Phenotypic models and automatic science for systems biophysics

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In this proposal, I extend my previous work on simulations of biological evolution and modelling of immune system to develop new strategies aiming at uncovering theoretical principles for biology, similar to what can be found in physics. I will use early immune recognition as the primary system of interest, but the methods developed will be general enough to be applied elsewhere.***I will first model collective decision of immune T cells to trigger immune response, using tools from computational and statistical physics, information and statistical decision theory. Then, I will study the related problem of model reduction, using our model of "adaptive sorting" for ligand recognition as a basis. The techniques used for model reductions are inspired by statistical physics, in particular renormalization, and can be automatically implemented numerically. I will combine these techniques with the numerical tools we have designed previously to develop an "automatized science" approach. The goal is to start with biological behavior to first generate classes of computational models predictive of biological systems, then reduce them automatically to simplified models from which we can extract simple biological principles. While this approach is theoretical, it is grounded in biological reality through present experimental collaborations. ***Our long term ambition is to produce a "theory of theory": we have built methods to generate predictive models of biological systems in the past, and this proposal is now concerned with the study of models generation itself and on the bridges we can build between models. There are two levels of impact: on the biology side, this will follow up on our previous work to provide much better fundamental understanding of immune strategies. On the theoretical side, our strategy will help figuring out how to go beyond the "big data/big model" paradigm currently used in so-called "systems biology" to extract predictive biological principles from very complex noisy biological datasets and experiments.**