Events Archive
2008
Systematic Mapping of Signal Transduction Pathways in Bacteria
Bldg: 221, Conference Room A216, Argonnne National Laboratory
Latest News
Gifts to boost University of Chicago as hub for biomedical ‘big data’
Two major gifts will build momentum behind the University of Chicago’s leadership in biomedical computation by assembling experts in the field and furnishing them with the tools to use “big data” to understand and treat disease. Kevin White and Robert Grossman will lead the Pancreatic Cancer Genomic Medicine Initiative, which aims to improve care for patients with this disease using genomic and physiological data.
Immunity, inflammation and natural selection
Barbara Stranger and colleagues take a systems approach, integrating GWAS, eQTL and protein interaction data, to demonstrate that loci associated with inflammatory disease susceptibility are enriched for genomic signatures of recent evolutionary selection. Their analyses suggest that natural selection for pathogen-defense mechanisms through human evolution may underlie modern susceptibility to inflammatory diseases.
Robert Grossman named a Federal 100 Award winner
The annual list, published by Federal Computer Week, recognizes government, industry and academic leaders who have played pivotal roles in the federal government IT community.
CBC announces a high throughput screening award
In response to the community-wide interest in High Throughput Screening, the Chicago Biomedical Consortium is offering a 1:1 HTS Matching Grant Program to help fund innovative small molecule discovery. The intent of this program is to support pilot projects involving bio medically-relevant targets using a HTS facility located at one of the CBC universities, including the IGSB’s Cellular Screening Center.
A network of nuclear receptors
By mapping the bindings sites of nuclear receptors, chromatin state markers and transcription factors associated with breast cancer, IGSB Director Kevin White and colleagues construct a network representing different types of regulatory relationships. Their analyses identifies transcriptions factors with previously unsuspected roles in breast cancer and enables predictions of responses to therapy.
