Prof. Ying Xu
University of Georgia, USA
Ying Xu has been the "Regents and Georgia Research Alliance Eminent Scholar" Chair of bioinformatics and computational biology and Professor in Biochemistry and Molecular Biology Department since 2003, and was the Founding Director of the Institute of Bioinformatics, the University of Georgia (UGA). He received his Ph.D. degree in theoretical computer science from the University of Colorado at Boulder in 1991. He started his bioinformatics career in 1993 when he joined Oak Ridge National Lab. His current research interests are in cancer bioinformatics and systems biology, particularly in cancer metabolism. He has over 300 publications, including five books, with total citations more than 13,000 and H-Index = 62; and has given over 250 invited/contributed talks at conferences, research organizations and universities.
Prof. Tatsuya Akutsu
Kyoto University, Japan
Tatsuya Akutsu received B.Eng. and M.Eng. in Aeronautics and D.Eng. in Information Engineering from University of Tokyo, in 1984, 1986 and 1989, respectively. From 1989 to 1994, he was with Mechanical Engineering Laboratory. From 1994 to 1996, he was an Associate Professor in the Department of Computer Science at Gunma University. From 1996 to 2001, he was an Associate Professor in Human Genome Center, Institute of Medical Science, University of Tokyo. Since 2001, he has been a Professor in Bioinformatics Center, Institute for Chemical Research, Kyoto University. He is a fellow of Information Processing Society of Japan (IPSJ), and was an editor-in-chief of IPSJ Transactions on Bioinformatics for 2006-2009. His research interests include bioinformatics, complex networks, and dicrete algorithms.
Speech Title: "Graph Theoretic Approaches to Analysis and Control of Biological Networks"
Abstract: Development of control theory for biological systems is one of major goals in systems biology and bioinformatics. Recently, several graph theoretic concepts have been utilized for finding driver nodes that can control the entire state of a network. In particular, maximum matching (MM), minimum dominating set (MDS), and feedback vertex set (FVS) have been widely utilized. In this talk, we briefly review and conceptually compare these approaches. Then, we present various extensions of the MDS-based method that have been developed by us. Although it is difficult to directly apply graph theoretic approaches to control of real biological systems. they are useful for finding important genes and proteins. Indeed, MDS has been applied to analysis of various kinds of biological networks by us and others, which include protein-protein interaction networks, drug-target protein network, non-coding RNA-protein network, and cancer metabolic network. We overview these applications too. This talk is based on joint work with Jose Nacher in Toho University.