Computational models for inferring biochemical networks

TY - JOUR

T1 - Computational models for inferring biochemical networks

AU - Rausanu, Silvia

AU - Grosan, Crina

AU - Wu, Zujian

AU - Parvu, Ovidiu

AU - Stoica, Ramona

AU - Gilbert, David

N1 - Funding Information: S. Rausanu acknowledges support from ISDC Romania and C. Grosan acknowledges support from the Romanian National Authority for Scientific Research, CNDI–UEFISCDI, Project No. PN-II-PT-PCCA-2011-3.2-0917. Publisher Copyright: © 2014, Springer-Verlag London.

PY - 2014/2

Y1 - 2014/2

N2 - 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.

AB - 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.

KW - Biochemical systems

KW - Genetic programming

KW - Optimization

KW - Petri nets

KW - Simulated annealing

KW - Systems biology

UR - http://www.scopus.com/inward/record.url?scp=84921699678&partnerID=8YFLogxK

U2 - 10.1007/s00521-014-1617-x

DO - 10.1007/s00521-014-1617-x

M3 - Article

AN - SCOPUS:84921699678

SN - 0941-0643

VL - 26

SP - 299

EP - 311

JO - NEURAL COMPUTING AND APPLICATIONS

JF - NEURAL COMPUTING AND APPLICATIONS

IS - 2

ER -