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Bayesian Inference for Systems Biological Models via a Diffusion Approximation
Published on Apr 04, 20075213 Views
As post-genomic biology becomes more predictive, the ability to infer rate parameters (known as reverse-engineering) of biochemical networks will become increasingly important. One approach is to repl
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Bayesian Inference for Systems Biology Models via a Diffusion Approximation00:01
Overview01:06
Computational Systems Biology (CSB)01:54
Modelling02:38
Mass Action Kinetics03:37
Mass Action Kinetics (2)04:43
Markov Process Models05:58
The Gillespie algorithm07:50
Example: Lotka-Volterra09:02
The Lotka-Volterra model10:07
The Lotka-Volterra model10:24
The Lotka-Volterra model10:38
The Lotka-Volterra model10:46
The Lotka-Volterra model10:53
The Lotka-Volterra model11:05
Key differences11:47
Fully Bayesian inference12:19
The Stochastic-Kinetic Diffusion Approximation13:58
Inference for Diffusions15:19
Bayesian Imputation approach16:22
Gibbs Sampling18:06
Irreducible Global MCMC Schemes20:21
Modified Innovation Scheme22:40
Algorithm23:31
Acceptance probabilities24:43
Toy Application: Prokaryotic Auto-Regulation25:41
Simulation Study27:17
Results, m = 10, Gibbs Sampler28:11
Results, m = 10, Innovation Scheme28:43
Results, m = 10, Innovation Scheme29:01
Results, m = 10, Innovation Scheme29:14
Results, m = 10, Innovation Scheme29:29
Results, m = 10, Innovation Scheme29:48
Summary30:26
Contact details...31:10