Computational methods for mechanistic understanding of inter-sample variability The overall goal of this R21 application is to develop a computational framework that will allow for the prediction of physiological differences between experimental samples. Differences between individual samples can be catalogued and described at the physiological level, in terms of properties such as action potentials, and also at the molecular level, in terms of measurements such as gene expression. Linking variability at one level to variability at another level in a quantitative manner, however, is not straightforward. Here, through an innovative combination of experimental studies, mathematical modeling, and statistical analyses, we will develop methods that allow for molecular-level differences between samples to be translated into quantitative predictions of physiological differences. This Multiple Principal Investigator proposal utilizes the complementary expertise of the two PIs. Dr. Eric Sobie is expert in cardiac physiology, mathematical modeling, and computational approaches for understanding variability~ Dr. Christoph Schaniel is expert in stem cell biology, differentiation of pluirpotent cells into specific cell types, and high-throughput methods. The combined efforts of the two PIs will generate new quantitative data and will yield new computational methods that can be applied broadly to understand variability in different contexts. To achieve the overall project goals, we propose to: 1. Collect measurements of cardiac physiology and expression of relevant ion channels, pumps, and transporters. These measurements will be matched on a sample-by-sample basis. 2. Perform population-based simulations with dynamical mathematical models to develop quantitative and mechanistic predictions regarding how differences between samples in expression of important genes are translated into physiological differences. 3. Use regression-based statistical methods to analyze the experimental and simulation results, and to relate the two sets of predictions to each other. Not only is this exploratory research likely to provide insight into the physiology of cardiac myocytes derived from stem cells, it is also likely to demonstrate a novel computational framework that can be used for quantitative treatments of variability between samples in many biological contexts.
The goal of this project is to develop computational methods to that can be used to understand variability between individuals. Through a combination of experiments and mathematical modeling, we will develop methods that can be used to predict, for example, why some individuals respond to a particular treatment whereas others do not.
|Devenyi, Ryan A; Ortega, Francis A; Groenendaal, Willemijn et al. (2017) Differential roles of two delayed rectifier potassium currents in regulation of ventricular action potential duration and arrhythmia susceptibility. J Physiol 595:2301-2317|
|Devenyi, Ryan A; Sobie, Eric A (2016) There and back again: Iterating between population-based modeling and experiments reveals surprising regulation of calcium transients in rat cardiac myocytes. J Mol Cell Cardiol 96:38-48|
|Lancaster, M Cummins; Sobie, E A (2016) Improved Prediction of Drug-Induced Torsades de Pointes Through Simulations of Dynamics and Machine Learning Algorithms. Clin Pharmacol Ther 100:371-9|
|Lee, Young-Seon; Hwang, Minki; Song, Jun-Seop et al. (2016) The Contribution of Ionic Currents to Rate-Dependent Action Potential Duration and Pattern of Reentry in a Mathematical Model of Human Atrial Fibrillation. PLoS One 11:e0150779|
|Krogh-Madsen, Trine; Sobie, Eric A; Christini, David J (2016) Improving cardiomyocyte model fidelity and utility via dynamic electrophysiology protocols and optimization algorithms. J Physiol 594:2525-36|
|Mayourian, Joshua; Savizky, Ruben M; Sobie, Eric A et al. (2016) Modeling Electrophysiological Coupling and Fusion between Human Mesenchymal Stem Cells and Cardiomyocytes. PLoS Comput Biol 12:e1005014|