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Eric A Sobie, PhD
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About Me
The Sobie Laboratory
Language
Position
Research Topics
Biophysics, Cardiovascular, Computational Biology, Electrophysiology, Mathematical Modeling of Biomedical Systems, Membrane Proteins/Channels, Signal Transduction
Multi-Disciplinary Training Areas
Cancer Biology [CAB], Pharmacology and Therapeutics Discovery [PTD]
About Me
The Sobie Laboratory
Language
Position
Research Topics
Biophysics, Cardiovascular, Computational Biology, Electrophysiology, Mathematical Modeling of Biomedical Systems, Membrane Proteins/Channels, Signal Transduction
Multi-Disciplinary Training Areas
Cancer Biology [CAB], Pharmacology and Therapeutics Discovery [PTD]
About Me
The Sobie Laboratory
Language
Position
Research Topics
Biophysics, Cardiovascular, Computational Biology, Electrophysiology, Mathematical Modeling of Biomedical Systems, Membrane Proteins/Channels, Signal Transduction
Multi-Disciplinary Training Areas
Cancer Biology [CAB], Pharmacology and Therapeutics Discovery [PTD]
Education
BSE, Duke University
PhD, The Johns Hopkins University School of Medicine
Postdoctoral Fellowship, University of Maryland
Awards
2008
Dr. Harold and Golden Lamport Research Award
Mount Sinai School of Medicine
Research
Specific Research Interest:
Mechanisms underlying cardiac dysfunction and arrhythmias using physiological experiments coupled with mathematical modeling
Postdoctoral Fellows: Young-Seon Lee, Eva Polakova
Graduate and Medical Students: John Jones Molina, Megan Cummins, Ryan Devenyi, Pavan Dalal
Undergraduates and Visitors: Kathleen McGovern, Alessandro Giovannini, Allie Lopatkin
Research Personnel: Frank Fabris
The Sobie Laboratory combines mathematical modeling with state-of-the-art experimental techniques in order to improve our quantitative understanding of cardiac physiology. We generate predictions using mathematical models, then test these predictions experimentally by measuring calcium and transmembrane voltage in heart cells. This provides novel insight into the initiation of arrhythmias and dysfunction that occurs in disease states such as heart failure. Specific research projects are described in more detail at our lab website.
Keywords: Biophysics, Calcium, Computer Simulation, Electrophysiology, Heart, Mathematical and Computational Biology, Systems Biology
Current Research Studies:
2010-2013: Novel computational approaches for understanding drug-induced arrhythmia. American Heart Association 10GRNT4170020. The goal of this project is to use new computational methods to understand sources of inter-patient variability in arrhythmias caused by pharmacological agents.
2005-2014: Sarcoplasmic reticulum calcium leak and calcium instability in cardiac myocytes; National Institutes of Health R01 HL 076230; The goal of this project is to investigate, through experiments and simulation, sources of unstable, potentially arrhythmogenic calcium release in cardiac myocytes.
2007-2012: Systems Biology Center in New York. National Institutes of Health P50 GM071558-01; Dr. Sobie leads one of the projects in this multi-investigator grant. We are developing methods for explicit multi-scale simulations of heart function.
Locations
Publications
Recent Artifacts
- Modeling the spatiotemporal properties of crosstalk between RYR and IP<sub>3</sub>R-mediated Ca<sup>2+</sup>release in failing cardiomyocytes
- Proteomic cellular signatures of kinase inhibitor-induced cardiotoxicity
- Time-regulated transcripts with the potential to modulate human pluripotent stem cell-derived cardiomyocyte differentiation
- Coupling and heterogeneity modulate pacemaking capability in healthy and diseased two-dimensional sinoatrial node tissue models
- Quantitative approaches to drug safety: The 2022 PSP special issue
- PPARdelta activation induces metabolic and contractile maturation of human pluripotent stem cell-derived cardiomyocytes
- Two heads are better than one: current landscape of integrating QSP and machine learning: An ISoP QSP SIG white paper by the working group on the integration of quantitative systems pharmacology and machine learning
- A library of induced pluripotent stem cells from clinically well-characterized, diverse healthy human individuals
- Plexin-B2 orchestrates collective stem cell dynamics via actomyosin contractility, cytoskeletal tension and adhesion
- Prediction of arrhythmia susceptibility through mathematical modeling and machine learning