Fusing mechanistic networks and machine learning to control heart growth

While the growth of the heart (hypertrophy) aids normal development and adaptation to exercise, paradoxically it is also a leading contributor to the progression of heart failure. This paradox persists because cardiac hypertrophy is controlled by complex, only partially characterized networks of signaling and gene regulation, and because these networks have largely been studied by reductionist rather than systems-level approaches. In this presentation, I'll discuss our methods that combine mechanistic network modeling with machine learning to systematically develop and expand computational models of the heart’s regulatory networks, enabling new understanding and opportunities to target drugs against heart disease. **Sponsor**: This seminar is sponsored by the Network and Graph Data Science Research Interest Group.