Neural Computing and Applications

, Volume 26, Issue 2, pp 299–311 | Cite as

Computational models for inferring biochemical networks

  • Silvia Rausanu
  • Crina Grosan
  • Zujian Wu
  • Ovidiu Parvu
  • Ramona Stoica
  • David Gilbert
Advances in Intelligent Data Processing and Analysis


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.


Systems biology Biochemical systems Genetic programming Simulated annealing Optimization Petri nets 



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.


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Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • Silvia Rausanu
    • 1
  • Crina Grosan
    • 1
    • 2
  • Zujian Wu
    • 3
  • Ovidiu Parvu
    • 2
  • Ramona Stoica
    • 1
  • David Gilbert
    • 2
  1. 1.Department of Computer ScienceBabes-Bolyai UniversityCluj-NapocaRomania
  2. 2.Department of Computer ScienceBrunel UniversityLondonUK
  3. 3.College of Information Science and TechnologyJinan UniversityGuangzhouPeople’s Republic of China

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