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: "Network-based Approaches to Identification of Important Genes and Proteins"
Abstract: It is crucial in analysis of biological networks to identify important genes and proteins such as biomarkers and driver genes. Various methods have been developed and utilized for this purpose. Graph theoretic approaches are major ones and such concepts as maximum matching, minimum dominating set (MDS), and feedback vertex set (FVS) have been utilized. However, weights are not taken into account in these approaches. Therefore, we extended MDS and FVS so that weights of edges and nodes are taken into account. In particular, we extended MDS to PMDS (Probabilistic MDS) so that weights can be given to edges in the form of failure probability. We also extended MFVS (minimum feedback vertex set) to WMFVS (weighted minimum dominating set) so that weights can be given to nodes. We applied PMDS to analysis of metabolic networks and both of PMDS and WMFVS to analysis of protein interaction networks.
Prof. Luonan Chen
Chinese Academy of Sciences, China
Luonan Chen received BS degree in the Electrical Engineering, from Huazhong University of Science and Technology, and the M.E. and Ph.D. degrees in the electrical engineering, from Tohoku University, Sendai, Japan, in 1988 and 1991, respectively. From 1997, he was an associate professor of the Osaka Sangyo University, Osaka, Japan, and then a full Professor. Since 2010, he has been a professor and executive director at Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences. He was the founding director of Institute of Systems Biology, Shanghai University. He was elected as the founding president of Computational Systems Biology Society of OR China, and Chair of Technical Committee of Systems Biology at IEEE SMC Society. In recent years, he published over 350 journal papers and two monographs (books) in the area of bioinformatics, nonlinear dynamics and machine learning.
Speech Title: "Network Biomarker for Disease Diagnosis and Dynamic Network Biomarker for Disease Prediction"
Abstract: We defined two new types of biomarkers to quantify the states of biological systems based on network, in contrast to the traditional molecular biomarkers. Network biomarker is constructed to quantify regular state of a biological system, while dynamic network biomarker is to quantify the critical state or tipping point of a biological system. (1) Network biomarker (NB) is a subnetwork or network module, which is composed of a number of associations or regulations between molecules (or variables), rather than simply a number of molecules. Those associations (the second-order statistics) in the module are formed collectively as a biomarker, thus robustly and accurately quantifying the regular state of a biological system, completely different from the concentrations of conventional molecular biomarkers (the first-order statistics). (2) Dynamic network biomarker (DNB) is a subnetwork or module, and is also composed of a number of associations or regulations between molecules but with three statistical conditions (in terms of variances and covariances), which are actually a number of strongly and collectively fluctuated molecules in the network. Theoretically, DNB is able to quantify the critical state or the tipping point of a biological system, thereby serving as a general early-warning signal to indicate an imminent state transition. A number of real datasets are provided to validate the effectiveness of NB and DNB.