Skip to content
DraconiticResearch
Search Sources API
184,937 studies31 sourcesUpdated 34 min ago
Peer-reviewedCC BY-NC-NDNpj metabolic health and disease

Roadmap for inferring metabolic regulation using multiomics, machine learning, and genome-scale modeling.

  • Ryan Schildcrout1
  • Nicole Rhoads2,3
  • Sriram Chandrasekaran4,5,6,7,8
8 affiliations
  1. 1Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.
  2. 2Department of Pharmacology, University of Michigan, Ann Arbor, MI, USA.
  3. 3Fred Hutchinson Cancer Center, Seattle, WA, USA.
  4. 4Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA. [email protected].
  5. 5Cellular and Molecular Biology Program, University of Michigan Medical School, Ann Arbor, MI, USA. [email protected].
  6. 6Program in Chemical Biology, University of Michigan, Ann Arbor, MI, USA. [email protected].
  7. 7Center for Bioinformatics and Computational Medicine, Ann Arbor, MI, USA. [email protected].
  8. 8Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI, USA. [email protected].
Read full textPDFDOICite

Abstract

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.

Related

  1. Decomposing multi-scale dynamic regulation from single-cell multiomics with scMagnify
    Xufeng Chen, Xi Yan, Bihan Shen +7
    Nature CommunicationsPeer-reviewedCC BY-NC-ND 4.0Full textMolecular biology & gene regulationCrossref
  2. Inferring flux-based metabolic fingerprints from gene expression to predict drug response and facilitate patient stratification
    Nathaniel Polley, Tony Kaoma, Petr V. Nazarov +1
    Communications MedicinePeer-reviewedCC BY-NC-ND 4.0Full textMolecular biology & gene regulationCrossref
  3. Inferring Cell Fate Trajectories in Time-Resolved Metabolic RNA Labeling data
    Anna Audit, Gabriel Peyré, Laura Cantini
    HALPreprintCC BY-NC 4.0Full textMolecular biology & gene regulationHAL
  4. TraSPIN: inferring transporter specificity through language model-guided graph learning
    Tong Pan, Jie Liu, Jing Xu +7
    Genome BiologyPeer-reviewedCC BY-NC-ND 4.0Molecular biology & gene regulationGenetics & genomicsCrossref
  5. Metabolic regulation of ATM-KAT8-RPA1 axis in DNA damage repair and chemoresistance
    Xin Ding, Jie Yang, Jinhuan Wu +14
    iSciencePeer-reviewedCC BY 4.0Molecular biology & gene regulationOncologyCell PressCrossref

Cite

BibTeXRIS
BibTeX
@article{schildcrout2026roadmap,
  title = {{Roadmap for inferring metabolic regulation using multiomics, machine learning, and genome-scale modeling.}},
  author = {Ryan Schildcrout and Nicole Rhoads and Sriram Chandrasekaran},
  journal = {Npj metabolic health and disease},
  year = {2026},
  month = nov,
  doi = {10.1038/s44324-026-00138-w},
  url = {https://europepmc.org/article/MED/42972011},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - Roadmap for inferring metabolic regulation using multiomics, machine learning, and genome-scale modeling.
AU  - Ryan Schildcrout
AU  - Nicole Rhoads
AU  - Sriram Chandrasekaran
PY  - 2026
DA  - 2026/11/02
JO  - Npj metabolic health and disease
DO  - 10.1038/s44324-026-00138-w
UR  - https://europepmc.org/article/MED/42972011
AB  - 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.
SN  - 2948-2828
ER  -