Computational Biology and Bioinformatics Discussion Group (2)

Computational Biology and Bioinformatics Discussion Group

Wednesday, September 21, 2005

The New York Academy of Sciences

Presented By

Presented by the Computational Bio & Bioinformatics Discussion Group


Organizer: Andras Fiser, Albert Einstein College of Medicine

The Bioinformatics and Computational Biology Discussion group brings together diverse institutions and communities to share new and relevant information at the frontiers of the interrelated fields of bioinformatics and computational biology. Recent topics have included "Benchmarking and Improving the Accuracy of Comparative Modeling of Protein Structures," "Integrated Statistical Modeling of Gene Expression Data," and "Estimating SNP Haplotype Frequencies from DNA Pools."


4:00–6:00: Presentations

Olga Troyanskaya, Princeton University
"Identifying Chromosomal Changes from Microarray Data."

David Shaw
, DE Shaw & Company
"Toward Millisecond-Scale Molecular Dynamics Simulations of Proteins."

Dmitry Rykunov, Albert Einstein College of Medicine
"Effects of Sparse Statistics and the Finite Size of Proteins on Distance-Dependent Pairwise Statistical Potentials."


"Identifying Chromosomal Changes from Microarray Data"
Olga Troyanskaya

Chromosomal copy number changes (aneuploidies) are common in cell populations that undergo multiple cell divisions, including yeast strains, cell lines, and tumor cells. Identification of aneuploidies is critical in evolutionary studies, where changes in copy number serve an adaptive purpose, as well as in cancer studies, where amplifications and deletions of chromosomal regions have been identified as a major pathogenetic mechanism. Aneuploidies can be studied on the whole-genome level using array CGH (a microarray-based method that measures DNA content), but their presence also affects gene expression. In gene expression microarray analysis, identification of copy number changes is especially important in preventing aberrant biological conclusions based on spurious gene expression correlation or masked phenotypes that arise due to aneuploidies. I will describe our expectation-maximization–based approach to identifying partial chromosome changes from microarray data. I will also discuss applications of our method to cancer data and to studies of molecular evolution.

"Toward Millisecond-Scale Molecular Dynamics Simulations of Protein"
David Shaw
Some of the most important outstanding questions in the fields of biology, chemistry, and medicine remain unsolved as a result of our limited understanding of the structure, behavior, and interaction of biologically significant molecules. The underlying laws of physics that determine the form and function of these biomolecules are relatively well understood. Current technology, however, does not allow us to simulate the effect of these laws with sufficient accuracy, and for a sufficient period of time, to answer many of the questions that biologists, biochemists, and biomedical researchers are most anxious to answer. This talk will summarize the current state of the art in biomolecular modeling based on all-atom molecular dynamics simulations in explicitly modeled solvent and will describe efforts within our own lab to develop novel algorithms and machine architectures to accelerate such simulations by several orders of magnitude.

"Effects of Sparse Statistics and the Finite Size of Proteins on Distance-Dependent Pairwise Statistical Pair Potentials"
Dmitry Rykunov

Statistical distance-dependent pair potentials are frequently used in a variety of applications. The applicability of these types of potentials is tightly connected to the question of accuracy and reliability of statistical observations. We analyzed distance dependence of pairwise statistical potentials using shuffled protein-like decoys. Such decoys correspond to a model with no interactions between their components; therefore, they are expected to yield zero potential. In contrast to this expectation, potentials obtained from various shu