Simulating Biological Systems
A computational approach to understanding the physics of life, from protein dynamics to molecular interactions.
Education
My academic background and research experience.
Master of Science in Bioinformatics
Johns Hopkins University
Researching under: TBD
Bachelor of Science in Physics
University of British Columbia
Researched under Park Lab
Core Competencies
A visual summary of the key technologies and methodologies utilized in my work.
Project Showcase
A selection of my recent work, from published research to interactive web applications.
Protein Folding Simulation Toolkit
A Python-based toolkit for running and analyzing molecular dynamics simulations of protein folding, featuring a streamlined pipeline for GROMACS and a web-based visualization dashboard built with React.
- Engineered a data processing pipeline that reduced analysis time by 40%.
- Developed an interactive 3D protein viewer using React and Three.js.
- Implemented a REST API with FastAPI to serve simulation results to the frontend.
Deep Learning for Ligand Binding Affinity
Published research on a novel graph neural network architecture that predicts small molecule binding affinity with state-of-the-art accuracy, outperforming traditional docking methods.
- Achieved a 15% improvement in prediction accuracy over existing baseline models.
- Co-authored a paper published in the Journal of Chemical Information and Modeling.
- Designed and trained a Graph Convolutional Network on a dataset of over 100,000 protein-ligand complexes.
Interactive Cell Signaling Pathway Explorer
A web application that allows users to explore complex cell signaling pathways interactively. Built with D3.js for data visualization and integrated with pathway databases like KEGG.
- Visualized complex biological networks using force-directed graphs in D3.js.
- Integrated real-time data from the KEGG API to ensure pathway information is up-to-date.
- Created a user-friendly interface that received positive feedback for its intuitive design.
Writing Section
Commentary on recent developments in computational biology.
On the Statistical Mechanics of Protein Folding Models
A review of first-principle and data-driven models in predicting protein conformation, examining their underlying physical assumptions.
Data-Driven Approaches for Small Molecule Binding Affinity
An analysis of recent machine learning architectures for predicting ligand-protein interactions and their implications for therapeutic design.
Emergence and Complexity in Biological Systems
A perspective on the challenges and opportunities in deriving macroscopic biological phenomena from fundamental physical laws.