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http://bura.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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