UBC Math Bio Seminar: Paul Francois

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 modeling with 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, interpretability and eventually allowing for new predictions and applications. I will illustrate the power of such approaches on the dynamics of the adaptive immune system, in particular T cell response. We used a robotic platform combined with machine learning to uncover a 'Universal encoding' from cytokine dynamics, and I will show how this response structure fits parsimonious models of immune recognition. Our approach suggests a new ‘antagonistic’ strategy for cancer immunotherapy that we validated both in vitro and in vivo.