Investigators from WID are among the recipients of the latest round of UW2020 awards.
WID researcher Sushmita Roy and collaborators at UW–Madison and the University of Florida will use a $7 million grant from the U.S. Department of Energy to study how some plants partner with bacteria to create usable nitrogen and to transfer this ability to the bioenergy crop poplar.
A computational biology group interested in developing statistical computational methods to understand regulatory networks driving cellular functions. The lab works to identify networks under different environmental, developmental and evolutionary contexts, comparing these networks across contexts, and construct predictive models from these networks.
Understanding diversity in microbial communities and their role in infectious disease; in particular, the genetic basis for stability of microbial communities, the role of a gut community as a source of opportunistic pathogens, and the soil microbial community as a source of new antibiotics and antibiotic resistance genes.
Working to understand the fundamental mechanisms of chromatin-based gene regulation. The lab studies how various chromatin factors are recruited to chromatin to “read” and ‘translate” epigenetic information into differential gene expression patterns under normal growth and development as well as stress conditions.
Investigating the mechanism and biological function of reversible protein modifications involved in modulating signal transduction, chromatin dynamics, and gene activation and addressing the “Histone Code” hypothesis by beginning to understand histone modification signaling code and its mechanisms and regulation.
The Multi-Omics Hub will focus on the use of big data about the genes, microorganisms, and metabolites to understand biological systems. WID’s expertise makes it an ideal home for the Epigenetics Initiative for the large campus community that studies the epigenome, and as such WID will organize meetings, seminars, mutli-PI …
In a paper in Cell Systems, Sushmita Roy and colleagues develop a probabilistic graphical model-based method, multi-species regulatory network learning that uses a phylogenetic framework to infer regulatory networks in multiple species simultaneously.