Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/9575
Title: Computational models for inferring biochemical networks
Authors: Rausanu, S
Grosan, C
Wu, Z
Parvu, O
Stoica, R
Gilbert, D
Keywords: Biochemical systems;Genetic programming;Optimization;Petri nets;Simulated annealing;Systems biology
Issue Date: 2014
Publisher: Springer London
Citation: Neural Computing and Applications, (12 June 2014)
Abstract: Biochemical networks are of great practical importance. The interaction of biological compounds in cells has been enforced to a proper understanding by the numerous bioinformatics projects, which contributed to a vast amount of biological information. The construction of biochemical systems (systems of chemical reactions), which include both topology and kinetic constants of the chemical reactions, is NP-hard and is a well-studied system biology problem. In this paper, we propose a hybrid architecture, which combines genetic programming and simulated annealing in order to generate and optimize both the topology (the network) and the reaction rates of a biochemical system. Simulations and analysis of an artificial model and three real models (two models and the noisy version of one of them) show promising results for the proposed method.
URI: http://link.springer.com/article/10.1007%2Fs00521-014-1617-x
http://bura.brunel.ac.uk/handle/2438/9575
DOI: http://dx.doi.org/10.1007/s00521-014-1617-x
ISSN: 0941-0643
Appears in Collections:Dept of Computer Science Research Papers

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