Mécanique statistique de la co-evolution immunitaire virale

par Jacopo Marchi

Projet de thèse en Physique

Sous la direction de Aleksandra Walczak et de Thierry Mora.

Thèses en préparation à Paris Sciences et Lettres , dans le cadre de École doctorale Physique en Île-de-France (Paris) , en partenariat avec LABORATOIRE DE PHYSIQUE THÉORIQUE DE L'ENS (laboratoire) et de Ecole normale supérieure (établissement de préparation de la thèse) depuis le 01-09-2016 .


  • Résumé

    L'objectif de ce projet est de construire un modèle statistique-théorique de la co-évolution du système immunitaire viral qui combine les interactions au niveau moléculaire pour prédire la réponse au niveau de la population. Le projet consiste à construire des outils d'inférence probabilistes pour quantifier la réponse des répertoires immunitaires aux vaccins. Les résultats de ces modèles seront utilisés pour proposer une théorie de l'évolution du répertoire basée sur les flux de fitness.

  • Titre traduit

    Statistical mechanics of viral-immune co-evolution


  • Résumé

    Organisms constantly evolve to find new solutions to existing challenges. The result of this evolution, the observed diversity of individuals, is shaped by many rare events and constrained by the environment and physical laws. Because of the non-equilibrium and stochastic nature of this ecosystem, rerunning the evolutionary process would most probably lead to a different observed diversity of individuals. Unfortunately, it is usually impossible to repeat the evolution experiment. The co-evolution of the adaptive immune repertoire and viruses offers a unique opportunity to learn about the fundamental rare-event forces that characterize life, thanks to the repeated co-evolution experiment simultaneously taking place in different individuals. The goal of this project is to build a statistical-theoretical model of viral-immune system co-evolution that combines the molecular level interactions to predict the population-level response. The project involves building probabilistic inference tools to quantify the response of immune repertoires to vaccines. The results of these models will be used to propose a theory of viral-immune repertoire evolution based on fitness fluxes. In this project we will use high throughput sequencing data from close collaborators to characterize the response of human repertoires to vaccines. The fitness flux approach is a formal non-equilibrium characterization of how the evolutionary landscape changes. Using Bayesian prediction methods combined with statistical biophysics models the project will use the analysis of experimental data to guide the building of effective Fokker-Planck equations for the evolution of the viral and immune populations. The results of these models will then be verified against existing data.