Bio-inspired reverse engineering of regulatory networks

Authors:
Cristina Costa Santini;Gunnar Tufte;Pauline Haddow
Affiliations:
CRAB Lab, Department of Computer and Information Science, Norwegian University of Science and Technology, Norway;CRAB Lab, Department of Computer and Information Science, Norwegian University of Science and Technology, Norway;CRAB Lab, Department of Computer and Information Science, Norwegian University of Science and Technology, Norway
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

Citing 2
Cited 0

Adaptation in natural and artificial systems

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Comparing association network algorithms for reverse engineering of large-scale gene regulatory networks

Bioinformatics

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Abstract

Regulatory networks are complex networks. This paper addresses the challenge of modelling these networks. The Boolean representation is chosen and supported as a suitable representation for an abstract approach. In in-silico experiments, two different bio-inspired techniques are applied to the reverse engineering of a Boolean regulatory network: as a search method a Genetic Algorithm is applied and an indirect method based on Artificial Development and tuned to this application, is proposed. Both methods are challenged at reverse engineering a known network - the yeast cell-cycle network model. Presented results show that they are both successful in reverse engineering the considered network.