COMPUTATIONAL RESEARCH in BOSTON and BEYOND (CRIBB)

Date	May 4, 2018
Speaker	Adam Riesselman Harvard University
Topic	Predicting the effect of mutations with generative models of evolutionary sequences
Abstract	Modern genome sequencing and synthesis can acquire and generate tremendous molecular diversity in a day, but our ability to navigate and interpret the exponentially large space of potential biological sequences remains limited. Central to this challenge is the lack of a priori knowledge about epistasis, i.e. non-additive interactions between positions in a molecule or genome. I will describe how generative models fit to evolutionary sequences can be used to help explain these factors. I will then discuss two classes of generative models, discrete undirected graphical models and neural-network powered latent variable models, which can reveal the three dimensional structures and mutational landscapes of proteins and RNA solely from evolutionary information.

Acknowledgements

We thank the generous support of MIT IS&T, CSAIL, and the Department of Mathematics for their support of this series.