Leveraging metabolic modeling and machine learning to uncover modulators of quiescence depth.
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ABSTRACT:
SUBMITTER: Eames A
PROVIDER: S-EPMC10825626 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
Publications
Leveraging metabolic modeling and machine learning to uncover modulators of quiescence depth.
Eames Alec A Chandrasekaran Sriram S
PNAS nexus 20240112 1
Quiescence, a temporary withdrawal from the cell cycle, plays a key role in tissue homeostasis and regeneration. Quiescence is increasingly viewed as a continuum between shallow and deep quiescence, reflecting different potentials to proliferate. The depth of quiescence is altered in a range of diseases and during aging. Here, we leveraged genome-scale metabolic modeling (GEM) to define the metabolic and epigenetic changes that take place with quiescence deepening. We discovered contrasting chan ...[more]
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