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Computational models for inferring biochemical networks

  • Advances in Intelligent Data Processing and Analysis
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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.

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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.

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Authors and Affiliations

  1. Department of Computer Science, Babes-Bolyai University, Cluj-Napoca, Romania

    Silvia Rausanu, Crina Grosan & Ramona Stoica

  2. Department of Computer Science, Brunel University, London, UK

    Crina Grosan, Ovidiu Parvu & David Gilbert

  3. College of Information Science and Technology, Jinan University, Guangzhou, People’s Republic of China

    Zujian Wu

Authors
  1. Silvia Rausanu
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  2. Crina Grosan
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  3. Zujian Wu
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  4. Ovidiu Parvu
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  5. Ramona Stoica
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  6. David Gilbert
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Corresponding author

Correspondence to Crina Grosan.

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Rausanu, S., Grosan, C., Wu, Z. et al. Computational models for inferring biochemical networks. Neural Comput & Applic 26, 299–311 (2015). https://doi.org/10.1007/s00521-014-1617-x

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  • DOI: https://doi.org/10.1007/s00521-014-1617-x

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