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

  • Mohsen Davarynejad
  • Zary Forghany
  • Jan van den Berg
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7673)


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.


Gravitational search algorithm Center-seeking bias Mass-dispersed gravitational search algorithm Gene regulatory network model identification 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Mohsen Davarynejad
    • 1
  • Zary Forghany
    • 2
  • Jan van den Berg
    • 1
  1. 1.Faculty of Technology, Policy and ManagementDelft University of TechnologyThe Netherlands
  2. 2.Department of Molecular Cell BiologyLeiden University Medical Center (LUMC)The Netherlands

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