Debora Marks

ISCB 2016 Overton Award Keynote

Ruth Nussinov

Department of Systems Biology, Harvard Medical School
Boston, United States

Presentation Title: Molecular structure and organism fitness from genomic sequences
Time: Sunday, July 10th, 4:40 pm - 5:40 pm
Introduction by: Alfonso Valencia
Room: Northern Hemisphere BCD, Dolphin Hotel

 

Abstract

The evolutionary trajectories of biological sequences are propelled by mutation and whittled away by selection to maintain function. Present day sequences can therefore be regarded as the outcomes of millions of evolutionary experiments that record functional constraints in the genotype-phenotype map. In this talk I will introduce computational methods that, when combined with recent growth in sequence databases, quantify evolutionary constraints in terms of evolutionary couplings between residues. We have applied these tools to predict (i) accurate 3D structures of proteins, RNA and complexes, (ii) conformational plasticity of ‘disordered’ proteins and (iii) quantitative effects of mutations on organism fitness. These computational approaches address the challenge of inferring causality from correlations in genetic sequences but can be applied more widely to other biological information such as gene expression or dynamics, cellular phenotypes or drug response. I will introduce challenges and opportunities for extending these methods to diverse challenges in biomedical and engineering applications.

Biography:

Debora is a mathematician and computational biologist with a track record of using novel algorithms and statistics to successfully address unsolved biological problems. She has a passion for interpreting genetic variation in a way that impacts biomedical applications. 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 now developing the algorithms to use in the quantitating the effects of genetic variants, including those involved in antibiotic resistance