Abhishek Agarwal

I have completed my master’s in Computational Biology from Indraprastha Institute of Information Technology, Delhi (IIIT Delhi), where I acquired the experience and skills involved in studies linking biological problems with the computational approach towards resolving them. My Master dissertation was entitled “Finding correct mouse models for human cells using single-cell genomics”, which helped me develop competent knowledge of Machine Learning, Single-cell RNA sequence analysis, clustering algorithm, etc. Before that, I have a bachelor’s degree in Biotechnology with a dissertation project entitled “Production and Optimization Of Process Parameter For The Production Of Beta-Galactosidase Using Whey”.

As a cordial person, I cherish socializing with friends and family, watching TV ( news, documentaries & NBA ). I enjoy playing sports, especially basketball & table tennis, apart from sports I have been practicing Yoga daily for the past 4-5 years. I also like to keep my commercial & political knowledge up to date by reading from multiple sources. As a part of Enhpathy Horizon 2020 program, I am delighted in the ways this course conduces me to thrive personally and professionally. I have always been seeking such kind of program in which I can develop interdisciplinary knowledge of the cutting-edge research problem.

My research project

The spatial organization of enhancers around promoter regions within chromatin contact domains for selected Human cell lines: structural regulatory landscape (WP2)

The objective of the research is to develop and test the concept of the structural landscape for regulatory elements around promoter regions for selected cell lines. We will propose novel biophysical method to construct probabilistic ensembles of three-dimensional conformations for chromatin contact domains (CCDs, sometimes described as TADs: topologically associating domains) at the whole genome scale. The computational method exploits results from two independent experimental sources: first the genomic-based interaction data from ChIA-PET together with epigenetic modifications and transcription factors binding sites occupancy (ChIP-seq), and secondly the mRNA expression profiles (RNA-seq) measured in the same cell lines. We will independently validate our findings and computational algorithms by extensive analysis of GWAS in relevant regulatory elements..