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Soft Computing Approach for Modeling Genetic Regulatory Networks

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Advances in Computing and Information Technology

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 178))

Abstract

Interactions among the cellular components determine the behaviour of the complex biological system. The major challenge of the post-genomic era is to understand how interactions among various molecules in a cell determine its form and function. Several computational techniques for modeling biological systems, particularly gene regulatory networks (GRNs), has been proposed in order to understand the complex biological interactions and behaviours. Gene regulatory models has been proved to be the most widely used mechanism to model, analyze and predict the behaviour of an organism. In this paper, we have reviewed the role of soft computing techniques, such as fuzzy logic, artificial neural networks, evolutionary algorithms and their hybridization, for modeling GRNs. In addition, recent developments in this area are introduced and various challenges and opportunities for future research are discussed.

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Authors and Affiliations

  1. Department of Computer Science, Jamia Millia Islamia (Central University), New Delhi, India

    Khalid Raza & Rafat Parveen

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  1. Khalid Raza
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  2. Rafat Parveen
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Correspondence to Khalid Raza .

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Editors and Affiliations

  1. , Department of Computer Science, Jackson State University, John R. Lynch Street 1400, Jackson, 39217, Mississippi, USA

    Natarajan Meghanathan

  2. Wireilla Net Solutions PTY ltd, Merlow Street 3, Melbourne, 3207, Australia

    Dhinaharan Nagamalai

  3. , Department of Computer Science & Eng., University of Calcutta, Calcutta, 700 073, India

    Nabendu Chaki

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Raza, K., Parveen, R. (2013). Soft Computing Approach for Modeling Genetic Regulatory Networks. In: Meghanathan, N., Nagamalai, D., Chaki, N. (eds) Advances in Computing and Information Technology. Advances in Intelligent Systems and Computing, vol 178. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31600-5_1

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  • DOI: https://doi.org/10.1007/978-3-642-31600-5_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31599-2

  • Online ISBN: 978-3-642-31600-5

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