Series of Three Lectures by


Eugene Shakhnovich,
Chemistry and Chemical Biology, Harvard University, 12 Oxford, Cambridge, MA 02138,

& invited professor at the Laboratory of Theoretical Physics at ENS,




Climbing the scales ladder in Biology:

from protein physics to population genetics and back.



Biological phenomena unfold in a broad range of scales ranging from molecules to cells to populations and ecosystems. Mutations affect the molecular properties of proteins and nucleic acids (abundance in the cytoplasm, thermodynamic stability, activity, interaction with other macromolecules in cytoplasm). Variations 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 mini-series of lectures I will outline my lab’s and others efforts in an emerging new field which merges molecular mechanism with evolution. The advances in this new field became possible due to recent spectacular progress in molecular biophysics, genomics, systems biology and population genetics. The research along these lines has the potential to transform our understanding of evolutionary dynamics from descriptive to quantitative and predictive with potential biomedical applications that extend from proactive treatment of infectious diseases to better modeling and treatment of cancer.


Lecture 1. Introduction to statistical mechanics of protein folding.

I will present the fundamental heteropolymer model of protein folding and its statistical mechanical analysis, which uncovered the energy gap criterion - the necessary and sufficient conditions for a heteropolymer sequence to encode a foldable protein. The analogy and fundamental differences between heteropolymer and spin glass models will be highlighted. 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.


Slides of lecture 1


Lecture 2.  Multiscale biophysical models of evolutionary dynamics.

In this lecture I will discuss 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 priori 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 of 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.


Slides of lecture 2


Lecture 3: Biophysical walks on fitness landscape: how a theorist became enchanted with experiment

Multiscale biophysical modeling crucially relies on assumptions about genotype-phenotype relationship (GPR). Fitness landscape (FL) is a common metaphoric description of GPR. However its precise nature is not known: ‘’Axes” on the pictorial presentations of FL remain unlabeled. In this lecture I will present experimental efforts, to outline FL in physical axes i.e. establish the link between changes in molecular properties of proteins and fitness for model living organisms. The effort encompasses multiple fields of experimental biology: molecular biophysics, genetics, genomics, proteomics and cell biology. The approach is bottom up and is based on our ability to edit genomes of bacterial and eukaryotic cells. Rational genetic variation is introduced on the chromosome of E. coli in the coding regions of essential genes dihydrofolate reductase (DHFR) and adenylate kinase (AdK). The mutant proteins are purified and their molecular properties are evaluated using the experimental tools of molecular biophysics. The fitness effects of rationally introduced mutations are determined using competition assays and are linked to known changes in molecular properties of proteins. As a complementary approach we use inter-species replacements of genes on E. coli chromosome, i.e. we replaced the gene encoding DHFR with same genes from various other bacteria. This approach allows one to explore a broader range of molecular variations inaccessible through point mutations. We show how genetic variation introduces major fitness barriers that can be overcome in evolutionary dynamics. Metabolomic, genomic and proteomic comparative analyses of genomically edited and wild-type strains highlights major elements in the GPR that encompasses molecular, system-level and organismal traits providing a crucial feedback for computational modeling.


Slides of lecture 3


Dates and place:


The lectures will be given in room 236 (29 rue d'Ulm) from 9 to 11am, on May 13, 20 and 27, 2015.