Abstract
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.
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Rosati, E. et al. (2016). Application of Evolutionary Algorithms for the Optimization of Genetic Regulatory Networks. In: Squillero, G., Burelli, P. (eds) Applications of Evolutionary Computation. EvoApplications 2016. Lecture Notes in Computer Science(), vol 9597. Springer, Cham. https://doi.org/10.1007/978-3-319-31204-0_13
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