Note: This content is accessible to all versions of every browser. However, this browser does not seem to support current Web standards, preventing the display of our site's design details.

  

227-0684-00L
Control Methods in Systems Biology

Professor(en):
H. Koeppl 		Betreuer:
E. August, A. Ganguly, M. Unger, P. Nandy
Vorlesung:
 Link zum Kurskatalog
Frühling 2013 		Webseite:
Ziele:
After successful completion of the course the student will be able to derive computational models from experimental facts; he will be acquainted with the basics of molecular cell biology; he will know what model formulation to chose that best fits the data situation.		 Vorlesungslevel:
D-ITET Master, Systems and Control specialization
Recommended Core Courses
Voraussetzungen:
Inhalt:
1. Basics of molecular cell biology 2. Reaction rate equations: Basics of nonlinear differential equations, and population models, Lyapunov stability, stoichiometric formulation, stoichiometry analysis. 3. Stochastic analysis: Markov process basics, Master equation, Omega expansions, Fokker-Planck equation, linear noise approximation, moment closures, Langevin, simulation algorithms, Gillespie, tau-leaping, SDE integration. 4. Spatial simulations: Smoluchowski diffusion model, Compartment models, spatial Gillespie, Greens functions reaction dynamics, mesh methods. 5. Parameter inference, system identification: ODE identification, Markov process inference, Markov Chain Monte Carlo methods, sequential Monte Carlo, optimal experimental design. 6. Computer science models: Petri nets, rule-based models, finite state automata, hybrid automata, boolean models, fuzzy models. 		Dokumentation: