The Department of Biomedical Informatics
presents
Mukesh Bansal, PhD
Mukesh Bansal is an associate research scientist in the laboratory of Andrea Califano. He majored in physics at the Indian Institute of Technology and received his PhD in systems biology from University of Naples, Italy. His research interests include inferring transcriptional, posttranscriptional, posttranslational, and signaling networks from gene/miRNA expression and proteomics data, and dissecting these networks to identify master regulators (MR) of physiologic and pathologic phenotypes. He successfully applied this methodology to identify patient-specific MRs of lung cancer, helping to pave the path for personalized medicine. He also developed a method to predict drug mechanism of action, which proved to be highly effective in predicting novel drug targets and genes involved in modulating drug sensitivity and drug related toxicity. Recently he co-organized a community-based challenge to identify methods to predict compound synergy and confirmed that in silico assessment of compound synergy is indeed possible. He is also a co-inventor of a patent of 3 gene biomarkers to predict indolent prostate cancer and method to infer compound mechanism of action.
“Driving Translational Research Using Systems Biology Approaches”
April 9, 2015
1:00 pm – 2:00 pm - HSC Level 2, Lecture Hall 2
Contact the Department of Biomedical Informatics at (631) 444-8459 with any questions regarding this event.
ABSTRACT
Large-scale molecular profiling experiments are producing genetic, transcriptomic and proteomic data for hundreds of patients with common disease phenotype. An analysis of these data types is an important initial step towards understanding the underlying genetic and molecular pathways involved. Despite being relatively successful, until recently, most of the approaches to analyze these datasets relied on the characteristics of individual genes without considering the complex molecular networking of genes within a biological system. Availability of large-scale data has provided us a unique opportunity to utilize Systems Biology approaches to reverse engineer gene regulatory network i.e. how different entities in a given cell interact with each other and develop predictive computational methods to model those interactions. These context specific network structures are invaluable for identification of key genes involved in maintaining cells in a specific physiological state, providing critical information on the mechanisms of resistance and sensitivity to therapeutic agents, or progression to a disease state.
I will present one such method showing how signaling network can be inferred from high throughput phospho-proteomic profiles, derived from non-small cell lung cancer patients, and how this network can be further interrogated to identify druggable addiction points. This method had successfully identified addiction points in a patient specific manner that has been experimentally validated in a panel of cell lines, thus paving the path for personalized medicine. I will also show how these networks can be further interrogated by studying the dynamic rewiring of stable network following drug perturbations to elucidate the mechanism of action (MoA) of drugs. Successful inference of MoA of drugs not only led us to identify specific effectors of drug resistance/sensitivity and drug toxicity, but also helped us in elucidating similarity between drug pairs, thus providing key evidence for drug repurposing. Finally I will present how we can move from single drug therapy to multidrug therapy in patients, thereby increasing the efficacy of treatment and lowering the drug induced toxicity.