Mahendra Prajapat

PhD

Postdoc

Bioinformatics Cancer Biology cell and molecular biology techniques Cell Culture Computational Biology Confocal Microscopy CRIPSR Cas9 genome editing Flow Cytometry Functional Genomics Genetics Genome Editing Genomics Histology Immunofluorescence Immunohistochemistry linux Mathematical modelling Mouse Models Perl qPCR R Programming RNA Sequencing Statistical Analysis Systems Biology Western Blotting

1st author preprint(s) Peer-reviewed publication(s) 1st author peer-reviewed publication(s) Conference Poster(s) Conference Talks(s) Funding

I developed an integrated experimental and quantitative program to determine how defined RNA-regulatory interactions influence gene expression and physiology at endogenous loci. Guided by kinetic modeling, I designed and generated two site-specific engineered mouse alleles: one disrupting both miR-19 regulatory sites in the Pten 3′UTR and another disrupting all seven conserved let-7 sites in the Hmga2 3′UTR. My contribution included CRISPR guide and donor design, mouse-model generation, founder screening, RFLP genotyping, Sanger-sequence confirmation, line establishment, breeding strategy, and the design of molecular and phenotypic studies. I also generated a complete Hmga2 3′UTR-deletion allele for a collaborative project and subsequently incorporated it as a useful comparator. Current work focuses on rigorous allele, transcript, and protein qualification before context-specific physiological characterization.

I conceived and executed an endogenous CRISPR-editing project to determine the contribution of individual microRNA-binding sites in otherwise isogenic mammalian cells. I designed the editing strategy, generated and screened edited clones, performed molecular and phenotypic assays, and analyzed the resulting expression data. The experiments revealed that clone-to-clone variation could be comparable to or greater than the regulatory effect being measured. I treated this unexpected result as an important experimental-design finding rather than overstating the original hypothesis. It led me to develop a more rigorous strategy based on independent biological replicates, endogenous mouse alleles, and statistical analysis of complete expression distributions. This project demonstrates my ability to manage technically demanding experiments, recognize confounding factors, interpret negative results constructively, and redesign a research program in response to the evidence.

I independently formulated and analyzed quantitative models of gene-regulatory circuits, feedback, transcriptional kinetics, and microRNA-mediated regulation. I translated biological reaction schemes into deterministic and stochastic simulations, including ordinary differential-equation and Gillespie-based approaches, and implemented analyses using R, Python, C/C++, and supporting command-line workflows. I conducted parameter exploration, sensitivity analysis, statistical evaluation, and benchmarking or calibration against independent experimental datasets. The models identified kinetic conditions under which regulatory interactions can have different effects on mean expression, variability, and cellular-state distributions. I used these results to develop falsifiable biological predictions and prioritize experiments. This work reflects my ability to connect mechanistic biology, mathematical reasoning, bioinformatics, and experimental design within a reproducible analytical framework.