Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 409991
Title Parameter estimation in genetic networks using a constrained stochastic space search method
Author(s) Omony, J.; Graaff, L.H. de; Straten, G. van; Boxtel, A.J.B. van
Source In: 30th Benelux Meeting on Systems and Control, Lommel, Belgium, 15 - 17 March, 2011. - - p. 129 - 129.
Event 30th Benelux Meeting on Systems and Control, Lommel, Belgium, 2011-03-15/2011-03-17
Department(s) Systems and Control Group
Systems and Synthetic Biology
Publication type Abstract in scientific journal or proceedings
Publication year 2011
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.
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