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

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.