Scholarlink

<p>Metabolic modeling with stoichiometric models and flux balance analysis (FBA) has greatly advanced our understanding of metabolism. However, valid FBA predictions require mechanistically correct constraints. Thermodynamic constraints can increase the mechanistic foundations of stoichiometric models and reduce the solution space, but incorporating them has so far required cumbersome manual effort. To circumvent manual curation, we introduce ’Thermo-Flux’, a semi-automated Python package that converts stoichiometric models into comprehensive thermodynamic-stoichiometric models. ’Thermo-Flux’ enables (i) automated mass and charge balancing while considering physical and biochemical parameters, (ii) definition of transporter variants and Gibbs energies for transport processes, (iii) handling of metabolites with unknown structures or Gibbs energies, and (iv) integration of recent methods for determining Gibbs energies and their uncertainties. To guide users, we provide detailed instructions on how to use ’Thermo-Flux’ and include background information to facilitate appropriate modeling assumptions. We highlight the applicability of ’Thermo-Flux’ by converting 87 stoichiometric models from the BiGG database and demonstrate improved flux predictions for a genome-scale yeast model (iMM904). We expect ’Thermo-Flux’ to support fundamental and applied metabolic research.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月3日

查看全文
  • Edward N Smith,
  • Nathan Fargier,
  • José Losa,
  • Matthias Heinemann

摘要

Metabolic modeling with stoichiometric models and flux balance analysis (FBA) has greatly advanced our understanding of metabolism. However, valid FBA predictions require mechanistically correct constraints. Thermodynamic constraints can increase the mechanistic foundations of stoichiometric models and reduce the solution space, but incorporating them has so far required cumbersome manual effort. To circumvent manual curation, we introduce ’Thermo-Flux’, a semi-automated Python package that converts stoichiometric models into comprehensive thermodynamic-stoichiometric models. ’Thermo-Flux’ enables (i) automated mass and charge balancing while considering physical and biochemical parameters, (ii) definition of transporter variants and Gibbs energies for transport processes, (iii) handling of metabolites with unknown structures or Gibbs energies, and (iv) integration of recent methods for determining Gibbs energies and their uncertainties. To guide users, we provide detailed instructions on how to use ’Thermo-Flux’ and include background information to facilitate appropriate modeling assumptions. We highlight the applicability of ’Thermo-Flux’ by converting 87 stoichiometric models from the BiGG database and demonstrate improved flux predictions for a genome-scale yeast model (iMM904). We expect ’Thermo-Flux’ to support fundamental and applied metabolic research.