Kazuyuki Aihara (University of Tokyo, Japan)
Kazuyuki Aihara received the B.E. degree of electrical engineering in 1977 and the Ph.D. degree of electronic engineering 1982 from the University of Tokyo, Japan.
Currently, he is Professor of Institute of Industrial Science, Professor of Graduate School of Information Science and Technology, Professor of Graduate School of Engineering, and Director of Collaborative Research Center for Innovative Mathematical Modelling, all in the University of Tokyo.
His research interests include mathematical modeling of complex systems, parallel distributed processing with spatio-temporal chaos, and time series analysis of complex data.

Title: An Application of Hybrid Dynamical Systems Modelling to Personalized Hormone Therapy of Prostate Cancer

Abstract: In this keynote lecture, I will review hybrid dynamical systems modeling [1] and our research on its application to personalized hormone therapy of prostate cancer [2-7]. First, I review recent progress of hybrid dynamical systems theory and its applications to biological and medical systems[1]. Then, I introduce our application of hybrid dynamical systems modelling to personalized intermittent hormone therapy of prostate cancer [2-7]. In particular, I show that we can make a personalized mathematical model for each patient only from observed time series data of serum PSA (Prostate-Specific Antigen) and optimize the schedule of intermitten hormone therapy on the basis of the mathematical modelling.

References

(1) Ed. by K.Aihara: A theme issue on theory of hybrid dynamical systems and its applications to biological and medical systems, Philosophical Transactions of the Royal Society A, Vol.368, No.1930(2010).
(2) A.M. Ideta, G. Tanaka, T. Takeuchi, and K. Aihara: J. Nonlinear Science, Vol.18, pp.593-614 (2008).
(3) G. Tanaka, K. Tsumoto, S. Tsuji¡¡èK. Aihara: Physica D, Vol.237, No.20, pp.2616-2627 (2008).
(4) T. Shimada and K. Aihara: Mathematical Biosciences, Vol.214, No.1/2, pp.134-139 (2008).
(5) Y. Tao, Q. Guo, and K. Aihara, J. Nonlinear Science, Vol.20, pp.219-240(2010).
(6) Y.Hirata, N.Bruchovsky, and K.Aihara, J. Theoretical Biology, Vol.264, pp.517-527 (2010).
(7) T. Suzuki, N. Bruchovsky, and K. Aihara:Phil. Trans. R. Soc. A, Vol.368, No.1930, pp.5045-5059(2010).

Ruth Nussinov (National Cancer Institute, USA)
Ruth Nussinov received her Ph.D. in 1977, from the Biochemistry Department at Rutgers University and did post-doctoral work in the Structural Chemistry Department of the Weizmann Institute.  Subsequently she was at the Chemistry Department at Berkeley, the Biochemistry Department at Harvard, and the NIH.  In 1984 she joined the Department of Human Genetics, at the Medical School at Tel Aviv University. In 1985, she accepted a concurrent position at the National Cancer Institute of the NIH, SAIC-Frederick, where she is a Senior Principal Scientist and Principle Investigator heading the Computational Structural Biology Group at the NCI. She has authored over 350 scientific papers. She is the Deputy Editor-in-Chief in PLoS Computational Biology and Associate Editor and on the Editorial Boards of a number of journals, including the Biophysical Journal, Proteins, Physical Biology, BMC Bioinformatics, and the J. Molecular Recognition, a long term member of an NIH Study Section (MSFD), and a frequent speaker in Domestic and International meetings, symposia and in numerous academic institutions. Her interests largely focus on protein folding and dynamics, protein-protein interactions, binding mechanisms and regulation, amyloid conformations and toxicity, and large multi-molecular associations with the ultimate goal of understanding the protein structure-function relationship. Among her accomplishments are the dynamic programming algorithm for the prediction of the secondary structure of RNA (1978); pioneering work in DNA sequence analysis (already in 1980); the proposition of the conformational selection and population shift as an alternative binding mechanism to induced fit and the role of conformational ensembles in protein function (1999) including protein allostery (2004). Her National Cancer Institute website gives further details.
http://ccr.cancer.gov/Staff/Staff.asp?profileid=6892

Title: Multi Scale Combinatorial Docking of the Proteome for Functional Predictions

Abstract: Construction of the structural protein interaction network is of crucial importance since it is a prerequisite for understanding how the proteome and thus the cell function. Yet, predicting, on the proteome scale, which proteins interact and how they interact is a daunting task. Structural predictions of protein interactions are frequently carried out via docking. However, in the absence of additional biochemical data, docking is challenging on the proteome scale because there are many favorable ways for proteins to interact. An alternative strategy is knowledge-based, using a protein-protein interface dataset. Using such a dataset is efficient because the number of architectures, in single chain proteins and in protein-protein interfaces is limited in nature, and structurally different protein pairs can use the same (preferred) binding architectures. This suggests that using structural alignment of each side of known interfaces against the entire surfaces of all monomers can predict protein associations: a protein whose surface matches one side of the interface can bind a protein whose surface matches the complementary side. Yet, on their own knowledge-based methods may not be sufficient for proteome modeling because they disregard flexibility and energetics. Now, for the first time, we combine the two methods, leading to a powerful combinatorial multi-scale strategy to predict functional associations of the proteome.

(Joint work with Nurcan Tuncbag, Attila Gursoy, and Ozlem Keskin)

Erik van Nimwegen (University of Basel, Switzerland)
Dr. van Nimwegen studied theoretical physics at the University of Amsterdam, the Netherlands, finishing in 1995 with a master's project on black hole entropy under the supervision of Prof. R. Dijkgraaf. For his PhD studies Dr. van Nimwegen joined the Santa Fe Institute in Santa Fe, New Mexico, studying theoretical evolutionary dynamics with Jim Crutchfield, while Paulien Hogeweg at the University of Utrecht, the Netherlands, acted as his official supervisor. His PhD worked focused on theories of metastable evolutionary dynamics and the role of neutral networks in evolution, earning his PhD, cum laude, from the University of Utrecht in 1999. After a post-doctoral stay at the  Santa Fe Institute, Dr. van Nimwegen became a fellow at the Center for Studies in Physics and Biology  of the Rockefeller University in 2000. At Rockefeller the focus of his research shifted toward analysis of genomic data, genome evolution, and the reconstruction of transcriptional regulatory networks. In 2003 he became assistant professor for computational systems biology at the Biozentrum of the University of Basel and associate professor in 2008. His research focuses on the investigation of quantiative laws in genome evolution, and in the development of probabilistic methods for reconstructing regulatory networks from combinations of high-throughput biological data.

Title: Motif Activity Response Analysis: Inferring Genome-wide Transcription Regulation in Mammals

Abstract: I will discuss an integrated computational approach, called motif activity response analysis (MARA), for reconstructing transcription regulatory networks in mammals from genome-wide expression data. Based on deep sequencing data of transcription start sites we obtained a comprehensive 'promoteromes' in human and mouse, and using probabilistic comparative genomic methods we predict binding sites for over 200 regulatory motifs in proximal promoters genome-wide. Motif Activity Response Analysis (MARA) models genome-wide gene expression profiles in terms of these predicted regulatory sites and I will describe how MARA identifies, for a given system of study, the key regulators driving expression changes, their activity profiles across the samples, and the sets of target promoters of each regulator. Time permitting I will talk about how MARA can be extended to incorporate epigenetic changes to chromatin structure.

Dong Xu (University of Missouri, USA)
Dong Xu is James C. Dowell Professor and Chair of Computer Science Department, with appointments in the Christopher S. Bond Life Sciences Center and the Informatics Institute at the University of Missouri. He obtained his Ph.D. from the University of Illinois, Urbana-Champaign in 1995 and did two-year postdoctoral work at the US National Cancer Institute. He was a Staff Scientist at Oak Ridge National Laboratory until 2003 before joining University of Missouri. His research includes protein structure prediction, high-throughput biological data analyses, in silico studies of plants, microbes, and cancers. He has published more than 160 papers. He is a recipient of 2001 R&D 100 Award, 2003 Federal Laboratory Consortium's Award of Excellence in Technology Transfer, and 2009 Outstanding Achievement Award from International Society of Intelligent Biological Medicine. He is an Editor in Chief of International Journal of Functional Informatics and Personalised Medicine. He is an Editorial Board member of Current Protein and Peptide Science, Applied and Environmental Microbiology, and International Journal of Data Mining and Bioinformatics. He is a standing member of the NIH Biodata Management and Analysis Panel.

Title: MUFOLD for Protein 3D Structure Prediction: A New Solution to An Old Challenge

Abstract: Knowledge of the three-dimensional structure of a protein often provides a basis for understanding its function. However, the gap between numbers of known protein sequences and structures has been dramatically increasing. One important approach to bridge this gap is computational prediction of protein structure from sequence. There have been steady improvements in protein structure prediction during the past two decades. However, current methods are still far from consistently predicting structural models accurately, especially with computing power accessible to common users. Towards achieving more accurate and efficient structure prediction, we developed a dramatically different framework from conventional methods for protein structure prediction. The framework is implemented into a software system MUFOLD, which integrates a number of novel methods. First, MUFOLD has a systematic protocol to identify useful templates and fragments from Protein Data Bank (PDB) for a given target protein. Next, an efficient process was applied for iterative coarse-grain model generation and evaluation. In this process, MUFOLD applies Multidimensional Scaling to construct multiple models by sampling inter-residue spatial restraints derived from alignments. It then evaluates models through clustering and a machine learning based scoring function, and iteratively improves selected models by integrating spatial restraints and previous models. Finally, the models were evaluated using molecular dynamics simulations based on structural changes under simulated heating. The computing time of MUFOLD is much shorter than most other tools for protein structure prediction. MUFOLD demonstrated its success in the community-wide experiment for protein structure prediction CASP.

Ya-ping Zhang (Kunming Institute of Zoology, Chinese Academy of Sciences, China)
Dr. Ya-Ping Zhang has been a foremost leader in genetic diversity and molecular evolution in China. He has established one the biggest animal DNA bank, which provided essential tools that facilitate scientific studies of genetic diversity in Asia. His contribution for understanding domestication of animals is noticeable. His extensive studies on genetic diversity of domestic animals found that domestication process is much complicated, and the South-east Asia is an important domestication center for some domestic animals. He has done extensive work on molecular evolution of genes which shed lights for understanding natural selection and genetic basis of animal adaptation. Because of his contribution for understanding the patterns and the underlying evolutionary principles of biodiversity, he was awarded Biodiversity Leadership Awards in 2002. More than 200 original papers have been published in peer review journals, including Nature, Science, PNAS, Mol Biol Evol, Mol Phylogent Evol, Am J Hum Genet, Genetics, Genomics Res. His current research interests also include following areas: (1) molecular phylogeny of animals, (2) population genetics and conservation genetics of animal species in China, (3) genetic diversity and evolution history of human populations in East Asia.

Title: Locomotion is an Important Factor Influence Evolution of Energy Metabolism Genes

Abstract: For the past 20 years, mtDNA has commonly been considered to be a neutral marker and widely applied in evolutionary and conservation studies. However, mitochondria produces, via the respiratory chain, 95% of the adenosine triphosphate (ATP) needed for locomotion, we speculated that locomotion may be an important factor influence evolution of energy metabolism genes. Since the respiratory chain has a dual genetic foundation, with genes encoded by both the mitochondrial and nuclear genomes, therefore we examined both genomes to test this hypothesis. Cetaceans and bats incur higher energy costs for locomotion than their terrestrial ancestors. We found convergent pattern of adaptive evolution of energy metabolism genes in cetaceans and bats. Furthermore, a negative correlation was found for the Ka/Ks ratio of mitochondrial DNA and locomotive speed in mammals, birds, and fishes. Those results suggest that locomotion is an important factor influence evolution of energy metabolism genes.

(Joint wotk with Yong-Yi Shen)