ABSTRACT
Large-scale profiling data of human patients and pre-clinical models can be analyzed with novel Systems Biology approaches to gain insights into drivers of disease pathogenesis, decipher drugs' mechanism of action, and repurpose drugs for new indications. Today I will present how we can use Systems Biology approaches for various aspects of drug discovery and precision medicine. One such method uses inference of signaling networks from high throughput phospho-proteomic profiles and interrogating them to identify druggable addiction points in non-small cell lung cancer patients. Another application of gene regulatory networks studies the dynamic rewiring of the stable network following drug perturbations to elucidate drugs' mechanism of action (MoA). Successful inference of MoA of drugs led us to identify specific effectors of drug resistance/sensitivity and drug toxicity and helped us elucidate similarity between drug pairs, thus providing key evidence for drug repurposing. Finally, I will show machine learning approaches applied to high-throughput phenotypic data to study drug safety and infer novel drug treatments for CNS disorders.

SPEAKER

Mukesh obtained his Ph.D. in Human Genetics from the University of Naples, Italy, and Post-Doctoral training in Systems Biology from Columbia University. His research interest is to use Systems Biology approaches and integrative data analyses to understand the mechanism of disease progression, identify biomarkers for disease prognosis and patients’ specific disease checkpoints, infer drug mechanism of action, and predict compound synergy. Later, he joined Psychogenics Inc as a VP of Data Sciences, leading a team of data scientists and software developers to design novel methods to facilitate various preclinical drug discovery programs using high throughput behavior data. He recently joined Bristol Myers Squibb as an Associate Director. He led a Systems Biology and Screening Informatics team to identify distinct MoA classes represented in the proprietary compound library, elucidate chemical features associated with these classes, and leverage them to perturb disease states observed in real-world patient cohorts. 

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