Inverse problems in statistical physics are motivated by the challenges of 'big data'in
different fields, in particular high-throughput experiments in biology. In inverse problems, the …

In the course of evolution, proteins undergo important changes in their amino acid
sequences, while their three-dimensional folded structure and their biological function …

Abstract Machine learning-based models of protein fitness typically learn from either
unlabeled, evolutionarily related sequences or variant sequences with experimentally …

The beginning of 2020 has seen the emergence of COVID-19 outbreak caused by a novel
coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). There is an …

The ability to design functional sequences and predict effects of variation is central to protein
engineering and biotherapeutics. State-of-art computational methods rely on models that …

Many high-throughput experimental technologies have been developed to assess the
effects of large numbers of mutations (variation) on phenotypes. However, designing …

The functions of proteins and RNAs are defined by the collective interactions of many
residues, and yet most statistical models of biological sequences consider sites nearly …

The quantitative characterization of mutational landscapes is a task of outstanding
importance in evolutionary and medical biology: It is, for example, of central importance for …

Many microbial populations rapidly adapt to changing environments with multiple variants
competing for survival. To quantify such complex evolutionary dynamics in vivo, time …

Machine learning has been proposed as an alternative to theoretical modeling when
dealing with complex problems in biological physics. However, in this perspective, we argue …