Soft Computing Approach for Modeling Genetic Regulatory Networks

  • Khalid Raza
  • Rafat Parveen
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 178)


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.


Particle Swarm Optimization Fuzzy Rule Gene Regulatory Network Soft Computing Genetic Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Computer ScienceJamia Millia Islamia (Central University)New DelhiIndia

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