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Model Reduction for Parameter Estimation
Published on Apr 04, 20076550 Views
Estimating parameters in biochemical network models is a central but often difficult problem. A general approach that may be worth developing further is first to seek simplified or "reduced" mode
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Chapter list
Model Reduction for Parameter Estimation00:03
Topics00:37
Transcriptional Gene Regulation Networks01:27
GRN Parameter Optimization02:34
GRN ANN Equations ’9105:31
Model Reduction Example: Gene Regulation Network Derived from Stat Mech06:10
Dynamical Model Reduction via Clustering07:45
Core/Leaf Model Inference10:15
SDE Advantages12:17
Hierarchical Cooperative Activation: Alternative diagram notations12:58
Hierarchical Cooperative Activation Model (HCA)13:39
How to model transcriptional regulation?13:58
Hard vs. Soft Logic14:25
HCA- Z and ANN-like Equations15:28
GRSN: Gene Regulation + Signal Transduction Network16:51
Arabidopsis Shoot Apical Meristem (SAM)17:59
WUS18:29
CLV3/WUS networks19:14
Biological scale hierarchies20:47
Dynamical Grammar Aims22:10
Elementary Reactions23:57
Elementary Processes24:14
Elementary process models24:52
SPG Modeling Language: Semantics Semantic map Y: GH from Grammar to Stochastic Process26:02
Time Ordered Product Expansion (TOPE)26:59
Model Reduction for Dynamical Systems27:42
Composition vs. Specialization in a Lattice of Models29:22
A Parameter Estimation Future31:07
Conclusions31:59
For further information: www.ics.uci.edu/~emj www.computableplant.org Funding: US National Science Foundation FIBR program, NIH BISTI program Invitation…32:47