Past Event: Oden Institute Seminar

Learning biology from the information encoded in sequences

Ryan Cheng, Rice University

Abstract

Modern biology is characterized by the abundance of large sequence datasets, particularly genomic data, while structural data remains more limited. All of this one-dimensional sequence information can be leveraged to learn about diverse biological phenomena, ranging from the three-dimensional structure of large biomolecules to cell phenotypes. For example, one can predict the 3D structure of a protein or complex from coevolutionary information. At the systems level, one can gain insights on the interaction network of bacterial two-component signaling (TCS) by quantifying the determinants of interaction specificity and using this information to connect mutations to organism phenotypes. Similarly, we can once again exploit sequence information to study chromosomal organization. We show how it is possible to combine energy landscape theory with biochemical information to predict genome architecture using the sequences of epigenetic marking patterns that decorate DNA. Finally, we combine aspects of the first two examples to predict the bacterial condensin protein complex, which is one of the molecular determinants of genome architecture.