Name
Abstract | ||
|---|---|---|
An important aspect of systems biology research is the so-called "reverse engineering" of cellular metabolic dynamics from measured input-output data. This allows researchers to estimate and validate both the pathway's structure as well as the kinetic constants. In this paper, a regularization based method which performs model structure selection is developed and applied to the problem of analyzing how existing pathway knowledge can be used as a prior investigate the model change complexity/sensitivity trade-off. Specifically, a 1-norm prior on parameter deviations from an existing model of the I kappa B-NF-kappa B pathway is combined with new experimental data and an analysis is performed to determine which are the most relevant components to alter. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/IJCNN.2008.4634363 | 2008 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-8 |
Keywords | Field | DocType |
kinetics,system biology,neural networks,artificial neural networks,biology,reverse engineering,input output,identification | Biological system,Experimental data,Computer science,Reverse engineering,Systems biology,Regularization (mathematics),Artificial intelligence,Artificial neural network,Machine learning | Conference |
ISSN | Citations | PageRank |
2161-4393 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fei He | 1 | 32 | 13.85 |
| M Brown | 2 | 106 | 15.73 |
| Lam Fat Yeung | 3 | 20 | 5.39 |