Parameter estimation in genetic networks using a constrained stochastic space search method

J. Omony, L.H. de Graaff, G. van Straten, A.J.B. van Boxtel

Research output: Chapter in Book/Report/Conference proceedingAbstract

Abstract

Numerous stochastic search methods have been applied in parameter estimation problems in genetic network identification. In this work, a constrained stochastic space search (CSSS) method for parameter estimation is proposed and used to optimize the goal function for the difference between measured and estimated gene expression time series data. Both linear and nonlinear model formalism were used. The performance of the proposed optimization method was compared to another robust stochastic algorithm(ICRS/DS), which is a modification of the ICRS algorithm [1]. Even though, the ICRS/DS method was shown to be robust, the problemwith using it is that thismethod requiresmaking heuristic guesses of various tuning parameters for initialization. The ICRS/DS also takes a long time to achieve convergence to optimum solutions. To address these problems an alternative method (the CSSS) is introduced, a method uses a technique of variance scaling on the parameters. This avoids the necessity to make heuristic guesses and speeds up the optimization process. The CSSS algorithmis fast and efficient when applied to less noisy time series data sets from small-sized genetic networks.
Original languageEnglish
Title of host publication30th Benelux Meeting on Systems and Control, Lommel, Belgium, 15 - 17 March, 2011
Pages129-129
Publication statusPublished - 2011
Event30th Benelux Meeting on Systems and Control, Lommel, Belgium -
Duration: 15 Mar 201117 Mar 2011

Conference

Conference30th Benelux Meeting on Systems and Control, Lommel, Belgium
Period15/03/1117/03/11

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