Network structure and fluctuation data improve inference of metabolic interaction strengths with the inverse Jacobian.

ABSTRACT: Based on high-throughput metabolomics data, the recently introduced inverse differential Jacobian algorithm can infer regulatory factors and molecular causality within metabolic networks close to steady-state. However, these studies assumed perturbations acting independently on each metabolite, corresponding to metabolic system fluctuations. In contrast, emerging evidence puts forward internal network fluctuations, particularly from gene expression fluctuations, leading to correlated perturbations on metabolites. Here, we propose a novel approach that exploits these correlations to quantify relevant metabolic interactions. By integrating enzyme-related fluctuations in the construction of an appropriate fluctuation matrix, we are able to exploit the underlying reaction network structure for

SUBMITTER: Li J 

PROVIDER: S-EPMC11585569 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature