This repository contains several computational biology projects developed for coursework.
The projects combine mathematical modeling, simulation, and numerical analysis to study biological systems at different scales.
The repository includes work in three main areas:
-
Enzyme kinetics and metabolic modeling
Modeling multi-substrate enzyme reactions, fitting kinetic models to data, evaluating goodness of fit, and analyzing biomass-production constraints. -
Gene regulation and stochastic biological dynamics
Studying hidden Markov models, regulatory ODE/SDE systems, transcriptional regulation, splicing sabotage, and downstream metabolic effects. -
Multiscale white matter repair modeling
Simulating demyelination and remyelination through coupled reaction-diffusion equations, a Cellular Potts Model, intracellular gene regulation, and myelin repair dynamics.
Across the repository, the main biological and computational topics include:
- Michaelis-Menten and multi-substrate enzyme kinetics
- parameter estimation and goodness-of-fit statistics
- batch-reaction time calculations
- stoichiometric and steady-state metabolic constraints
- hidden Markov models and the Viterbi algorithm
- gene regulatory networks with Hill functions
- deterministic and stochastic differential equation models
- stability analysis of biological dynamical systems
- reaction-diffusion systems
- Cellular Potts Models for cell-based simulation
- multiscale modeling of tissue damage and repair
The projects use a mix of:
- ordinary differential equations (ODEs)
- stochastic differential equations (SDEs)
- hidden Markov models (HMMs)
- reaction-diffusion PDEs
- numerical optimization
- statistical goodness-of-fit measures
- simulation of cell-based and multiscale systems
The code in this repository is written in Python and is intended for scientific computing workflows.
Typical libraries for this kind of work may include:
numpyscipymatplotlibpandas
Run the Python scripts from the command line, for example:
python main.py