Biophysics in latent space

Link to join online: https://sfu.zoom.us/j/85265469079?pwd=Z0F5SmlKczhJTHVsUlc3d0xzcFo1QT09

Synopsis

Many phenomena in biology are considered too complicated or too contingent to be captured by predictive theories similar to what is done in physics. But complex systems theory has taught us that simple, higher level laws with few effective parameters can emerge from the interaction of small scale components. As biology is becoming more and more quantitative, one can use a combination of first-principle theoretical modelling with simple machine learning techniques to build accurate and tractable theories of biological dynamics. Those dynamics can often be best understood in (abstract) latent spaces, giving « physics-like » intuition. I will illustrate the power of such approaches on a couple of biological examples, with a special focus on the dynamics of the adaptive immune system (T cells response). Our approach leads to applications in cancer immunotherapy that I will briefly describe.