Keynotes

1. Professor Haruki Nakamura (Osaka University, Japan) (Keynote 1 - pdf)

Biography: Professor Haruki Nakamura was awarded his Doctor degree of Science in Physics from the University of Tokyo , Tokyo , Japan in 1980 and joined the Department of Applied Physics, Faculty of Engineering, the University of Tokyo, Japan, where he made several researches of biological liquid crystals and of electrostatic properties of protein molecules. In 1985, he visited the Astbury Department of Biophysics, Leeds University , UK , and learned the technology of protein design. He then moved to Protein Engineering Research Institute (PERI) in 1987, at Osaka , and became a Research Director of Second department, where computational design and analyses of the engineered proteins were made. He continuously worked as a Research Director of Department of Bioinformatics at Biomolecular Engineering Research Institute (BERI) from 1996 to 1999. In April, 1999, Dr. Haruki Nakamura moved to Osaka University and became Professor of Laboratory of Protein Informatics, Research Center for Structural Biology, Institute for Protein Research. He also founded the Protein Data Bank Japan (PDBj) in 2001, granted by Japan Science and Technology Agency (JST), and is a member of the wwPDB, which was founded in 2003. He is now a Head of Research Center for Structural and Functional Proteomics, Institute for Protein Research, Osaka Univ. Dr. Haruki Nakamura has published over 200 papers. He is a board member of Protein Engineering, Design and Selection (PEDS). He is currently a board member of Protein Science Society of Japan, and a member of Biophysical Society of Japan, The Molecular Biology Society of Japan, The Physical Society of Japan, and The Society of Polymer Science, Japan .

His websites are at:

http://www.protein.osaka-u.ac.jp/rcsfp/pi/

http://www.pdbj.org/

Topic : Protein functional annotation from pattern recognition for 3D structures:
Similarity search of protein folds, local atomic arrangements, and molecular surfaces with physicochemical properties

Abstract : The Protein Data Bank Japan (PDBj) curates, edits and distributes protein three-dimensional (3D) structural data as a member of the worldwide Protein Data Bank (wwPDB) and currently processes about 25-30% of all deposited data in the world. In addition to prepare viewers of those structural data, several tools and services have been developed for functional annotations from those protein 3D structures. In particular, we have developed the algorithms of (i) rapid and correct similarity search of protein folds, (ii) very rapid comprehensive atomic structural alignment of functional sites, and (iii) molecular surface comparison with physicochemical properties. Several examples of the applications will be shown.

Reference : Standley, D.M., Kinjo, A.R., Kinoshita, K., Nakamura, H. (2008) Protein structure databases with new web services for structural biology and biomedical research. Brief Bioinform. 9(4), 276-285

2. Associate Professor Ram Samudrala, ( University of Washington , USA )

Biography: As a young researcher Associate Professor Ram Samudrala has directed his research at understanding protein folding, structure, function, and evolution, both at the single molecule as well as the genomic, proteomic, and organismal levels, using computational approaches. His work has led to more than 80 publications and freely copiable software for molecular and systems modelling (which are being used on high-performance Linux-based computing clusters that he has pioneered). He is a Searle Scholar with his research being funded by awards from the National Science Foundation, the National Institutes of Health, the Gates Foundation, and the University of Washington Advanced Technology Initiative in Infectious Diseases. He was named one of the world's top young innovators (TR100) by MIT Technology Review magazine in 2003, and was selected to present the UW New Investigator Science in Medicine Lecture in 2004. In 2005, he received an NSF CAREER award which recognized him as an outstanding scientist showing exceptional potential for leadership at the frontiers of knowledge. He was an NIH Director's Pioneer Award Finalist in 2006, and won the Alberta Heritage Foundation for Medical Research Visiting Scientist Award 2008. Most recently, he was awarded honorary diplomas from the cities of Carsma and Yaoton , Peru , for his humanitarian work on vaccine discovery.

His research group's web page is at: http://compbio.washington.edu .

Topic: Protein Folding Algorithms

Abstract: A fundamental biological challenge is to understand how the linear information in an organism's genome is processed to produce the resulting behavior or phenotype. Genes are transcribed and translated into proteins that adopt three-dimensional conformations. Evolutionary processes ensure that a folded protein conformation interacts with its environment in a manner that is beneficial to the organism, using the protein to catalyze reactions, recognize cellular signals, build cellular structures, and to perform a host of other diverse biological functions.

The talk aims to explain understanding of these processes by developing computational algorithms to model, annotate, and understand the relationships between the sequences, structures, functions, and interactions of proteins, DNA, proteins and metabolites, at both the molecular and the genomic/systems levels. The goal is to develop a coherent picture of the mechanistic basis (wiring diagram) of molecular and organismal structure, function, networks, and evolution within a fundamental scientific framework. The research team has applied the methods to more than fifty genomes/proteomes, encapsulated by the object-oriented Bioverse database and web application ( http://bioverse.compbio.washington.edu ). The individual structure and function prediction algorithms are available at the Protinfo webserver ( http://protinfo.compbio.washington.edu ).

The talk will detail our current progress and provide an overview of the methodologies being pursued by our group to achieve the above goals, recent basic science discoveries, and successful applications in the context of drug discovery and nanotechnology.

3. Professor David Sankoff ( University of Ottawa , Canada )

Biography: Professor David Sankoff, Canada Research Chair in Mathematical Genomics at the University of Ottawa and a member of the Centre de Recherches Mathématiques at the Université de Montréal is the recipient of the first-ever ISCB Senior Scientist Accomplishment Award. He has also been awarded “The Weldon Memorial Prize 2004”. Professor Sankoff's research attempts to expand the field of mathematical genomics on several fronts.  This includes the probabilistic modeling of prokaryotic genome evolution, with particular attention to short inversions, gene duplications, insertions and deletions, and investigating the consequences of these mechanisms for gene-order based phylogenetics.  The modeling of eukaryotic nuclear genome evolution poses somewhat different problems, particularly the quantitative parameters of inversion, transposition, translocation and duplication, and the connection between rearrangements observed at the experimental, clinical, population and evolutionary levels. The availability of refined genome sequences from humans and other mammals raise many new types of question for comparative study, largely due to uncertainty in gene localization and the identification of homologous genes in related species.  They have recently been focusing on the combination of statistical and algorithmic approaches to this problem. 

These models give rise to statistical analyses and tests for a variety of questions pertaining to functional versus historical versus random proximities of genes.  Comparison of the models with empirical data also promise to contribute to understanding phenomena as diverse as speciation, infertility due to chromosomal rearrangement, and chromosomal aberrations in neoplasms.

His website is at: http://albuquerque.bioinformatics.uottawa.ca/

Topic: Whole Genome Doubling and Guided Genome Halving

Abstract: Whole genome doubling is followed, over evolutionary time, by genome rearrangement through intra-and inter-chromosomal movement of genetic material. The genome halving problem is to reconstruct the ancestral genome on the basis of a decomposition of the present-day genome into a set of duplicated blocks of genes or DNA sequence dispersed among the chromosomes. A linear-time algorithm to find the ancestral genome that minimizes the genomic rearrangement distance to the present-day genome is available, but this combinatorial optimization solution does not suffice as a solution to the evolutionary biology problem, because it suffers from severe non-uniqueness; there may be large numbers of rather different solutions. We counteract this problem by guiding the reconstruction by one or more reference, or out group, genomes and illustrate with analyses of the genomes of cereals, yeasts and flowering plants.

4. Professor Geoff McLachlan (The University of Queensland , Australia )   (Invited talk -ppt)

Biography: Geoff McLachlan is Professor of Statistics in the Department of Mathematics and a Professorial Research Fellow in the Institute for Molecular Bioscience. He is also a chief investigator in the Australian Research Council (ARC) Centre of Excellence in Bioinformatics. He currently holds an ARC Professorial Fellowship and was recently appointed to the ARC College of Experts. He has written numerous research articles including six monographs. His current research interests are focussed in the fields of machine learning and bioinformatics.

His website is at http://www.maths.uq.edu.au/~gjm/

Topic: On Some Problems in the Classification of Microarray Gene-Expression Data  

Abstract: In this talk, we discuss some problems in the classification of microarray gene-expression data. One concerns the need to correct for selection bias in assessing the error rates of prediction (diagnostic) rules formed from a relatively small subset selected in some "optimal" way from the available number of genes. Another important application in microarray experiments concerns the clustering of gene-expression profiles as, for example, in time-course studies. For such applications, we need a procedure that allows for any known structure in the data. We consider the use of mixtures of linear mixed models in which for each cluster, a regression model is adopted to incorporate any covariates, and the correlation and replication structure in the data are handled by the inclusion of random effects terms. The procedure is illustrated in its application to the clustering of some well-known time-course data sets.