
We are a computational biology group interested in developing statistical machine learning methods to understand gene regulatory networks driving cellular functions. We are interested in identifying networks under different environmental, developmental, disease and evolutionary contexts and examining their dynamics. We ultimately aim to construct predictive models from these molecular networks that can inform us how the system will behave under different perturbations. We develop tools for bulk and single cell omic datasets and apply them to diverse biological and biomedical questions with the central theme of understanding gene regulation. Learn more about our research.
The Latest
Deciphering the Regulatory Network of a Pathogenic Fungus
In a new study, researchers from the Wisconsin Institute for Discovery (WID) have created a software tool that can help reveal biological pathways of a notorious pathogenic fungus. Aspergillus fumigatus, which is found…
How disabling one gene protects mice against Type 1 diabetes
In collaboration with the Feyza Lab, Khagani Eynullazada, a grad student from Sushmita Roy’s lab identified gene regulatory networks capturing shared and perturbation-specific stress pathways in Type 1 diabetes using GRN inference…
Sysbio Journal Club
Meets biweekly
Mondays at 12:30 pm
Discovery Building, room 3160

