Inference and modeling of biological networks : a statistical-physics approach to neural attractors and protein fitness landscapes

Abstract : The recent advent of high-throughput experimental procedures has opened a new era for the quantitative study of biological systems. Today, electrophysiology recordings and calcium imaging allow for the in vivo simultaneous recording of hundreds to thousands of neurons. In parallel, thanks to automated sequencing procedures, the libraries of known functional proteins expanded from thousands to millions in just a few years. This current abundance of biological data opens a new series of challenges for theoreticians. Accurate and transparent analysis methods are needed to process this massive amount of raw data into meaningful observables. Concurrently, the simultaneous observation of a large number of interacting units enables the development and validation of theoretical models aimed at the mechanistic understanding of the collective behavior of biological systems. In this manuscript, we propose an approach to both these challenges based on methods and models from statistical physics. We present an application of these methods to problems from neuroscience and bioinformatics, focusing on (1) the spatial memory and navigation task in the hippocampal loop and (2) the reconstruction of the fitness landscape of proteins from homologous sequence data.
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Lorenzo Posani. Inference and modeling of biological networks : a statistical-physics approach to neural attractors and protein fitness landscapes. Physics [physics]. PSL Research University, 2018. English. ⟨NNT : 2018PSLEE043⟩. ⟨tel-02280155⟩

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