Roadmap for inferring metabolic regulation using multiomics, machine learning, and genome-scale modeling
Open accessAbstract
Machine learning and genome-scale metabolic modeling have accelerated the discovery of metabolic-regulatory relationships in model microbes and mammalian cells. This review provides a roadmap for integrating omics with computational methods for studying systems-level transcriptional, post-translational, and allosteric regulation. Developing methods to infer and integrate multiple regulatory mechanisms simultaneously is essential for discovering novel modes of metabolic regulation and precise engineering of metabolic networks.
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References 124
- Authors
- Schildcrout, R., Rhoads, N., & Chandrasekaran, S.
- Affiliations
- University of Michigan (US)
- Corresponding author
- Chandrasekaran, Sriram
- Venue
- npj Metabolic Health and Disease
- Published
- 2026-11-02
- Volume
- 4
- Issue
- 1
- ISSN
- 2948-2828 (ISSN-L)
- OpenAlex
- W7225089056
- Cited by
- 0
- References
- 124
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- npj Metabolic Health and Disease (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- Advanced Proteogenomics of Cancer Program (T32 CA140044)
- Graduate Assistance in Areas of National Need
- National Institute of General Medical Sciences (R35GM137795)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- metabolic regulation, multi-omics integration, genome-scale metabolic modeling, machine learning, transcriptional regulation, post-translational regulation, allosteric regulation
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