ArticleThe Journal of biological chemistry2012
Network reconstruction and systems analysis of cardiac myocyte hypertrophy signaling.
Article in The Journal of biological chemistry, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 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.
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Who cites it
73 citing papers in PubMed, 119 citations in OpenAlex.
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- Single-cell dynamics reveal a stress-induced decision between hypertrophy and apoptosis in neonatal rat cardiomyocytes.Journal of molecular and cellular cardiology plus · 2025Article
- Multi-cellular network model predicts alterations in glomerular endothelial structure in diabetic kidney disease.PLoS computational biology · 2025Article
- Thermodynamically consistent, reduced models of gene regulatory networks.Royal Society open science · 2025Article
- Computational modelling of cardiac fibroblast signalling reveals a key role for CaThe Journal of physiology · 2025Article
- The Evolution of Systems Biology and Systems Medicine: From Mechanistic Models to Uncertainty Quantification.Annual review of biomedical engineering · 2025Review
- Contributions of mechanical loading and hormonal changes to eccentric hypertrophy during volume overload: A Bayesian analysis using logic-based network models.PLoS computational biology · 2025Article
- Logic-based modeling of biological networks with Netflux.PLoS computational biology · 2025Article
- Logic-based machine learning predicts how escitalopram attenuates cardiomyocyte hypertrophy.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- 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
- Benchmarking of protein interaction databases for integration with manually reconstructed signalling network models.The Journal of physiology · 2024Article
- Network model of skeletal muscle cell signalling predicts differential responses to endurance and resistance exercise training.Experimental physiology · 2024Article
- Modeling cardiomyocyte signaling and metabolism predicts genotype-to-phenotype mechanisms in hypertrophic cardiomyopathy.Computers in biology and medicine · 2024Article
- Computational model captures cardiac growth in hypertensive pregnancies and in the postpartum period.American journal of physiology. Heart and circulatory physiology · 2024Article
- A Graphical Approach to Visualize and Interpret Biochemically Coupled Biomechanical Models.Journal of biomechanical engineering · 2024Article
13 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Cardiac hypertrophy is managed by a dense web of signaling pathways with many pathways influencing myocyte growth. A quantitative understanding of the contributions of individual pathways and their interactions is needed to better understand hypertrophy signaling and to develop more effective therapies for heart failure. We developed a computational model of the cardiac myocyte hypertrophy signaling network to determine how the components and network topology lead to differential regulation of transcription factors, gene expression, and myocyte size. Our computational model of the hypertrophy signaling network contains 106 species and 193 reactions, integrating 14 established pathways regulating cardiac myocyte growth. 109 of 114 model predictions were validated using published experimental data testing the effects of receptor activation on transcription factors and myocyte phenotypic outputs. Network motif analysis revealed an enrichment of bifan and biparallel cross-talk motifs. Sensitivity analysis was used to inform clustering of the network into modules and to identify species with the greatest effects on cell growth. Many species influenced hypertrophy, but only a few nodes had large positive or negative influences. Ras, a network hub, had the greatest effect on cell area and influenced more species than any other protein in the network. We validated this model prediction in cultured cardiac myocytes. With this integrative computational model, we identified the most influential species in the cardiac hypertrophy signaling network and demonstrate how different levels of network organization affect myocyte size, transcription factors, and gene expression.
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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.