MUKUL SHEREKAR

Accomplished scientist with 13 years of expertise in protein sciences. Demonstrated success with a track record of publishing three papers in peer-reviewed journals, including one as the first author. Aspire to solve challenges at intersection of protein engineering and artificial intelligence.

• Application of a Hybrid Neural Network-Mechanistic Model on Genome Scale Metabolic Model of Vibrio natriegens, Demonstrated a novel published model can predict accurate flux-balance-analysis simulated fluxes in a new scenario on a model of Vibrio natriegens (instead of publlished model of E.colli). Predicted flux-balance-analysis simulated growth rates on an independent test set with regression and validation coefficients of 0.74 each. Conducted limited hyper-parameter sweep by varying nodes in single hidden neural layer and hidden dimensions. Showed applicability and advantages of combining white-box and black-box models in a new scenario., Hybrid Modeling, Neural Networks, Mechanistic Modeling, Hyper-Parameter Optimization
• Cellular-Automata Model for Cancer Tumor Growth Dynamics, Simulated five virtualization scenarios of tumor growth dynamics as published in a paper. Showed advantages of a stochastic cellular automata., Dynamical Modeling, Simulation Design, Cellular Automata Modeling, Stochastic Modeling
• Database (SQL) Development for Protein Inventory, Created Entity Relationship Diagram for the conceptual design using MySQL Workbench. Created logical design for the Entity Relationship Diagram. Implemented Entity Relationship Diagram to create relationships using MySQL. Wrote queries in MySQL for data retrieval., Planning, Designing, Data Modeling, Implementation
• Database (MongoDB) Development for Volume Electron Microscopy Metadata, Developed a logical database design for MongoDB (NoSQL database). Applied the design in python to create two databases for two different microscopes. Created a simple user interface and queries using python. Created a system for automated export of instrument level and run level metadata., Planning, Designing, Data Modeling, Implementation

• Creating AI-enabled Systems, Deep Neural Networks (Spring 2024)
• Python, Data Structures, Foundation of Algorithms, Applied Machine Learning
• Systems Biology, Modeling & Simulation of Complex Systems
• Principles of Database Systems, Biological Databases and Tools
• Biochemistry, Molecular Biology, Epigenetics

• Biochemical and structural analyses reveal that the tumor suppressor neurofibromin (NF1) forms a high-affinity dimer., Journal of Biological Chemistry, 295, 4, 1105-1119, 2020, Sherekar, Mukul
• R pyocin tail fiber structure reveals a receptor-binding domain with a lectin fold., PLoS One, 14, 2, e0211432, 2019, Salazar, A. J., Sherekar, M., Tsai, J., Sacchettini, J. C.
• Structure, activity, and inhibition of the carboxyltransferase β-subunit of acetyl coenzyme A carboxylase (AccD6) from Mycobacterium tuberculosis., Antimicrobial agents and chemotherapy, 58, 10, 6122-6132, 2014, Reddy, M. C., Breda, A., Bruning, J. B., Sherekar, M., Valluru, S., Thurman, C., ..., Sacchettini, J. C.