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Estimation of Neuronal Signaling Model Parameters using Deterministic and Stochastic in Silico Training Data: Evaluation of Four Parameter Estimation Methods

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5 Author(s)
Antti Pettinen ; Institute of Signal Processing, Tampere University of Technology, P.O. Box 553, FI-33101 Tampere, Finland. ; Tiina Manninen ; Olli Yli-Harja ; Keijo Ruohonen
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This study evaluates parameter estimation methodology in the context of neuronal signaling networks. Based on the results of a previous study, four parameter estimation methods, Evolutionary Programming, Genetic Algorithm, Multistart, and Levenberg-Marquardt, are selected. All the reaction rate constants of the test case, the protein kinase C (PKC) pathway model, are estimated using the selected four methods. The estimations are done with both error and disturbance free training data from deterministic 1/1 silica simulations and with more realistic training data from stochastic in silica simulations. The results show that in overall the evolution based algorithms perform well. However, there is a clear need for further development, especially when utilizing more realistic training data.

Published in:

2007 IEEE International Workshop on Genomic Signal Processing and Statistics

Date of Conference:

10-12 June 2007