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Mass-Dispersed Gravitational Search Algorithm for Gene Regulatory Network Model Parameter Identification

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Simulated Evolution and Learning (SEAL 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7673))

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Abstract

The interaction mechanisms at the molecular level that govern essential processes inside the cell are conventionally modeled by nonlinear dynamic systems of coupled differential equations. Our implementation adopts an S-system to capture the dynamics of the gene regulatory network (GRN) of interest. To identify a solution to inverse problem of GRN parameter identification the gravitational search algorithm (GSA) is adopted here. Contributions made in the present paper are twofold. Firstly the bias of GSA toward the center of the search space is reported. Secondly motivated by observed center-seeking (CS) bias of GSA, mass-dispersed gravitational search algorithm (mdGSA) is proposed here. Simulation results on a set of well-studied mathematical benchmark problems and two gene regulatory networks confirms that the proposed mdGSA is superior to the standard GSA, mainly duo to its reduced CS bias.

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Authors and Affiliations

  1. Faculty of Technology, Policy and Management, Delft University of Technology, The Netherlands

    Mohsen Davarynejad & Jan van den Berg

  2. Department of Molecular Cell Biology, Leiden University Medical Center (LUMC), The Netherlands

    Zary Forghany

Authors
  1. Mohsen Davarynejad
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  2. Zary Forghany
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  3. Jan van den Berg
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Editor information

Editors and Affiliations

  1. Faculty of Information Technology, Le Quy Don Technical University, 100 Hoang Quoc Viet Street, Cau Giay District,, Hanoi, Vietnam

    Lam Thu Bui

  2. School of Computer Engineering, Nanyang Technological University, Block N4, 2b-39, Nanyang Avenue, 639798,, Singapore, Singapore

    Yew Soon Ong

  3. Faculty of Information Technology,, Hanoi University, km9 Nguyen Trai Road, Hanoi, Vietnam

    Nguyen Xuan Hoai

  4. Graduate School of Engineering, Department of Computer Science, Osaka Prefecture University, 1-1 Gakuen-cho, Nakaku, 599-8531, Sakai, Osaka, Japan

    Hisao Ishibuchi

  5. School of Electrical and Electronic Engineering, Nanyang Technological University, Block N4, 2b-39, Nanyang Avenue, 639798, Singapore, Singapore

    Ponnuthurai Nagaratnam Suganthan

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© 2012 Springer-Verlag Berlin Heidelberg

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Davarynejad, M., Forghany, Z., van den Berg, J. (2012). Mass-Dispersed Gravitational Search Algorithm for Gene Regulatory Network Model Parameter Identification. In: Bui, L.T., Ong, Y.S., Hoai, N.X., Ishibuchi, H., Suganthan, P.N. (eds) Simulated Evolution and Learning. SEAL 2012. Lecture Notes in Computer Science, vol 7673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34859-4_7

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  • DOI: https://doi.org/10.1007/978-3-642-34859-4_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34858-7

  • Online ISBN: 978-3-642-34859-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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