| 1. Introduction to Python programming |
| – 1A. Basics of programming in
Python |
| – 1B. Efficient programming in
Python |
| – 1C. Numerical methods |
| – 1D. Exercises: How to access PDB
data in Python |
| 2. Ordinary differential equations |
| – 2A. Modeling gene circuits with rate equations |
| – 2B. Numerical
integration |
| – 2C. Practice: modeling bacterial growth |
| – 2D. Stability |
| – 2E. Bifurcation |
| – 2F. Exercises |
| 3. Phase plane |
| – 3A. Nulllines |
| – 3B. Stability in 2D |
| – 3C. Practice: modeling chemostat |
| – 3D. Practice: predator-prey model |
| – 3E. Bifurcation for two-variable
systems |
| – 3F. Separatrix |
| – 3G. Effective potential
revisited |
| – 3H. Multi-component
systems |
| – 3I. Exercises |
| 4. Systems with time delays |
| – 4A. Delayed differential
equations |
| – 4B. Examples of systems with
time delays |
| – 4C. Delays from indirect interactions |
| – 4D. Exercises |
| 5. Molecular dynamics |
| – 5A. Integrators for second order
ODEs |
| – 5B. Orbital motions |
| – 5C. Modeling a box of 2D
particle |
| – 5D. Exercises |
| 6. Stochastic differential equations |
| – 6A. Random number
generators |
| – 6B. Brownian motion |
| – 6B. SDE integrators |
| – 6D. Stochastic state
transitions |
| – 6E. Exercises |
| 7. Partial differential equations |
| – 7A. Modeling diffusion |
| – 7B. Reaction-diffusion
systems |
| – 7C. Turing instability |
| – 7D. Pattern formation in Dictyostelium |
| – 7E. Exercises |
| 8. Monte Carlo Simulations |
| – 8A. Monte Carlo Method |
| – 8B. Metropolis
algorithm |
| – 8C. Particles in a box: MCMC
sampling |
| – 8D. Gillespie Algorithm |
| – 8E. Exercises |
| 9. Global optimization |
| – 9A. MCMC optimization
methods |
| – 9B. Dynamic programming |
| – 9C. Genetic algorithm |
| – 9D. Exercises |
| 10. High dimensional data analysis |
| – 10A. Dimensionality reduction |
| – 10B. Clustering |
| – 10C. Network algorithms |
| – 10D. Exercises |
