Prof. Jeff Gore discusses modeling stochastic systems. The discussion of the master equation continues. Then he talks about the Gillespie algorithm, an exact way to simulate stochastic systems. He then moves on to the Fokker-Planck equation.

License: Creative Commons BY-NC-SA
More information at ocw.mit.edu/terms
More courses at ocw.mit.edu

1. Introduction to the class and overview of topics
2. Input function, Michaelis-Menten kinetics, and cooperativity
3. Autoregulation, feedback and bistability
4. Synthetic biology and stability analysis in the toggle switch
5. Oscillatory genetic networks
6. Graph properties of transcription networks
7. Feed-forward loop network motif
8. Introduction to stochastic gene expression
9. Causes and consequences of stochastic gene expression
10. Stochastic modeling
11. Life at low Reynold’s number
12. Robustness and bacterial chemotaxis
13. Robustness in development and pattern formation
14. Microbial evolution experiments and optimal gene circuit design
15. Evolution in finite populations
16. Clonal interference and the distribution of beneficial mutations
17. Fitness landscapes and sequence spaces
18. Evolutionary games
19. Survival in fluctuating environments
20. Parasites, the evolution of virulence and sex
21. Interspecies interactions
22. Ecosystem stability, critical transitions, and biodiversity
23. Dynamics of populations in space
24. The neutral theory of ecology

This course presented by Prof. Jeff Gore provides an introduction to cellular and population-level systems biology with an emphasis on synthetic biology, modeling of genetic networks, cell-cell interactions, and evolutionary dynamics. Cellular systems include genetic switches and oscillators, network motifs, genetic network evolution, and cellular decision-making. Population-level systems include models of pattern formation, cell-cell communication, and evolutionary systems biology.