Adam Riesselman from Insitro
In this video from DOE CSGF 2019, Adam Riesselman from Insitro presents: Reasoning About Biology With Data-Driven Approaches.
Biology has traditionally been a low-throughput science, where gleaning insights into processes has been slow and expensive. However, with advances in DNA synthesis and sequencing, as well as high-content imaging, biology is quickly becoming a data-driven discipline, in which thousands of biological hypotheses can be answered in a single test tube. I will first highlight the technologies that have enabled this revolution. I will then discuss these advances in the context of understanding natural genetic variation with computational models and their application in predicting the effects of mutations and designing new sets of sequences with desirable properties.
Adam Riesselman is a computational biologist with experience in developing powerful, interpretable machine learning models for complex biological data. At insitro, Adam is focused on integrating high-throughput measurements with new scalable algorithms to understand disease. Adam received a BA in Biochemistry: Cell and Molecular Biology from Drake University and his PhD in Biomedical Informatics at Harvard University with Debora Marks as a Department of Energy Computational Science Graduate Fellow. There he developed new statistical models for unsupervised mutation effect prediction from evolutionary data, de novo protein structure prediction via simulation, protein library design with improved biomolecular properties, and small molecule production optimization utilizing biosynthetic pathway engineering.
