ArticlePNAS nexus2024
Leveraging metabolic modeling and machine learning to uncover modulators of quiescence depth.
Article in PNAS nexus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 7 citations in OpenAlex.
- The eight pillars of within-host tuberculosis modelling.Frontiers in immunology · 2026Review
- Machine learning and metabolic modeling-based identification of hypoxia-driven metabolic signatures in pediatric cancers.Frontiers in pharmacology · 2026Article
- Article
- Quiescence Multiverse.Biomolecules · 2025Review
- AI-driven drug discovery and repurposing using multi-omics for myocardial infarction and heart failure.Exploration of medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 2 countries.
Funding
Abstract
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 changes in lipid catabolism and anabolism and diverging trends in histone methylation and acetylation. We then built a multi-cell type machine learning model that accurately predicts quiescence depth in diverse biological contexts. Using both machine learning and genome-scale flux simulations, we performed high-throughput screening of chemical and genetic modulators of quiescence and identified novel small molecule and genetic modulators with relevance to cancer and aging.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.