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Workshop Program

December 11, 2008, 13:30–16:15   MS3

13:30-14:25

Modular Biology: the Function and Evolution of Molecular Networks
Aviv Regev, Broad Institute of MIT & Harvard, Cambridge, MA, U.S.A.

14:25-15:20

Computational Studies Discover an new Mode of Gene Regulation
Steven Brenner, University of California, Berkely, U.S.A.

15:20-16:15

Statistical Models for Predicting HIV Phenotypes
Thomas Lengauer, Max Planck Insitute for Informatics, Saarbrücken, Germany

December 12, 2008, 07:45–10:30 and 15:45–18:30 Hilton: Mt. Currie

Morning session

7.45-8.10

Learning Temporal Sequence of Biological Networks
Le Song and Eric Xing

8.10-8.35

Switching Regulatory Models of Cellular Stress Response
Guido Sanguinetti, Andreas Ruttor, Manfred Opper and Cedric Archambeau

8.35-9.00

Detecting the Presence and Absence of Causal Relationships Between Expression of Yeast Genes with Very Few Samples
Eun Yong Kang, Ilya Shpitser, Hyun Min Kang, Chun Ye and Eleazar Eskin

9.00-9.15

Coffee

9.15-9.40

KIRMES: Kernel-based Identification of Regulatory Modules in Euchromatic Sequences
Sebastian J. Schultheiss, Wolfgang Busch, Jan Lohmann, Oliver Kohlbacher and Gunnar Rätsch

9.40-10.05

Approximate Substructure Matching for Biological Sequence Classification
Pavel Kuksa and Vladimir Pavlovic

10.05-10.30

Predicting Binding Affinities of MHC Class II Epitopes Across Alleles
Nico Pfeifer and Oliver Kohlbacher

Afternoon session

3.45-4.10

Inside the black box: Identifying causal genetic factors of drug resistance
Bo-Juen Chen, Helen Causton, Ethan Perlstein and Dana Peer

4.10-4.35

Full Bayesian Survival Models for Analyzing Human Breast Tumors
Volker Roth, Thomas Fuchs, Sudhir Raman, Peter Wild, Edgar Dahl and Joachim Buhmann

4.35-5.00

Probabilistic assignment of formulas to mass peaks in metabolomics experiments
Simon Rogers, Richard A. Scheltema, Mark Girolami and Rainer Breitling

5.00-5.15

Coffee

5.15-5.40

Learning “graph-mer” motifs that predict gene expression trajectories in development
Xuejing Li, Chris Wiggins, Valerie Reinke and Christina Leslie

5.40-6.05

On the relationship between DNA periodicity and local chromatin structure
Sheila Reynolds, Jeff Bilmes and William Stafford Noble

6.05-6.30

Discussion

Abstracts of Invited Speakers

Modular Biology: the Function and Evolution of Molecular Networks
Aviv Regev, Broad Institute of MIT & Harvard, Cambridge, MA, U.S.A.

Molecular networks provide the information processing backbone of cells and organisms, transforming intra- and extra-cellular signals into coherent cellular responses. The qualitative and quantitative understanding of the function and evolution of molecular networks is among the most fundamental questions in biology. Genomics provides powerful tools with which to probe the components and behavior of molecular networks. However, to successfully gain scientific insight from the huge volumes of heterogeneous data they generate requires a combination of experimental design, biological knowledge, and the power of computation. To address this challenge we focus on the unifying abstraction of the functional module - a collection of biological entities that act in concert to perform an individual identifiable function, such as a molecular machine, a signaling cascade, a regulatory unit, or a biosynthesis pathway.

In this talk I will describe the development and application of computational methods for the reconstruction of the architecture, function and evolution of modules in molecular networks. I will show how we leverage diverse genomics data to discover component modules in yeast, malaria and cancer; identify how relevant information is encoded at different layers of the network and translated into cellular responses; determine how multiple networks are integrated together; and reconstruct how contemporary complex systems have evolved over time to achieve their specific organization and remarkable functionality in organisms from yeast to mammals.

Computational Studies Discover an new Mode of Gene Regulation
Steven Brenner, University of California, Berkeley, U.S.A.

Statistical Models for Predicting HIV Phenotypes
Thomas Lengauer, Max Planck Institute for Informatics, Saarbrücken, Germany

We describe statistical models for predicting two important phenotypes of HIV, namely HIV resistance to combination drug therapies and HIV tropism.

HIV resistance: Given the relevant portion of an HIV genome, we predict the resistance of HIV to any of a number of antiviral drugs that are in clinical use. Furthermore we rank combination drug therapies with respect to their expected effectiveness against the given HIV variant. This involves a look into the future of the expected evolution of the virus when confronted with the given drug regimen.

HIV tropism: When entering the human host cell, HIV uses one of two coreceptor molecules on the cell surface. Which one the viral variant uses is indicative of the progression of the disease. We present a statistical model that predicts which of the two coreceptors the viral variant uses.

Both models are trained using various linear and nonlinear statistical learning procedures. The training data are carefully assembled databases comprising relevant genotypic, phenotypic and clinical parameters. While the resistance model only incorporates sequence features, one version of the tropism model also involves information on the structure of the relevant portion of the viral gp120 protein that docks to the human cell.

Both models are available via the webserver www.geno2pheno.org. The webserver has been developed in the context of the Arevir consortium, a German National research consortium targeted at the bioinformatical analysis of HIV resistance data, and is currently in prototypical use for research purposes. Members of the consortium and their associated practices treat about two thirds of the AIDS patients in Germany.

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