Inverse Problems in Biological Systems: From Data to Mechanism

Biological mechanisms often have to be reconstructed from indirect measurements such as microscopy images, gene activity and cellular time series. This workshop examines inverse problems in living systems, where noise, complexity and diversity challenge existing mathematical approaches. Mathematicians, physicists, computer scientists and biologists will explore methods including deep learning and models informed by physical constraints to infer hidden cellular, genetic and neural mechanisms from experimental data.