# The Essential Role of Thermodynamics in metabolic network modeling: physical insights and computational challenges

## TL;DR

This paper emphasizes the importance of thermodynamic constraints in metabolic network modeling, discussing computational challenges and physical insights to improve the accuracy of steady-state analyses.

## Contribution

It reviews the integration of thermodynamics into metabolic models, highlighting computational challenges and proposing variational principles for better constraint implementation.

## Key findings

- Thermodynamic constraints prevent unfeasible loops in models.
- Gibbs inequalities are key to ensuring physical feasibility.
- Exploration of non-convex flux spaces requires advanced algorithms.

## Abstract

Quantitative studies of cell metabolism are often based on large chemical reaction network models. A steady state approach is suited to analyze phenomena on the timescale of cell growth and circumvents the problem of incomplete experimental knowledge on kinetic laws and parameters, but it shall be supported by a correct implementation of thermodynamic constraints. In this article we review the latter aspect highlighting its computational challenges and physical insights. The simple introduction of Gibbs inequalities avoids the presence of unfeasible loops allowing for correct timescale analysis but leads to possibly non-convex feasible flux spaces, whose exploration needs efficient algorithms. We shorty review on the implementation of thermodynamics through variational principles in constraints based models of metabolic networks.