Thermo-Flux: generation and analysis of comprehensive thermodynamic-stoichiometric metabolic network models

Abstract

Metabolic modelling, particularly through genome-scale stoichiometric models and flux balance analysis (FBA), has greatly advanced our understanding of metabolism. Yet, there is a continued quest to further improve the predictive capabilities of FBA. While thermodynamic constraints can allow for improved predictions, their addition to metabolic models has so far required cumbersome manual curation. To circumvent manual curation, we introduce Thermo-Flux, a semi-automated Python package, which converts stoichiometric metabolic network models into comprehensive thermodynamic-stoichiometric models for improved predictions. Thermo-Flux enables (i) automated mass and charge balancing while considering physical and biochemical parameters, (ii) definition of transporter variants and Gibbs energies for membrane transport, (iii) robust handling of metabolites with unknown structures or Gibbs energies, and integrates (iv) recent advances in determining Gibbs energies and their respective uncertainties. To guide users in the conversion of stoichiometric models into comprehensive thermodynamic-stoichiometric models, we provide detailed instructions on how to use the Thermo-Flux pipeline and include background information to enable appropriate modeling assumptions. By converting 87 stoichiometric models from the BiGG database and demonstrating improved flux predictions for a genome-scale yeast model (iMM904) converted into a thermodynamic-stoichiometric model, we showcase the applicability of Thermo-Flux. We expect Thermo-Flux to support fundamental metabolic research and biotechnological applications. The package is available at https://github.com/molecular-systems-biology/thermo-flux along with tutorials.