Jeffrey J. Saucerman

The paper identifies SP1 as a transcription factor necessary for human atrial cardiomyocyte identity, showing it interacts with NR2F2 to drive gene regulation.

This paper uses a computational framework to identify FDA-approved drugs that can promote cardiomyocyte proliferation, finding lithium as a potential therapeutic but highlighting congenital risks.

The paper develops a computational network model predicting DYRK1A inhibition promotes cardiomyocyte cycling after ischemic/reperfusion injury, validated through experiments in neonatal rat cardiomyocytes.

This study identifies mTOR as a therapeutic target for MYBPC3 mutation-induced hypertrophic cardiomyopathy using network modeling and experimental validation.

This paper develops a multiscale computational model to study how Thy-1 expression in fibroblast subpopulations influences fibrotic foci formation in IPF.

This study benchmarks large language models' ability to generate biochemical networks, finding moderate accuracy in signaling and metabolic networks but variable prediction performance.

This paper introduces CARBONITE, a method that links calcium dynamics with molecular markers in single cardiomyocytes, revealing functional and identity heterogeneity.

This study shows that reduced TBX5 dosage disrupts atrial cardiomyocyte identity, leading to cellular and molecular changes associated with atrial diseases.

This study identifies MEF2C-dependent gene regulatory networks in specific heart segments and shows their role in heart tube morphogenesis using multi-omics data and deep learning.

The paper identifies distinct PI3K and p38 pathways mediating neuregulin-1-induced changes in cardiomyocyte size and shape through systems analysis.

This paper uses a Bayesian network model to analyze how mechanical loading and hormonal changes contribute to eccentric hypertrophy in volume overload, showing neurohormonal responses drive growth rather than initial stretch.

The paper introduces Netflux, a user-friendly tool for simulating biological networks without programming, and provides a tutorial on its use for modeling and perturbation analysis.

The paper uses a logic-based machine learning method to predict how escitalopram reduces cardiomyocyte hypertrophy, validated through experiments and patient data.

The paper develops a computational model of cardiac proliferation signaling that identifies key regulators and predicts Hippo to cMyc module crosstalk driving cardiomyocyte proliferation.

The paper uses a multiscale computational model to predict how environmental changes and drug treatments affect microvascular remodeling in idiopathic pulmonary fibrosis.

The paper identifies Endoglin as an inhibitor of cardiomyocyte proliferation and shows its role in cardiac regeneration through genetic and mechanistic studies.

This paper develops a computational model of HCM cardiomyocyte signaling and metabolism to predict genotype-to-phenotype mechanisms and identify potential drug targets.

The paper uses high-content imaging and a new computational model to predict how drugs affect cardiac fibroblast phenotype and identify potential therapeutic mechanisms.

The paper develops a signaling network model to investigate genotype-to-phenotype mechanisms in familial cardiomyopathy, showing it can predict experimental outcomes and suggest therapeutic strategies.

The paper benchmarks protein interaction databases for their ability to recover manually curated edges from signaling network models of cardiac hypertrophy, mechano-signaling, and fibrosis.

This study uses single-cell tracking to show that cardiomyocytes decide early to either hypertrophy or undergo apoptosis under stress, with initial cell size and DNA content predicting fate.

The paper develops a computational model of macrophage signaling to predict activation responses to cytokine combinations, revealing a second axis of activation distinct from the M1-M2 polarization.

The paper develops a computational model to simulate fibroblast signaling and fibrosis after myocardial infarction, revealing how paracrine stimuli and signaling pathways drive fibroblast phenotype dynamics.

The paper identifies disease-associated genes missed by traditional methods using a personalized differential gene expression analysis in a genetically diverse mouse model of cardiac hypertrophy and heart failure.