Computational Biology and Bioinformatics Discussion Group (1)

Computational Biology and Bioinformatics Discussion Group

Wednesday, November 9, 2005

The New York Academy of Sciences

Presented By

Presented by the Computational Bio & Bioinformatics Discussion Group


Organizer: Harmen Bussemaker, Columbia University

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."


Grégoire Altan-Bonnet, Memorial Sloan-Kettering Cancer Center
"Modeling T Cell Antigen Discrimination Based on Feedback Control of Digital ERK Responses."

Rafael A. Irizarry; Johns Hopkins University
"Using Stochastic Models to Improving Microarray Measurement."

Christina Leslie
, Columbia University
"Discovering Regulatory Element Motifs by Predictive Modeling of Gene Regulation."


"Modeling T Cell Antigen Discrimination Based on Feedback Control of Digital ERK Responses"
Grégoire Altan-Bonnet

T lymphocyte activation displays a remarkable combination of speed, sensitivity, and discrimination in response to peptide–major histocompatibility complex (pMHC) ligand engagement of clonally distributed antigen receptors (T cell receptors or TCRs). Even a few foreign pMHCs on the surface of an antigen-presenting cell trigger effective signaling within seconds, whereas 105–106 self-pMHC ligands that may differ from the foreign stimulus by only a single amino acid fail to elicit this response. No existing model accounts for this nearly absolute distinction between closely related TCR ligands while also preserving the other canonical features of T cell responses. Here we present the unexpected highly amplified and digital nature of extracellular signal-regulated kinase (ERK) activation in T cells. On the basis of this observation and evidence that competing positive- and negative-feedback loops contribute to TCR ligand discrimination, we constructed a new mathematical model of proximal TCR-dependent signaling. The model made clear that competition between a digital positive feedback based on ERK activity and an analogue negative feedback involving SH2 domain-containing tyrosine phosphatase (SHP-1) was critical for defining a sharp ligand discrimination threshold while preserving a rapid and sensitive response. Several nontrivial predictions of this model, including the notion that this threshold is highly sensitive to small changes in SHP-1 expression levels during cellular differentiation, were confirmed by experiment. These results combining computation and experiment reveal that ligand discrimination by T cells is controlled by the dynamics of competing feedback loops that regulate a high-gain digital amplifier, which is itself modulated during differentiation by alterations in the intracellular concentrations of key enzymes. The organization of the signaling network that we model here may be a prototypic solution to the problem of achieving ligand selectivity, low noise, and high sensitivity in biological responses.

"Using Stochastic Models to Improving Microarray Measurement"
Rafael A. Irizarry
; Johns Hopkins University
High density oligonucleotide expression arrays are a widely used tool for the measurement of gene expression on a large scale. Affymetrix GeneChip arrays appear to dominate this market. These arrays use short oligonucleotides to probe for genes in an RNA sample. Due to optical noise, non-specific hybridization, probe-specific effects, and measurement error, ad-hoc measures of expression, that summarize probe intensities, can lead to imprecise and inaccurate results. Various researchers have demonstrated that expression measures based on simple statistical models can provide great improvements over the ad-