Our group develops computational methods for understanding the dynamics, interactions and conservation of complex biological systems. As new high-throughput biological data sources become available, they hold the promise of revolutionizing molecular biology by providing a large-scale view of cellular activity. However, each type of data is noisy, contains many missing values and only measures a single aspect of cellular activity. Our computational focus is on methods for large scale data integration. We primarily rely on machine learning and statistical methods. Most of our work is carried out in close collaboration with experimentalists. Many of the computational tools we develop are available and widely used.
Communication of the ACM (CACM), the top and most widely read computer science journal publishes a paper we wrote on biological distributed computing as the cover story for their first issue of 2015. The paper summarizes several recent studies by us and others in this area. With the success of the BDA workshop we recently organized it seems that this idea is catching up and more and more people are looking into biological systems to gain insights into the design of complex distributed networks and systems. See also an interview CACM recorded with co-author Saket Navlakha discussing the area and the paper.
Three group members accept new faculty positions. Current group members Saket Navlakha (postdoc MLD) and Xin He (Lane fellow and postdoc, co advised with K. Roeder) and former group member Tony Gitter (PhD 2012) will start as Assistant Prof. in the Fall. Saket is joining the Salk Institute for Biological Studies in San Diego. Xin will join the Human Genetics department at the Universtiy of Chicago and Tony will be joining Department of Biostatistics & Medical Informatics and the Morgridge Institute for Research at the University of Wisconsin, Madison. The diversity in terms of types of the departments each is joining indicates the growing importance of computational biology across areas ranging from basic science to clinical studies to statistics and computer science. Congratulations and good luck to all!
Yeast networks provide insights for improving computational network security . In a new paper in the journal Proceeding of the Royal Society Interface we discuss how lessons from yeast and other biological networks can be used to design and evaluate secure communication networks. The work has been highlighted by a CMU press release and a few other venues ( dataconomy , Campus Technology )