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Operon Structure Optimization by Random Self-assembly

  • Yusuke Nakagawa
  • Katsuyuki Yugi
  • Kenji Tsuge
  • Mitsuhiro Itaya
  • Hiroshi Yanagawa
  • Yasubumi Sakakibara
Conference paper
  • 1.6k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5347)

Abstract

Synthetic biology is an emerging research area that aims to investigate natural biological phenomena and reconstruct complex artificial biological systems. Recent development of genetic engineering such as multiple gene assembly method accelerates the synthetic biology study. Ordered gene assembly in Bacillus subtilis (OGAB method) is to assemble multiple genes in one step using an intrinsic B.subtilis plasmid transformation system and enables to reconstitute sets of relevant genes. The OGAB method assembles multiple DNA fragments with a fixed order and orientation and constructs an operon structure in a resultant plasmid. However, the optimal order and orientation to reconstitute a set of genes are generally not trivial and depends on several factors in host bacteria, where the “optimal” means the efficiency of biosynthesis induced by transfered genes in a metabolic pathway. We propose a method to apply self-assembly technique to optimization problem of operon structure. Self-assembly of multiple genes generates all possible orders of genes on operon structure. The number of generated orders on operon structure becomes the factorial of the number of multiple genes. All generated orders of multiple genes are then introduced into E.coli cells and most prominent colony for biosynthesis is extracted. We show some preliminary experiment to construct more efficient orders for five genes in the carotenoid biosynthetic pathway, and found a new order that is more efficient than previous studies for gene order.

Keywords

Synthetic Biology Biosynthesis Gene Carotenoid Biosynthesis Operon Structure Hamiltonian Path Problem 
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 2009

Authors and Affiliations

  • Yusuke Nakagawa
    • 1
  • Katsuyuki Yugi
    • 1
  • Kenji Tsuge
    • 2
  • Mitsuhiro Itaya
    • 2
  • Hiroshi Yanagawa
    • 1
  • Yasubumi Sakakibara
    • 1
    • 3
  1. 1.Department of Biosciences and InformaticsKeio UniversityYokohamaJapan
  2. 2.Institute for Advanced BiosciencesKeio UniversityYamagataJapan
  3. 3.Institute for Bioinformatics Research and Development (BIRD)Japan Science and Technology Agency (JST)Japan

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