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Learning and Inference in Computational Systems Biology
Pascal

Parameter estimation using moment-closure methods

author: Colin Gillespie

Description

This poster will give tackle one of the key problems in the new science of systems biology: inference for the rate parameters underlying complex stochastic kinetic biochemical network models, using partial, discrete, and noisy time-course measurements of the system state. Although inference for exact stochastic models is possible, it is computionally intensive for relatively small networks. We explore Bayesian estimation of stochastic kinetic rate parameters using approximate models, based on moment closure analysis of the underlying stochastic process. By assuming a Gaussian distribution and using moment-closure estimates of the first two-moments, we can greatly increase the speed of parameter inference. The parameter space can be efficiently explored by embedding this approximation into an MCMC procedure.

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Slides
0:00 Parameter estimation using moment closure methods
0:00 Modelling - 1
0:43 Modelling - 2
1:07 Modelling - 3
1:13 Modelling - 4
1:29 Modelling - 5
1:44 Simulation techniques
4:10 Moment equations - 1
5:02 Moment equations - 2
5:42 Moment equations - 3
6:16 Moment closure - 1
7:06 Moment closure - 2
7:55 Moment closure - 3
8:25 Parameter inference - 1
8:55 Parameter inference - 2
8:57 Parameter inference - 3
9:17 Parameter inference - 4
9:23 Parameter inference - 5
9:54 Parameter inference - 6
10:49 Technicalities
11:39 Example 1: Immigration-death model
12:18 Example 1: Immigration-death model - Results
12:55 Example 2: Michaelis-Menten
13:39 Example 2: Michaelis-Menten inference - 1
14:14 Example 2: Michaelis-Menten inference - 2
14:46 Example 2: Michaelis-Menten QSSA inference
15:11 Example 3: Prokaryotic auto-regulatory gene network
15:43 Conclusions and future work
17:18 - Questions

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