Current Progress in Static and Dynamic Modeling of Biological Networks

2010 ◽  
pp. 13-73 ◽  
Author(s):  
Bernie J. Daigle ◽  
Balaji S. Srinivasan ◽  
Jason A. Flannick ◽  
Antal F. Novak ◽  
Serafim Batzoglou
Keyword(s):  
Dynamic Modeling ◽  
Biological Networks

Dynamic Modeling and Parameter Identification for Biological Networks

2011 ◽  
pp. 478-510 ◽  
Author(s):  
Fortunato Bianconi ◽  
Gabriele Lillacci ◽  
Paolo Valigi
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Extended Kalman Filter ◽  
Dynamic Modeling ◽  
Biological Networks ◽  
In Silico ◽  
High Gain ◽  
The Other ◽  
Sensitivity Analyses ◽  
Identification Techniques

Then, two different parameter identification techniques are presented for the proposed models. One is based on a least squares procedure, which treats the signals provided by a high gain observer; the other one is based on a Mixed Extended Kalman Filter. Prior to the estimation phase, identifiability and sensitivity analyses are used to determine which parameters can be and/or should be estimated. The procedures are tested and compared by means of data obtained by in silico experiments.

Dynamic Modeling and Wear-Based Remaining Useful Life Prediction of High Power Clutch Systems

2005 ◽  
Vol 48 (2) ◽  
pp. 208-217 ◽  
Author(s):  
Matthew Watson ◽  
Carl Byington ◽  
Douglas Edwards ◽  
Sanket Amin
Keyword(s):  
Dynamic Modeling ◽  
High Power ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Useful Life

Hierarchical Bayesian continuous time dynamic modeling.

2018 ◽  
Vol 23 (4) ◽  
pp. 774-799 ◽  
Author(s):  
Charles C. Driver ◽  
Manuel C. Voelkle
Keyword(s):  
Dynamic Modeling ◽  
Continuous Time ◽  
Hierarchical Bayesian ◽  
Time Dynamic
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