Hosted by the School of Medicine at Cardiff University

There’s now an amazing opportunity to accelerate discovery across important 21st century challenges by using computation tightly coupled to biological experiments and clinical medicine. I will describe some recent approaches from my lab for these challenges where we have developed new machine learning methods that can exploit the enormous natural sequence diversity and our ability to sequence DNA at scale. To demonstrate the power of these new approaches I will present recent work predicting the effects of human genetic variation on disease and drug response, anticipation of viral escape from the host immune system for vaccine design, protein design for enzyme optimization, antibodies and sustainable biomaterials.

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Debora S. Marks is a Professor of Systems Biology at Harvard Medical School. She is a computational biologist with a track record of using novel algorithms and statistics to successfully address unsolved biological problems. During her PhD, she quantified the potential pan-genomic scope of microRNA targeting and combinatorial regulation of protein expression and co-discovered the first microRNA in a virus. As a postdoc she and her colleagues cracked the classic, unsolved problem of ab initio 3D structure prediction of proteins using a maximum entropy probability model for evolutionary sequences. She has developed this approach to determine functional interactions, biomolecular structures, including the 3D structure of RNA and RNA-protein complexes and the conformational ensembles of apparently disordered proteins. Her new lab at Harvard is interested in developing methods in deep learning to address a wide range of biological challenges including predicting the effects of genetic variation and sequence design for biosynthetic applications.

She was awarded the Overton Prize by the International Society for Computational Biology and was elected as a Fellow of the International Society for Computational Biology.