Artificial bee colony algorithm-neural networks for S-system models of biochemical networks approximation
- 323 Downloads
High-throughput technologies nowadays allow for the acquisition of biological data. These temporal profiles carry topological and kinetic information regarding the biochemical network from which they were drawn. Retrieving this information requires systematic application of both experimental and computational methods. S-systems are nonlinear mathematical approximate models based on the power-law formalism and provide a general framework for the simulation of integrated biological systems exhibiting complex dynamics, such as genetic circuits, signal transduction, and metabolic networks. However, S-systems need lots of iterations to obtain convergent gene expression profiles. For this reason, this study constructed a substitutive approach using artificial neural networks (ANNs) based on the artificial bee colony (ABC) algorithm with learning and training processes. This was used to obtain models and prove that our model (called ABC-NN) certainly is another method to acquire convergent gene expressions, except for S-systems, supported by our testing results.
KeywordsS-system models Artificial bee colony (ABC) algorithm Artificial neural network Gene expression
The authors are highly grateful to referees and Dr. John Maclntyre, Editor-in-Chief, for their constructive comments and recommendations, which have significantly improved the presentation of this paper.
- 3.Goodacre R, Harrigan GG (2003) Metabolite profiling: its role in biomarker discovery and gene function analysis. Kluwer, DordrechtGoogle Scholar
- 8.Guille′n A, Pomares H, Rojas I, Gonza′lez J, Herrera LJ, Rojas F, Valenzuela O (2008) Studying possibility in a clustering algorithm for RBFNN design for function approximation. Neural Comput Appl 17:75–89Google Scholar
- 11.Socha K, Blum C (2007) An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training. Neural Comput Appl 3:235–247Google Scholar
- 17.Karaboga D, Ozturk C (2009) Neural networks training by artificial bee colony algorithm on pattern classification. Neural Netw World 19:279–292Google Scholar
- 21.Voit EO (2000) Computational analysis of biochemical systems. Cambridge University Press, CambridgeGoogle Scholar
- 23.Basturk B, Karaboga D (2006) An artificial bee colony (abc) algorithm for numeric function optimization. In: IEEE Swarm intelligence symposium, Indianapolis Indiana USA MayGoogle Scholar
- 25.Kenedy J, Eberhart R (1995) Particle swarm optimization. In Proceedings of the IEEE international conference on neural networks, 1942–1948Google Scholar
- 26.Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann ArborGoogle Scholar