Application of Evolutionary Algorithms for the Optimization of Genetic Regulatory Networks

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• 关键词：Synthetic biology ; EASEA ; Gene regulatory networks ; Design automation ; Biosystems modeling
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9597
• 期：1
• 页码：184-200
• 全文大小：1,713 KB
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• 作者单位：Elise Rosati (15)
  Morgan Madec (15)
  Abir Rezgui (15)
  Quentin Colman (15)
  Nicolas Toussaint (15)
  Christophe Lallement (15)
  Pierre Collet (15)
  
  15. ICube Laboratory (Engineering Sciences, Computer Sciences and Imaging Laboratory, UMR 7357), University of Strasbourg/CNRS, 300 boulevard Sébastien Brandt, 67412, Illkirch Cedex 02, France
  
• 丛书名：Applications of Evolutionary Computation
• ISBN：978-3-319-31204-0
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Synthetic biology aims at reinvesting theoretical knowledge from various do-mains (biology, engineering, microelectronics) for the development of new bio-logical functions. Concerning the design of such functions, the classical trial-error approach is expensive and time consuming. Computer-aided design is therefore of key interest in this field. As for other domains, such as microelectronics or robotics, evolutionary algo-rithms can be used to this end. This article is a first step in this direction: it describes the optimization of an existing artificial gene regulatory network using evolutionary algorithms. Evolutionary algorithms successfully find a good set of parameters (the simu-lated response of the system which fits at 99 % the expected response) in about 200 s (corresponding to 5000 generations) on a standard computer. This is the proof of concept of our approach. Moreover, results analysis allows the biologist not only to save time during the design process but also to study the specificity of a system.