Artificial bee colony algorithm-neural networks for S-system models of biochemical networks approximation

• Published in: Neural computing & applications Vol. 21; no. 2; pp. 365 - 375
• Main Authors: Yeh, Wei-Chang, Hsieh, Tsung-Jung
• Format: Journal Article
• Language: English
• Published: London Springer-Verlag 01.03.2012
• Subjects: Algorithms; Artificial Intelligence; Circuits; Colonies; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer applications; Computer Science; Data Mining and Knowledge Discovery; Data processing; Gene expression; Image Processing and Computer Vision; Information systems; Kinetics; Learning; Mathematical models; metabolic networks; Neural networks; Original Article; Probability and Statistics in Computer Science; Signal transduction; S-system models; Artificial bee colony (ABC) algorithm; Artificial neural network; Gene expression
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-010-0435-z

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.