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|Title:||Computational models for inferring biochemical networks|
|Keywords:||Biochemical systems;Genetic programming;Optimization;Petri nets;Simulated annealing;Systems biology|
|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.|
|Appears in Collections:||Dept of Computer Science Research Papers|
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