Mathematical Modeling of Cell Signalling Pathways in Cancer
Developed a mathematical modeling framework to investigate intracellular cell signalling pathways involved in apoptosis and cancer progression. The project focused on understanding the interactions between STAT1, STAT3, Bcl-2, and BAX proteins and their influence on downstream signalling dynamics in cancer cells.
The study integrated ordinary differential equation (ODE) modeling, numerical simulations, and stability analysis to explore the behaviour of the signalling network under different biological conditions. Numerical solutions were implemented using Euler’s Method and the Runge-Kutta Fourth-Order (RK4) method to analyze equilibrium points and system stability.
The results demonstrated multiple equilibrium states within the signalling system, including stable and unstable conditions depending on parameter values. This research highlights the importance of mathematical modeling approaches in understanding complex biological systems and cancer-related signalling mechanisms.
Key Technical Work
- Developed ordinary differential equation (ODE) models for intracellular signalling pathways
- Modeled interactions between STAT1, STAT3, Bcl-2, and BAX proteins
- Performed numerical simulations using Euler’s Method and Runge-Kutta Fourth-Order (RK4) method
- Conducted equilibrium point and stability analysis of nonlinear systems
- Investigated stable and unstable states in apoptosis signalling networks
- Implemented computational simulations for biological system dynamics
- Analyzed how signalling pathway alterations impact downstream cellular responses
- Applied mathematical and computational approaches to cancer biology research
GitHub Repository
Technologies
- Python
- Java
- Mathematical Modeling
- Ordinary Differential Equations (ODEs)
- Numerical Methods
- Euler Method
- Runge-Kutta Method (RK4)
- Scientific Computing
Skills
- Mathematical Modeling
- Computational Biology
- Numerical Analysis
- Stability Analysis
- Differential Equation Modeling
- Cancer Systems Biology
- Scientific Programming
- Data Visualization