Abstract:
In Charles Darwin’s Origin of Species, the evolutionary history of species takes the shape of a tree. Ever since, trees have been a mainstay of evolutionary biology and, indeed, almost all of biology. However, genomic data is revealing a picture of evolution that is too complex to be captured by a single, simple tree. Horizontal gene transfer (HGT) is rampant in the bacterial world. Hybridization is estimated to be involved in the evolutionary history of over 25% of all plants. Gene flow has occurred between humans and Neanderthals and its extent in many groups of eukaryotic species, including our own order—the primates. 

In the presence of horizontal gene transfer, hybridization, or gene flow, the evolutionary history takes the shape of a rooted, directed, acyclic graph (rDAG), commonly referred to as ‘phylogenetic network.’  The main computational task in this field is inferring these phylogenetic networks (the rDAG structure itself as well as parameters associated with its nodes and edges) from genomic sequences that are available only for the leaves of the rDAG. Accurate reconstruction of these networks facilitates multiple biological tasks including, but not limited to, understanding the mode of speciation, adaptation to environmental changes (e.g., antibiotic resistance in bacteria), gene functions, and how traits evolve.    

The talk is divided into three parts. I will first describe models and optimization criteria of increasing complexity for phylogenetic networks. I will then describe methods for inferring phylogenetic networks under these models and criteria. Finally, I will discuss computational challenges that currently limit the scalability of phylogenetic network inference to large data sets. 

Bio: 
Luay Nakhleh is the William and Stephanie Sick Dean of Engineering and Professor of Computer Science at Rice University in Houston, Texas. He received a B.Sc. degree in Computer Science (1996) from the Technion (Israel), a master’s degree in Computer Science (1998) from Texas A&M University, and a Ph.D. degree in Computer Science (2004) from The University of Texas in Austin. He conducts research in the areas of bioinformatics and computational biology, focusing mainly on questions in evolutionary biology and genomics. Luay is a recipient of multiple awards including the Department of Energy CAREER award, the National Science Foundation CAREER award, the Sloan Fellowship, and the Guggenheim Fellowship. He was recently named Fellow of the International Society for Computational Biology.