Keynote Speakers

 

Terry Gaasterland
Professor and Director Scripps Genome Center & Scripps Institution of Oceanography University of California, San Diego
Vice President, 
International Society for Computational Biology

http://genomes.ucsd.edu/index.shtml

TOPIC   Computational Dissection of Cell Cycle Control Networks
ABSTRACT   Protein phosphylation by a family of enzymes called cyclin-dependent kinases (Cdks) directs the cell cycle by modifying the function of regulators of key processes such as cell cycle start, DNA replication, and mitotic progression. This talk will report on two complementary techniques to examine cell cycle control and their use in understanding the "control circuit" for cell cycle Start. First, we present a novel computational procedure to predict target phosphorylation substrates of Cdks, and discuss results of predicting and validating substrates for Cdc28 (Cdk1) in Saccharomyces cerevisiae (yeast). Validation was done using independent Cdk proteomic datasets. Second, this talk will describe an approach for monitoring cell division starting with a single cell and measuring cycle features for each cell. Data was collected under different knock-out conditions in which key components that control cell cycle Start were eliminated. Single-cell measurements elucidate their impact on the cell cycle Start control circuit and give insights into how control circuits are organized.

  Satoru Miyano
Professor Human Genome Center Institute of Medical Science The University of Tokyo
http://bonsai.ims.u-tokyo.ac.jp/people/miyano/index.html
TOPIC   Data Assimilation for Systems Biology
ABSTRACT   One of the hottest topics in systems biology is to determine the parameters in simulation models from observational data. For this problem, we are challenging to develop a supercomputer application technology based on data assimilation which is a statistical and computational method which "blends" simulation models and observational data "rationally". Originally, this technology has been developed for geophysical simulation sciences in which simulation models are incomplete or too complex to model. The simulation models in geophysical sciences are firmly based on well-established physical principles and data points in time-courses are many. On the other hand, it is not straightforward to apply the idea of data assimilation to biological pathway modeling because the mechanisms and principles in dynamic pathways are vague and the number of data points in time-course is usually very small, e.g. microarray gene expression data. In this talk, we show our strategy of data assimilation for systems biology that is comprised of (1) modeling biological pathways using hybrid functional Petri net with extension (HFPNe) on Cell Illustrator Online, (2) conversion of HFPNe models to nonlinear state space models, (3) estimating parameters in the models by Bayesian statistical inference, and (4) statistical model selection for hypotheses and new structures. Microarray gene expression data and protoemics data in time-courses are used in our case studies, and we introduce our recent advancements in data assimilation technology that will employ peta flops computing.

  Vladimir Brusic
Director of Bioinformatics Cancer Vaccine Center Dana-Farber Cancer Institute Harvard Medical School
Professor of Bioinformatics and Database management
University of Queensland
TOPIC   Translational Medicine: A Grand Challenge for Bioinformatics
ABSTRACT   Translational medicine focuses on connecting basic research and technological advances into improved patient care. Traditionally, translational medicine focused on drug discovery and development, but more recently it has been defined as a global approach that applies advances from basic science and engineering directly into clinical care. Combining clinical and laboratory data for analysis aimed to support patient care brings a new dimension since it combines traditionally separate fields of bioinformatics and medical informatics. The main focus of bioinformatics has been on the study of genes, proteins, genomes, proteomes, and their interactions. There are several driving forces that are changing this focus, including:
  • There is increasing effort in the study of higher level biological structures, such as cells, tissues, organs, and organisms;
  • Large-scale screening efforts driven by genomics and proteomics are shifting from screening to the development of diagnostic, therapeutic, and monitoring applications
  • Information from clinical trials is now available and readily accessible;
  • Information technology advances, in particular database technology, standardization, modeling and simulation, and global infrastructure such as Grid computing are rapidly changing all aspects of health care.

The new challenges for bioinformatics arise from the enormous scale of the problem where complex multidimensional data derived from multi-level hierarchical systems need to be analyzed to support discovery, decision making, and design of applications in the clinic. Information technologies needed for such tasks exist, but their integration is lagging. This talk will address these four topics and present examples from vaccine development. The examples will include the process of integration of antigen data from multiple sources, integration of prediction tools that simulate laboratory experiments, use of these tools for design of diagnostic applications, and scaling up the whole process by using immune system simulator (ImmunoGrid).


shoba   Shoba Ranganathan
Chair Professor of Bioinformatics Dept of Chemistry and Biomolecular Sciences ARC Centre of Excellence in Bioinformatics Macquarie University Australia
http://biolinfo.org/people/shoba.html
TOPIC   Needles in the EST haystack: advances in transcriptome analysis
ABSTRACT   Expressed sequence tags (EST) represent short, unedited, randomly selected single-pass sequence reads derived from cDNA libraries, providing a low-cost alternative (also called “poor man’s genome) to whole genome sequencing, with a glimpse of the transcriptome of an organism at various stages of development. Parasitic nematodes of humans, other animals and plants continue to impose a significant public health and economic burden worldwide, due to the diseases they cause. Mining the entire expressed sequence tag (EST) data available from parasitic nematodes represents an approach to discover novel genes and proteins for therapeutic applications. Based on a critical evaluation and benchmarking of the current methods used in the analysis of ESTs we developed a new high throughput EST analysis platforms, to clean, assemble and annotate ESTs at both DNA and protein levels. Results from the EST analyses of various parasitic nematodes of economic importance, such as Dictyocalulus viviparus, Haemonchus contortus, Ancylostoma caninum, and Trichostrongylus vitrinus. will be presented. Promising antiparasitic drug and vaccine candidates have been discovered from excreted or secreted (ES) proteins released from the parasite and exposed to the immune system of the host. To mine the entire EST dataset (~0.5 M) available from 39 parasitic nematodes for ES proteins, we developed EST2Secretome and uncovered 4,710 putative ES proteins. The significance of these in terms of observed Caenorhabditis elegans loss-of-function phenotyes, Gene Ontology and pathway assignments, interaction partners and novel nematode-specific domains will be presented.

Invited Speakers