caa a22 4500 606160469 CHVBK 20210128100630.0 cr unu---uuuuu 210128e20150201xx s 000 0 eng 10.1007/s00521-014-1617-x doi (NATIONALLICENCE)springer-10.1007/s00521-014-1617-x Computational models for inferring biochemical networks [Elektronische Daten] [Silvia Rausanu, Crina Grosan, Zujian Wu, Ovidiu Parvu, Ramona Stoica, David Gilbert] 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. Springer-Verlag London, 2014 Systems biology nationallicence Biochemical systems nationallicence Genetic programming nationallicence Simulated annealing nationallicence Optimization nationallicence Petri nets nationallicence Rausanu Silvia Department of Computer Science, Babes-Bolyai University, Cluj-Napoca, Romania aut Grosan Crina Department of Computer Science, Babes-Bolyai University, Cluj-Napoca, Romania aut Wu Zujian College of Information Science and Technology, Jinan University, Guangzhou, People's Republic of China aut Parvu Ovidiu Department of Computer Science, Brunel University, London, UK aut Stoica Ramona Department of Computer Science, Babes-Bolyai University, Cluj-Napoca, Romania aut Gilbert David Department of Computer Science, Brunel University, London, UK aut Neural Computing and Applications Springer London 26/2(2015-02-01), 299-311 0941-0643 26:2<299 2015 26 521 https://doi.org/10.1007/s00521-014-1617-x text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1617-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Rausanu Silvia Department of Computer Science, Babes-Bolyai University, Cluj-Napoca, Romania aut NATIONALLICENCE 700 1- Grosan Crina Department of Computer Science, Babes-Bolyai University, Cluj-Napoca, Romania aut NATIONALLICENCE 700 1- Wu Zujian College of Information Science and Technology, Jinan University, Guangzhou, People's Republic of China aut NATIONALLICENCE 700 1- Parvu Ovidiu Department of Computer Science, Brunel University, London, UK aut NATIONALLICENCE 700 1- Stoica Ramona Department of Computer Science, Babes-Bolyai University, Cluj-Napoca, Romania aut NATIONALLICENCE 700 1- Gilbert David Department of Computer Science, Brunel University, London, UK aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/2(2015-02-01), 299-311 0941-0643 26:2<299 2015 26 521