Heart failure (HF) is a nationwide epidemic with over 6 million afflicted patients and 600,000 new patients diagnosed each year. Ischemic heart disease continues to be the leading cause of death in the United States. Over the past 16 years, cardiac resynchronization therapy (CRT) has been shown to increase LV performance, quality of life, and overall survival in a large number of (ischemic and non-ischemic) HF patients. Approximately 30% of patients, however, still do not improve after therapy (CRT non-responders) and the percentage of CRT non-responders have remained stable over the past decade. We believe that one of the critical barrier in improving CRT responder rate is the lack of an understanding of the interactions between ischemia and asynchronous activation. In this proposal, we seek to close this gap by using a multi-disciplinary approach that combines large-animal experiments and validated computational modeling. The overall goal of this proposal is to develop an experimentally validated, physics-based cardiac electro-mechanics-perfusion (EMP) computational (finite element, FE) model to predict and optimize CRT response under ischemic conditions. The following specific aims are constructed to accomplish this goal. First, we will couple a cellular-based electromechanical model of the heart to a circulation model of the coronary vasculature that will be validated using experimental measurements in normal pigs. Second, we will validate the EMP model against pig model of acute ischemia and pseudo left bundle branch block (LBBB) to elucidate how the interactions between asynchronous activation and ischemia can affect CRT response. Third, we will use the validated EMP model to optimize CRT by identifying optimal pacing parameters associated with the degree and location of ischemia. The proposed approach and methodologies are innovative. More importantly, the completion of this project will significantly increase our understanding on the interactions between ischemia and asynchronous activation, and how these interactions can affect CRT response. The findings of this project is translational and can serve as a foundation for future development of patient-specific methodologies to optimize long-term CRT response.