Advertisement

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
  • 189 Downloads

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.

Keywords

Systems biology Biochemical systems Genetic programming Simulated annealing Optimization Petri nets 

Notes

Acknowledgments

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.

References

  1. 1.
    Aarts E, Korst J, Michiels W (1989) Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing. Wiley, New York, pp 188–202MATHGoogle Scholar
  2. 2.
    Breitling R, Gilbert D, Heiner M, Orton R (2008) A structured approach for engineering of biochemical network models, illustrated for signalling pathways. Brief Bioinform 9(5):402–404CrossRefGoogle Scholar
  3. 3.
    Calder M, Gilmore S, Hillston J (2004) Modelling the influence of RKIP on the ERK signalling pathway using the stochastic process algebra PEPA. In: Priami C et al (eds) Transactions on computational systems biology. Springer, Berlin, pp 1–23Google Scholar
  4. 4.
    Elliot W, Elliot D (2002) Biochemistry and molecular biology, 2nd edn. Oxford University Press, OxfordGoogle Scholar
  5. 5.
    Fogel G, Corne D (2003) Evolutionary computation in bioinformatics. Morgan Kaufmann, Los Altos, pp 256–276Google Scholar
  6. 6.
    Heaton JT (2008) Introduction to neural networks with java. Heaton Research Inc., Chesterfield, pp 245–266Google Scholar
  7. 7.
    Heiner M, Donaldson R, Gilbert D (2010) Petri nets for systems biology, symbolic systems biology: theory and methods. Jones & Bartlett Learning, Woods Hole, pp 61–97Google Scholar
  8. 8.
    Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680CrossRefMATHMathSciNetGoogle Scholar
  9. 9.
    Kitagawa J, Iba H (2002) Identifying metabolic pathways and gene regulation networks with evolutionary algorithms. In: Fogel G, Corne D (eds) Evolutionary computation in bioinformatics. Elsevier, AmsterdamGoogle Scholar
  10. 10.
    Klingmueller U, Bergelson S, Hsiao JG, Lodish HF (1996) Multiple tyrosine residues in the cytosolic domain of the erythropoietin receptor promote activation of STAT5. Proc Natl Acad Sci USA 93:8324–8328 (JAK-STAT)CrossRefGoogle Scholar
  11. 11.
    Kwang-Hyun C et al (2003) Mathematical modeling of the influence of RKIP on the ERK signaling pathway. In: Priami C (ed) Computational methods in systems biology (CMSB). LNCS, vol 2602. Springer, Berlin, Heidelberg, pp 127–141Google Scholar
  12. 12.
    Oltean M, Grosan C (2003) A Comparison of several linear genetic programming techniques. Complex Syst 14(4):285–313MathSciNetGoogle Scholar
  13. 13.
    Oltean M, Grosan C, Diosan L, Mihaila C (2009) Genetic programming with linear representation: a survey. Int J Artif Intell Tools 18(2):197–238CrossRefGoogle Scholar
  14. 14.
    Rausanu S, Grosan C, Wu Z, Parvu O, Gilbert D (2013) Evolving biochemical systems. In: IEEE congress on evolutionary computation, IEEE CS, pp 1602–1609Google Scholar
  15. 15.
    Sakamoto E, Iba H (2000) Inferring a system of differential equations for a gene regulatory network by using genetic programming. In: Proceedings of the IEEE congress on evolutionary computation, IEEE Service Center, Piscataway, NJGoogle Scholar
  16. 16.
    Voet D, Voet J, Pratt CW (2006) Fundamentals of biochemistry: life at the molecular level. Wiley, New YorkGoogle Scholar
  17. 17.
    Swameye I, Muller TG, Timmer J, Sandra O, Klingmuller U (2003) Identification of nucleocytoplasmic cycling as a remote sensor in cellular signaling by databased modelling. Proc Natl Acad Sci USA 100(3):1028–1033 CrossRefGoogle Scholar
  18. 18.
    Yeung K, Janosch P, McFerran B, Rose DW, Mischak H, Sedivy JM, Kolch W (2000) Mechanism of suppression of the Raf/MEK/extracellular signal regulated kinase pathway by the Raf kinase inhibitor protein. Mol Cell Biol 20(9):3079–3085CrossRefGoogle Scholar
  19. 19.
    Yeung K, Seitz T, Li S, Janosch P, McFerran B, Kaiser C, Fee F, Katsanakis KD, Rose DW, Mischak H, Sedivy JM, Kolch W (1999) Suppression of Raf-1 kinase activity and MAP kinase signaling by RKIP. Nature 401:173–177CrossRefGoogle Scholar
  20. 20.
    Wu Z, Grosan C, Gilbert D (2013) Empirical study of computational intelligence strategies for biochemical systems modelling. In: Nature inspired cooperative strategies for optimization (NICSO). Studies in computational intelligence, vol 512. Springer International Publishing, Switzerland, pp 245–260Google Scholar
  21. 21.
    Wu Z, Yang S, Gilbert D (2012) A hybrid approach to piece-wise modelling of biochemical systems. In: 12th international conference on parallel problem solving from nature, LNCS 7491/2012, pp 519–528Google Scholar
  22. 22.

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

Personalised recommendations