- Transcription Factor Networks In Drosophila Melanogaster
- Mixtures of opposing phosphorylations within hexamers precisely time feedback in the cyanobacterial circadian clock
- Intersection of population variation and autoimmunity genetics in human T cell activation
- Longitudinal analysis of microbial interaction between humans and the indoor environment
- Comparative Analysis of Regulatory Information and Circuits Across Distant Species
IGSB core investigator Barbara Stranger received supplemental funding from NIH to explore the effects of gender on human traits. Her group will characterize these differences and determine what underlies them. This new funding will allow her lab to focus on how gender influences the relationship between genetic differences among individuals and variation in protein levels among individuals and across tissues, with an ultimate goal of understanding how this influences disease susceptibility. Her lab also received funding to specifically investigate the role of gender in pharmacogenomics phenotypes and neuropsychiatric phenotypes (with IGSB core member Andrey Rzhetsky). For more information, click here.
IGSB students in Mike Rust's lab are collaborating on an International Genetically Engineered Machines (iGEM) project about synthetic biology. IGSB team members are using standardized biological parts provided by iGEM and molecular components they have devised to engineer E.coli mutator strains that can optimize the production of a desired metabolite using a novel technique in directed evolution. The team will go to the 2014 iGEM jamboree at MIT to present their research. Participating iGEM teams from around the world will also attend and demonstrate synthetic living systems with innovative functions and capabilities.
IGSB Research Professional, Dr. Audrey Fu, was awarded a K99 NIH grant to support a research project entitled: Causal Inference of Gene Regulatory Networks with Application to Breast Cancer. The aim is to develop statistical models and computational methods for the inference of causal gene regulatory networks. The project will investigate how genetic variation acting on biological networks influences development and progression of diseases, using subtypes of breast cancer as a disease model.
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