Stochastic approximation of signalling pathway dynamics

CREATED: 200809290400 Speaker: Bing Liu ** Motivation

• reactions in pathway described using ODE
• solving ODE needs to use small time step, time consuming
• Model analyses require a large number of simulations ** parameter estimation ** global sensitivity analysis ** perturbation analysis
• Biological systems are noisy
• Input data are coarse ** Approach using PGM
• approximate deterministic ODE using PGM
• pose questions as inference problems
• use discrete numbers to represent intervals
• a trajectory is sequence of states
• the dynamics is the set of all possible trajectories $\rightarrow$ state transition graph
• state transition graph $\rightarrow$ Dynamic Bayesian Network
• model analysis $\rightarrow$ Bayesian inference