Biology is defined by non-linear reactions caused by numerous molecular components interacting inside living cells. The complexity of such systems has limited classical experimental approaches in their capacity to measure living biological networks. This talk will explore how new computational tools derived from artificial intelligence are currently applied to study complex biological networks in living systems. Specifically, I will show how deep-learning approaches can track protein networks in cells ranging from bacterial and fungal systems to cancerous growth. Our results provide a solution to study complex living systems and generate new data sets to reveal the yet-to-be-discovered mathematical principles of non-linear networks in biology.

Meeting ID: 924 1000 5098 Passcode: 288986