Quantifying the relationship between a biomolecule's genetic sequence and its biological function is a fundamental problem that addresses how complex molecules can evolve. Recent technologies have allowed the collection of large numbers of sequence-function pairs. However, exploiting this data for biophysical insight is challenging, as the data is a sparse sampling of the combinatorially large space of possible sequences. With statistical models inferred from these datasets, I show how a small number of intermediate molecular phenotypes can explain many aspects of sequence-function relationship. From a library of mutated promoter sequences and expression measurements in e.coli lac operon I infer detailed physical interactions between two regulatory proteins. Using a different heuristic approach, I infer fold stability from an thousands of mutated variants of green fluorescent protein, and show how enzymatic activity can be quantified in beta-lactamase. Finally, with a thermodynamic model I infer a detailed energy landscape of a small bacterial protein and…

The technological advances underlying the screening of large combinatorial libraries in high-throughput mutational scans deepen our understanding of adaptive protein evolution and boost its applications in protein design. However, many selection signals can be present in one experiment, entangled with the phenotype of interest. I will show how Restricted Boltzmann Machines (RBM), a powerful yet simple generative model from machine learning, can be trained on all the rounds of a sequence screening experiment, capturing an accurate map of the genotype-to-fitness relationship exercised in the experiment. We discuss how to integrate selections with more than one phenotype, and demonstrate that the model can disentangle multiple signals and predict multi-specific variants. The method fits remarkably well several datasets, with robustness to experimental and sampling noise. I will conclude with other applications of RBMs to screening data.

Like other processes across the sciences, micro-evolution is powered by the interaction of multiplicative growth (selection) and diffusive transport (mutations). In the presence of disorder, these opposing forces can generate localized structures and bursty dynamics, a phenomenon known as “intermittency” in non-equilibrium physics and as “punctuated equilibrium” in evolutionary theory. This behaviour is difficult to forecast; in particular there is no general principle to locate the regions where the system will settle, how long it will stay there, or where it will jump next. In this talk I will introduce a Markovian representation of growth-transport dynamics that closes these gaps. This “retrospective view” of evolution unifies the concepts of linear intermittency and metastability, and provides a generally applicable method to reduce, and predict, the dynamics of disordered linear systems. Applications range from Zeld’dovich’s parabolic Anderson model to Eigen’s quasispecies model of molecular evolution.