ArticlePLoS computational biology2017
Predictive model identifies key network regulators of cardiomyocyte mechano-signaling.
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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Who cites it
48 citing papers in PubMed, 69 citations in OpenAlex.
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- Dynamic map illuminates Hippo-cMyc module crosstalk driving cardiomyocyte proliferation.Development (Cambridge, England) · 2025Article
- Global Sensitivity Analysis of a Novel Signaling Network for Heart Growth With Local IGF1 Production.International journal for numerical methods in biomedical engineering · 2025Article
- Multiscale computational model predicts how environmental changes and treatments affect microvascular remodeling in fibrotic disease.PNAS nexus · 2025Article
- Benchmarking of protein interaction databases for integration with manually reconstructed signalling network models.The Journal of physiology · 2024Article
- Modeling cardiomyocyte signaling and metabolism predicts genotype-to-phenotype mechanisms in hypertrophic cardiomyopathy.Computers in biology and medicine · 2024Article
- Network model of skeletal muscle cell signalling predicts differential responses to endurance and resistance exercise training.Experimental physiology · 2024Article
- Multiscale computational model predicts how environmental changes and drug treatments affect microvascular remodeling in fibrotic disease.bioRxiv : the preprint server for biology · 2024Article
- Differential sensitivity to longitudinal and transverse stretch mediates transcriptional responses in mouse neonatal ventricular myocytes.American journal of physiology. Heart and circulatory physiology · 2024Article
- Logic-based mechanistic machine learning on high-content images reveals how drugs differentially regulate cardiac fibroblasts.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Virtual drug screen reveals context-dependent inhibition of cardiomyocyte hypertrophy.British journal of pharmacology · 2023Article
- Logic-based mechanistic machine learning on high-content images reveals how drugs differentially regulate cardiac fibroblasts.bioRxiv : the preprint server for biology · 2023Article
- Review
- Network model integrated with multi-omic data predicts MBNL1 signals that drive myofibroblast activation.iScience · 2023Article
- Signaling network model of cardiomyocyte morphological changes in familial cardiomyopathy.Journal of molecular and cellular cardiology · 2023Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Mechanical strain is a potent stimulus for growth and remodeling in cells. Although many pathways have been implicated in stretch-induced remodeling, the control structures by which signals from distinct mechano-sensors are integrated to modulate hypertrophy and gene expression in cardiomyocytes remain unclear. Here, we constructed and validated a predictive computational model of the cardiac mechano-signaling network in order to elucidate the mechanisms underlying signal integration. The model identifies calcium, actin, Ras, Raf1, PI3K, and JAK as key regulators of cardiac mechano-signaling and characterizes crosstalk logic imparting differential control of transcription by AT1R, integrins, and calcium channels. We find that while these regulators maintain mostly independent control over distinct groups of transcription factors, synergy between multiple pathways is necessary to activate all the transcription factors necessary for gene transcription and hypertrophy. We also identify a PKG-dependent mechanism by which valsartan/sacubitril, a combination drug recently approved for treating heart failure, inhibits stretch-induced hypertrophy, and predict further efficacious pairs of drug targets in the network through a network-wide combinatorial search.
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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.