and to accurately predict reaction directions and metabolite concentrations. Interestingly, predicted flux distributions are multimodal, leading to discrete hypotheses on E.coli's metabolic capabilities.

Automated summary

The study introduces a probabilistic framework combining thermodynamics and steady-state flux constraints to analyze the properties of a metabolic network, accurately predicting reaction directions and metabolite concentrations and revealing metabolic mechanisms. The predicted flux distributions are multimodal, leading to discrete hypotheses on the metabolic capabilities of E.coli.