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Flux-sum coupling analysis of metabolic network models
Mihriban Seyis1, Zahra Razaghi-Moghadam1,2, Zoran Nikoloski1,2
1Bioinformatics Department, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany.
Plos Computational Biology
|April 7, 2025
Summary
We developed Flux-Sum Coupling Analysis (FSCA) to understand metabolite concentration links without measurements. FSCA reveals metabolic regulation insights by analyzing flux-sums in cellular metabolism.
Area of Science:
- Systems Biology
- Metabolic Modeling
- Biochemistry
Background:
- Metabolites are crucial for cellular metabolism, acting as substrates and regulators.
- Accurate metabolite concentration proxies are needed for metabolic modeling, especially without direct measurements.
- Existing constraint-based modeling lacks efficient methods to link metabolite concentrations.
Purpose of the Study:
- To introduce a novel constraint-based approach, Flux-Sum Coupling Analysis (FSCA).
- To investigate interdependencies between metabolite concentrations using flux-sums.
- To provide a tool for understanding metabolic regulation and improving systems biology.
Main Methods:
- Developed the Flux-Sum Coupling Analysis (FSCA) approach.
- Applied FSCA to metabolic models of Escherichia coli, Saccharomyces cerevisiae, and Arabidopsis thaliana.
- Validated FSCA using existing metabolite concentration measurements from E. coli.
Main Results:
- FSCA identified conserved coupling relationships across different species' metabolic models.
- Similarities in coupled metabolite pairs were pinpointed between the studied organisms.
- FSCA-identified couplings accurately reflected qualitative associations with measured E. coli metabolite concentrations, validating flux-sum as a proxy.
Conclusions:
- Flux-Sum Coupling Analysis (FSCA) is a reliable tool for studying metabolite concentration interdependencies.
- FSCA advances the understanding of metabolic regulation in the absence of direct concentration data.
- The method improves flux-centered systems biology approaches by providing insights into metabolic states.
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