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Estimating Parameters and Hidden Variables in a Non-linear State-space Model of Regulatory Networks
Published on Apr 04, 20077516 Views
Understanding and identifying biological complex systems at work in the cell requires to develop models able to capture the stochastic nature of biological processes as well as their dynamics. Focusin
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Chapter list
Estimating Parameters and Hidden Variables in a Nonlinear State-space Model of Biological Networks00:01
Outline01:04
Outline01:15
Biological networks01:17
Quantitative models of Biological Networks02:32
Reverse Engineering of Biological Networks03:21
Reverse Engineering of Biological Networks03:54
Outline04:05
Nonlinear State-Space Model04:06
Nonlinear State-Space Model04:48
Outline05:25
Bayesian inference05:33
Bayesian inference06:28
Recursive Bayesian Filtering06:39
Recursive Bayesian Filtering07:14
Recursive Bayesian Filtering08:04
Nonlinear SSM -> Approximate Solutions08:34
Gaussian Approximations09:35
Nonlinear transformation10:14
Unscented Kalman Filter11:14
Parameter Estimation12:23
Outline12:59
Repressilator13:01
Synthetic data15:29
Parameter Estimation15:52
Parameter Estimation16:38
State Estimation16:51
Outline17:18
JAK-STAT signaling pathway17:22
Prediction vs Experimental data18:47
Conclusion19:14