Computational Biology and Bioinformatics Discussion Group

Computational Biology and Bioinformatics Discussion Group

Wednesday, November 8, 2006

The New York Academy of Sciences

Presented By

Presented by the Computational Biology & Bioinformatics Discussion Group

 

Organizer: Mona Singh, Princeton

Speakers
: Artemis Hatzigeorgiou, University of Pennsylvania; Teresa Przytycka, NIH; Steve Kleinstein, Yale

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."

Abstracts



Artemis Hatzigeorgiou: Computational predictions of microRNA genes and targets: exploring the significance and validation of current approaches

MicroRNAs (miRNAs) are small RNAs, ~21 nucleotides in length, that regulate gene expression at the posttranscriptional level. During the past three years, more than a dozen programs have been published for the prediction of miRNA genes and targets. I will speak about the main features and underlying algorithmic methodology that most widely used programs employ in order to make predictions. A focus of the talk will be on the experimental and statistical approaches that are used in order to determine the specificity and sensitivity of these programs. Finally I will discuss how such approaches are currently used for answering questions such as: How many miRNA genes are encoded within the human genome? How many coding genes are targeted by miRNAs?

Teresa Przytycka: Predicting domain-domain interactions and interaction dynamics from protein-protein interaction network.

Comprehending the cell functionality requires knowledge about the functionality of individual proteins as well as the interactions among them. In the last few years new high-throughput interaction detection methods generated enormous amounts of interaction information, usually represented in the form of protein-protein interaction network. In this talk I will address two challenging questions in analyzing protein-protein interaction data. First, while some proteins form stable complexes, other form transient associations and are part of different complexes at different stages of a cellular process. Deciphering information about temporal relations between interactions presents a formidable challenge. Furthermore, proteins typically contain two or more domains, and a protein interaction usually involves binding between specific pairs of domains. Identifying such interacting domain pairs is an important step towards understanding the protein-protein interaction network.

Steven Kleinstein: Modeling and Simulation for the Analysis of Adaptive Immunity

The ability of the immune system to adapt in response to challenges protects us from recurrent infections, guards against rapidly mutating pathogens, and underlies many vaccines. However, dysregulation of adaptive immunity may produce the high affinity autoantibodies responsible for tissue damage in autoimmune disease, and aberrant targeting may contribute to the genesis of B cell cancers. This talk describes two computational-experimental collaborations focused on adaptive immunity that apply modeling and simulation approaches to: (1) understand cell fate decisions from time-series labeling data quantified by flow cytometry, and (2) link biological dynamics to B cell receptor lineage trees from tissue microdissection.