*Understanding evolution on multiple scales: from protein physics to population genetics* Prof. Eugene Shakhnovich When: Wednesday, March 23rd 4 p.m. Where: MIT Building 4, Room 163 Biological phenomena unfold in a broad range of scales ranging from molecules to cells to populations and ecosystems. Variation of molecular properties of biomolecules profoundly impact the ability of cells to survive and propagate (fitness). Finally, the fate of a mutation is decided by Darwinian selection on the level of the population, where three outcomes are possible: fixation in the population, elimination by purifying selection or separation in the population in a subdominant clone (polymorphism). In this lecture I will outline my lab’s and others efforts in an emerging new field which merges molecular mechanism with evolution. I will review basic concepts and models that contributed to our multiscale understanding of physical-chemical basis of biological phenomena. First, I will discuss physical chemistry of protein folding. I will present the fundamental heteropolymer model of protein folding and briefly outline the statistical mechanical analysis, which uncovered the energy gap criterion - the necessary and sufficient conditions for a heteropolymer sequence to encode a foldable protein. I will highlight the analogy and fundamental differences between heteropolymer and spin glass models. I will also discuss how understanding of basic principles of protein folding helps in our efforts to design new proteins and decipher the ‘’messages’’ hidden in multiple sequence alignment. I will then discuss the analogy between sequence selection for energy gaps and statistical mechanics of a class of generalized spin models. The statistical mechanical view of sequence selection enjoyed renaissance with the development of statistical methods to derive structural information about proteins from the analysis of variation in multiple sequence alignment. Finally I will discuss the relation between selection for foldable sequences and thermodynamic and kinetic mechanisms of protein folding such as first-order-like cooperativity. Next, I will present recent efforts at modeling evolutionary dynamics that merges molecular mechanisms with population genetics. Traditional population genetics models are agnostic to the physical-chemical nature of mutational effects. Rather they operate with an a prior assumed distributions of fitness effects (DFE) of mutations from which evolutionary dynamics are derived. Alternatively some population genetics models aim to derive DFE from evolutionary observations. In departure with this tradition the novel multiscale models integrate the molecular effects of mutations on physical properties of proteins into physically intuitive yet detailed genotype-phenotype relationship (GPR) assumptions. I will present a range of models from simple analytical diffusion-based model on biophysical fitness landscapes to more sophisticated computational models of populations of model cells where genetic changes are mapped into molecular effects using biophysical modeling of proteins and ensuing fitness changes determine the fate of mutations in realistic population dynamics. Examples of insights derived from biophysics-based multiscale models include the scale-free character of Protein Universe, the fundamental limit on mutation rates in living organisms, physics of thermal adaptation, co-evolution of protein interactions and abundances in cytoplasm and related results, some of which I will present and discuss. Finally I will briefly present “bottom-up: experimental efforts based on genome editing approaches to test basic assumptions of multiscale biophysics-based models of evolution.