4.5 Article

SBML to bond graphs: From conversion to composition

期刊

MATHEMATICAL BIOSCIENCES
卷 352, 期 -, 页码 -

出版社

ELSEVIER SCIENCE INC
DOI: 10.1016/j.mbs.2022.108901

关键词

SBML; BioModels; Bond graphs; Automatic conversion; Glycolysis; Pentose phosphate pathway

资金

  1. Aotearoa Fellowship
  2. School of Mathematics and Statistics University of Melbourne
  3. Marsden Fast-Start grant [UOA1703]
  4. Royal Society of New Zealand
  5. Health Research Council of New Zealand [CE140100036]
  6. Australian Research Council Centre of Excel-lence in Convergent Bio-Nano Science and Technology [21/116]
  7. Aotearoa Fellowship from the Aotearoa Foundation
  8. Sir Charles Hercus Health Research Fellowship
  9. Center for Reproducible Biomedical Modeling [P41 EB023912/EB/NIBIB NIH HHS/United States]

向作者/读者索取更多资源

This article introduces a new framework that automatically converts SBML models into bond graphs to assess the physical plausibility and thermodynamic consistency of the models. By converting the models into bond graphs, mergeable and physically plausible coupled models can be obtained.
The Systems Biology Markup Language (SBML) is a popular software-independent XML-based format for describing models of biological phenomena. The BioModels Database is the largest online repository of SBML models. Several tools and platforms are available to support the reuse and composition of SBML models. However, these tools do not explicitly assess whether models are physically plausible or thermodynamically consistent. This often leads to ill-posed models that are physically impossible, impeding the development of realistic complex models in biology. Here, we present a framework that can automatically convert SBML models into bond graphs, which imposes energy conservation laws on these models. The new bond graph models are easily mergeable, resulting in physically plausible coupled models. We illustrate this by automatically converting and coupling a model of pyruvate distribution to a model of the pentose phosphate pathway.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据