Dynamical models for metabolomics data integration

TL;DR

This paper reviews recent advances in dynamical metabolic models for metabolomics data integration, highlighting their potential to analyze complex, heterogeneous datasets beyond steady-state assumptions.

Contribution

It provides a comprehensive overview of recent methods and challenges in applying dynamical models to metabolomics data integration and analysis.

Findings

Progress in scalable simulation tools

Dynamical models can integrate heterogeneous data

Challenges remain in full adoption and application

Abstract

As metabolomics datasets are becoming larger and more complex, there is an increasing need for model-based data integration and analysis to optimally leverage these data. Dynamical models of metabolism allow for the integration of heterogeneous data and the analysis of dynamical phenotypes. Here, we review recent efforts in using dynamical metabolic models for data integration, focusing on approaches that are not restricted to steady-state measurements or that require flux distributions as inputs. Furthermore, we discuss recent advances and current challenges. We conclude that much progress has been made in various areas, such as the development of scalable simulation tools, and that, although challenges remain, dynamical modeling is a powerful tool for metabolomics data analysis that is not yet living up to its full potential.