DSAI seminar with Alexander Gower

Abstract

Scientific discovery in biology is difficult due to the complexity of the systems involved and the expense of obtaining high quality experimental data. Automated techniques are a promising way to make scientific discoveries at the scale and pace required to model large biological systems. A key problem for 21st century biology is to build a computational model of the eukaryotic cell. The yeast Saccharomyces cerevisiae is the best understood eukaryote, and genome-scale metabolic models (GEMs) are rich sources of background knowledge that we can use as a basis for automated inference and investigation.

We present an overview of our experimental platform, the robot scientist Genesis, which is based around chemostat cultivation and high-throughput multi-omics. We then present LGEM+, a system for automated abductive improvement of GEMs consisting of: a compartmentalised first-order logic framework for describing biochemical pathways (using curated GEMs as the expert knowledge source); and a two-stage hypothesis abduction procedure.

This talk will also touch on the philosophical aspects of automating scientific discovery, and how we can think about this problem using machine learning paradigms.

About the speaker

Alexander is a WASP PhD student at Chalmers University of Technology and is working under the supervision of Professor Ross King. He read mathematics at the University of Oxford, graduating with an master's (MMath) in 2015. Alexander's research is mainly focused on developing AI systems for the automation of scientific discovery, with applications in systems biology.

This is a seminar from the DSAI seminars series usually held every Monday at 14:00 by the Data Science and AI division. The seminars are usually hybrid. No registration is required.