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Dynamic Interaction Networks and Global Ontology-Based Modelling of Brain Dynamics

  • Nikola Kasabov
  • Lubica Benuskova

Abstract

With the advancements of bioinformatics and brain research technologies more and more data becomes available tracing the activity of genes, neurons, neural networks and brain areas over time. How can such data be used to create dynamic models that capture dynamic interactions at a particular functional level and across levels over time? The paper addresses these questions through dynamic interaction network (DIN) modelling: first, at a genetic level, a Gene Regulatory Network (GRN) model can be created from a time series gene expression data; second, at a cognitive level, a DIN can be created from a time series of data (e.g. LFP/EEG data) related to perceptual or cognitive functions; and third, a DIN model can be developed for cross-level dynamic interactions, e.g. between GRN and brain signals measured as LFP/EEG. We conclude with introducing brain-gene ontology integrated environment for representing and modelling brain-gene dynamic relations.

Keywords

Dynamic interaction networks gene regulatory networks neuroinformatics brain-gene ontology gene expression data EEG 

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References

  1. 1.
    Benuskova, L., Kasabov, N.: Computational Neurogenetic Modeling. Springer, New York (2007)Google Scholar
  2. 2.
    Chan, Z., Kasabov, N., Collins, L.: A Two-Stage Methodology for Gene Regulatory Network Extraction from Time-Course Gene Expression Data. Expert Systems with Applications: An International Journal 30 (2006) 59–63CrossRefGoogle Scholar
  3. 3.
    Chan, Z. S. H., Collins, L., Kasabov, N. K.: Bayesian Learning of Sparse Gene Regulatory Networks. Biosystems 87 (2007) 299–306PubMedCrossRefGoogle Scholar
  4. 4.
    Kasabov, N., Dimitrov, D.: A Method for Gene Regulatory Network Modelling with the Use of Evolving Connectionist Systems. In: ICONIP. IEEE Press, Singapore (2002)Google Scholar
  5. 5.
    Kasabov, N., Chan, S. H., Jain, V., Igor, S., Dimiter, D.: Computational Modeling of Gene Regulatory Networks. In: Bajic, V. B., Wee, T. T. (eds.): Information Processing and Living Systems. World Scientific (2005) 673–686Google Scholar
  6. 6.
    Benuskova, L., Jain, V., Wysoski, S. G., Kasabov, N.: Computational neurogenetic modelling: a pathway to new discoveries in genetic neuroscience. Intl. J. Neural Systems 16 (2006) 215–227CrossRefGoogle Scholar
  7. 7.
    Kasabov, N. K.: Evolving Connectionist Systems. Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines. Springer-Verlag, London (2003)Google Scholar
  8. 8.
    Kasabov, N.: Evolving Connectionist Systems. The Knowledge Engineering Approach. Springer, New York (2007)Google Scholar
  9. 9.
    Kasabov, N., Song, Q.: DENFIS: Dynamic, Evolving Neural-Fuzzy Inference Systems and its Application for Time-Series Prediction. IEEE Trans. Fuzzy Systems 10 (2002) 144–154CrossRefGoogle Scholar
  10. 10.
    Buzsaki, G., Draguhn, A.: Neuronal Oscillations in Cortical Networks. Science 304 (2004) 1926–1930PubMedCrossRefGoogle Scholar
  11. 11.
    Villa, A. E. P., Asai, Y., Tetko, I. V., Pardo, B., Celio, M. R., Schwaller, B.: Cross-Channel Coupling of Neuronal Activity in Parvalbumin-Deficient Mice Susceptible to Epileptic Seizures. Epilepsia 46 (2005) 359Google Scholar
  12. 12.
    Kasabov, N., Benuskova, L.: Computational Neurogenetics. Journal of Computational and Theoretical Nanoscience 1 (2004) 47–61CrossRefGoogle Scholar
  13. 13.
    Kasabov, N., Benuskova, L., Wysoski, S. G.: Biologically Plausible Computational Neurogenetic Models: Modeling the Interaction between Genes, Neurons and Neural Networks. Journal of Computational and Theoretical Nanoscience 2 (2005) 569–573CrossRefGoogle Scholar
  14. 14.
    Kasabov, N., Jain, V., Gottgtroy, P. C. M., Benuskova, L., Joseph, F.: Brain Gene Ontology and Simulation System (BGOS) for a Better Understanding of the Brain. Cybernetics and Systems 38 (2007) 495–508CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Nikola Kasabov
    • 1
  • Lubica Benuskova
    • 1
  1. 1.Knowledge Engineering and Discovery Research InstituteAuckland University of TechnologyAucklandNew Zealand

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