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Detecting Gene Regulatory Networks from Microarray Data Using Fuzzy Logic

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Book cover Fuzzy Systems in Bioinformatics and Computational Biology

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 242))

Summary

With the arrival of high-throughput genomic data, biologists now have the ability to investigate the expression of genetic transcripts on a genome-wide scale. With this advancement, it is important to consider the regulation of gene expression in the context of a system, including the discovery of any genetic interactions that contribute to regulation. Genetic networks provide a concise representation of the interaction between multiple genes at the system level, giving investigators a broader view of the cellular state compared to a singular declaration of whether a gene is over/under expressed. Many methods currently exist to infer gene regulatory networks, including discrete models (Boolean networks, Bayesian networks), continuous models (weight matrices, differential equations models), and fuzzy logic models. The attractive feature of the fuzzy logic model is that it allows for a simplified rule structure, since observations are categorized, but retains information in the original data by allowing partial membership in multiple categories. The fuzzy logic model is flexible, and can be adapted to a variety of regulatory models and inferential rule sets. In this work, we review several recent advances in fuzzy logic methodologies developed for the genetic network reconstruction problem. The goals of the approaches range from whole genome screening of microarray data for small regulatory units, to detailed reconstruction of the iteractions between genes in a particular pathway. We apply the methods to real microarray data concerning the yeast cell cycle and simulated data concerning the Raf signaling pathway, and compare results with other well-known algorithms.

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Author information

Authors and Affiliations

  1. University of Louisville, Louisville, KY, USA

    Guy N. Brock & Vasyl Pihur

  2. The Ohio State University, Columbus, OH, USA

    Laura Kubatko

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  1. Guy N. Brock
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  2. Vasyl Pihur
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  3. Laura Kubatko
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Editors and Affiliations

  1. Honda Research Institute Honda Research Institute Europe , Offenbach, Germany

    Yaochu Jin

  2. Nanyang Technological University, Singapore

    Lipo Wang

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Brock, G.N., Pihur, V., Kubatko, L. (2009). Detecting Gene Regulatory Networks from Microarray Data Using Fuzzy Logic. In: Jin, Y., Wang, L. (eds) Fuzzy Systems in Bioinformatics and Computational Biology. Studies in Fuzziness and Soft Computing, vol 242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89968-6_8

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  • DOI: https://doi.org/10.1007/978-3-540-89968-6_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89967-9

  • Online ISBN: 978-3-540-89968-6

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