Learning objectives: Insight in typical properties of complex, biochemical networks, like robustness.
1. Static representation and analysis of biological pathways as graphs (degree distribution, hubs, distance metrics, cluster coefficient).
2. Analysis and prediction of the influence of network structure (topology, modularity, feedback) and kinetic parameters on system dynamics. Estimation of model parameters from experimental data (inverse problem) and how experiments can be designed to yield (optimally) informative data.
3. Stochastic models; understanding the difference with deterministic descriptions; modeling and simulation.
The methods will be applied to examples from ongoing research related to metabolic networks and signal transduction pathways. In particular, type 2 diabetes will be discussed.
Date: Feb 9 – March 27, 2015
Target audience: Master students; Bioinformaticians; System biologists; Biomedical engineers
Program: Sequence alignment, Hidden Markov Models, graph theory.
State space models (system of coupled differential equations) of biochemical networks: modeling with enzyme kinetics, implementation and simulation in Matlab. Parameter sensitivity analysis, including global sensitivity analysis. Parameter estimation and uncertainty analysis
Stochastic models: master equation, Gillespie simulation algorithm.
Keywords: graph analysis; biochemical networks; parameter sensitivity analysis; parameter estimation; uncertainty analysis; stochastic simulation; graph theory; Maximum Likelihood Estimation; Bayesian inference