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Gene regulatory network model identification using artificial bee colony and swarm intelligence

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3 Author(s)
Forghany, Z. ; Gorlaeus Lab., Leiden Univ., Leiden, Netherlands ; Davarynejad, M. ; Snaar-Jagalska, B.E.

Gene association/interaction networks have complex structures that provide a better understanding of mechanisms at the molecular level that govern essential processes inside the cell. The interaction mechanisms are conventionally modeled by nonlinear dynamic systems of coupled differential equations (S-systems) adhering to the power-law formalism. Our implementation adopts an S-system that is rich enough in structure to capture the dynamics of the gene regulatory networks (GRN) of interest. A comparison of three widely used population-based techniques, namely evolutionary algorithms (EAs), local best particle swarm optimization (PSO) with random topology, and artificial bee colony (ABC) are performed in this study to rapidly identify a solution to inverse problem of GRN reconstruction for understanding the dynamics of the underlying system. A simple yet effective modification of the ABC algorithm, shortly ABC* is proposed as well and tested on the GRN problem. Simulation results on two small-size and a medium size hypothetical gene regulatory networks confirms that the proposed ABC* is superior to all other search schemes studied here.

Published in:

Evolutionary Computation (CEC), 2012 IEEE Congress on

Date of Conference:

10-15 June 2012

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