Cluster Project 3

Integrative Modeling of Mechanisms of Sudden Cardiac Death

Aim 3.1: Develop an integrative metabolic and electrophysiological model of the guinea pig ventricular mycoyte. This aim will extend a previously developed GP model of mitochondrial energetics4 (Appendix 1B), integrated with excitation-contraction coupling and isometric force generation6 (Appendix 1C) to include a model of oscillatory mitochondrial ROS-induced ROS release5 (Appendix 1D). Data on altered expression of the mitochondrial sub-proteome available from NHLBI-NO1-HV-28180 will also be used to constrain the model. Functional significance of these proteomic changes will be assessed using both metabolic control analysis and whole-cell simulations. Hypotheses regarding mechanisms of metabolic oscillations emerging from the model will be tested against experimental data collected in CP1.

Aim 3.2: Develop a computational model of conduction in the GP ventricle and use the model to test the hypothesis that mitochondrial depolarization and the formation of metabolic sinks contributes to the formation of regions of slowed or no conduction and promotes formation of reentrant arrhythmias. Develop preparation-specific models of electrical conduction in the GP ventricles studied in Aim 1.1 describing: a) cell properties using the GP ventricular myocyte model developed in Aim 3.1; b) geometry and fiber structure of each heart as measured using the SHG imaging data collected in CP1 Aim 1.3; and c) the spatial extent of mitochondrial depolarization in each heart using the image data collected in Aim 1.2. The models will be used to test the hypotheses that regional mitochondrial depolarization: a) through formation of electrically in-excitable zones,; and/or b) by increasing regional dispersion of APD and repolarization properties; promotes the formation of reentrant arrhythmias. This will be done by developing preparation-specific heart models describing location of metabolic sinks, using these models to predict conduction patterns in regions near the metabolic sinks, and then comparing model predictions to the experimental data obtained in CP 1.

Aim 3.3: Develop an integrative metabolic and electrophysiological model of the normal and failing canine ventricular myocyte: We will extend existing computational models of the normal and failing canine LV myocyte15, 23 (Appendix 1E) to describe metabolism and it’s coupling to electrophysiological processes by incorporating models of ATP production4, coupling to energy requiring cellular processes6 and ROS-induced ROS release5. The model of the failing canine LV myocyte15, 23 will be parameterized by incorporating information on relative changes in the expression of the canine mitochondrial sub-proteome in end-stage HF vs control using data measured in CP2. The models will be used to predict whether or not canine ventricular myocytes are more susceptible to metabolic oscillation in the setting of heart failure than under normal conditions, and to determine the mechanisms underlying these differences in ROS-induced cellular oscillations. Aim 2.2 of CP2 will then test these model predictions experimentally.

Aim 3.4: Develop a metabolically, biophysically and anatomically detailed computational model of transmural conduction in the normal and failing canine LV wedge: 3.4A Using the micro-anatomic structural data obtained with two-photon SHG imaging in CP1 and the cell models developed in Aim 3.3, we will develop a metabolically, biophysically and anatomically detailed computational model of electrical conduction in the normal and failing coronary-perfused canine LV wedge preparation. We will incorporate within this model data on transmural variation of ion channel, excitation-contraction coupling and connexin protein expression density already available from prior and ongoing experimental studies. 3.4B We will test the ability of this model to reconstruct and predict electrical activation, repolarization and CaTs measured optically in the normal and failing canine wedge preparation. We will test the hypothesis that failing myocardium exhibits increased susceptibility to metabolic stress and to formation of metabolic sinks. We will test the hypotheses of arrhythmia generation described above. Model predictions will be compared against experimental data in CP2 Aim 2.3.