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Application of Learning Classifier Systems to Gene Expression Analysis in Synthetic Biology

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Nature-Inspired Computing and Optimization

Part of the book series: Modeling and Optimization in Science and Technologies ((MOST,volume 10))

Abstract

Learning classifier systems (LCS) are algorithms that incorporate genetic algorithms with reinforcement learning to produce adaptive systems described by if-then rules. As a new interdisciplinary branch of biology, synthetic biology pursues the design and construction of complex artificial biological systems from the bottom-up. A trend is growing in designing artificial metabolic pathways that show previously undescribed reactions produced by the assembly of enzymes from different sources in a single host. However, few researchers have succeeded thus far because of the difficulty in analyzing gene expression. To tackle this problem, data mining and knowledge discovery are essential. In this context, nature-inspired LCS are well suited to extracting knowledge from complex systems and thus can be exploited to investigate and utilize natural biological phenomena. This chapter focuses on applying LCS to gene expression analysis in synthetic biology. Specifically, it describes the optimization of artificial operon structure for the biosynthesis of metabolic pathway products in Escherichia coli. This optimization is achieved by manipulating the order of multiple genes within the artificial operons.

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Acknowledgements

Dr. Yoshihiko Hasegawa and Dr. Hiroshi Dohi of The University of Tokyo give insightful discussions that were significant in this analysis of the operon structure. The authors would also like to especially thank UTDS members Marishi Mochida, Akito Misawa, Takahiro Hashimoto, Kazuki Taniyoshi, Yuki Inoue and Mari Sasaki for making a writing environment so pleasant.

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Authors and Affiliations

  1. Graduate School of Information Science and Technology, The University of Tokyo, 122B1 Engineering Building 2, 7-3-1 Hongo, Bunkyo, Tokyo, Japan

    Changhee Han

  2. Institute for Advanced Biosciences, Keio University, 403-1 Nipponkoku, Daihoji, Tsuruoka, Yamagata, Japan

    Kenji Tsuge

  3. Graduate School of Information Science and Technology, The University of Tokyo, 101D4 Engineering Building 2, 7-3-1 Hongo, Bunkyo, Tokyo, Japan

    Hitoshi Iba

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  1. Changhee Han
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Correspondence to Changhee Han .

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Editors and Affiliations

  1. Dept of Computer Science and Engineering, SOA University Dept of Computer Science and Engineering, Bhubaneswar, Odisha, India

    Srikanta Patnaik

  2. School of Science and Technology, Middlesex University School of Science and Technology, London, United Kingdom

    Xin-She Yang

  3. School of H.S.E., University of Hyogo School of H.S.E., Himeji, Japan

    Kazumi Nakamatsu

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Han, C., Tsuge, K., Iba, H. (2017). Application of Learning Classifier Systems to Gene Expression Analysis in Synthetic Biology. In: Patnaik, S., Yang, XS., Nakamatsu, K. (eds) Nature-Inspired Computing and Optimization. Modeling and Optimization in Science and Technologies, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-319-50920-4_10

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  • DOI: https://doi.org/10.1007/978-3-319-50920-4_10

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