
Biotechnology undergraduate with a 9.5 CGPA and research experience at the Indian Institute of Science, with a growing focus on bioinformatics and computational approaches for understanding complex biological systems. With a foundation in molecular biology techniques and experimental research, I am increasingly interested in building computational pipelines and analytical tools to work with genomic, proteomic, and molecular interaction datasets. My current interests include protein ligand interaction analysis, targeted drug delivery modeling, and computational prediction of blood brain barrier permeability, along with developing Python based workflows for biological data analysis and visualization. Through independent exploration and research exposure, I aim to combine biological insight with data driven methods to identify regulatory patterns, therapeutic targets, and predictive models that can contribute to modern drug discovery and systems level understanding of disease.
Technical Skills
1)Bioinformatics and Computational Tools:Python for bioinformatics workflows (NumPy, Pandas)Bioinformatics pipeline development and biological data processingSequence analysis concepts and genomic data handlingMolecular docking workflows and protein ligand interaction analysisBiological data visualization (Matplotlib)
2)Data Analysis and Scientific Computing:Data processing and statistical analysis using PythonBiological dataset handling and exploratory analysisBasic command line and computational workflow concepts
3)Molecular Biology Techniques (Laboratory Experience)PCR, Gel Electrophoresis, DNA IsolationSDS PAGE, Coomassie and Bradford assaysMicroscopy, Gram staining, plasmid handling
Soft SkillsScientific communicationTeam collaborationTime management
• Automated Drug–Target Screening Pipeline integrating protein structures from Protein Data Bank and ligands from PubChem with docking using AutoDock Vina (In Progress)
• AI-Driven Drug–Target Interaction Prediction using machine learning models and biochemical feature extraction from protein and ligand datasets (In Progress)
• Brain Aging Transcriptomic Analysis Pipeline for differential gene expression and visualization using datasets from Gene Expression Omnibus (In Progress)
• Gene Co-Expression Network Analysis to identify regulatory interactions and hub genes from gene expression datasets (In Progress)
• Reproducible RNA-Seq Data Processing and Visualization Pipeline using Python libraries including Pandas, NumPy, and Matplotlib (In Progress)
• Biopolymer Hydrogel Development using cellulose extracted from agricultural waste and polymer characterization
• Arduino-Based Ethylene Receptor Signaling Model demonstrating plant hormone response pathways