We combine massively parallel perturbation experiments with modelling and machine learning to understand, predict and engineer the fundamental sequence-to-activity encoding functions of molecular biology.

The goal is to make molecular biology and genetics predictive and programmable, and we are also building reference atlases and methods for the interpretation of genetic variation. Our focus is currently on the biophysical properties of proteins, RNAs and their interactions and we combine ‘deep’ DNA mutagenesis/synthesis with selection and sequencing experiments to generate datasets of sufficient size and diversity to build predictive and, where possible, mechanistic models. Topics of interest include protein stability, binding affinity, aggregation and allostery, genetic interactions/epistasis, splicing, gene expression, and human genetics, particularly cancer.

https://www.crg.eu/ben_lehner

https://www.sanger.ac.uk/group/lehner-group/

https://www.sanger.ac.uk/person/lehner-ben/