Published today in Evolutionary Bioinformatics is a new methodology article by Yen-Chang Chang, Chun-Liang Lin and Tanagorn Jennawasin.  Read more about this paper below:

Title

Design of Synthetic Genetic Oscillators Using Evolutionary Optimization

Abstract

Efforts have been made to establish computer models of genetic oscillation. We have developed a real structured genetic algorithm (RSGA) which combines advantages of the traditional real genetic algorithm (RGA) with those of the structured genetic algorithm (SGA) and applies it as an optimization strategy for genetic oscillator design. For the generalized design, our proposed approach fulfils all types of genes by minimizing the order of oscillator while searching for the optimal network parameters. The design approach is shown to be capable of yielding genetic oscillators with a simpler structure while possessing satisfactory oscillating behavior. In silico experiments show effectiveness of the proposed algorithm to genetic oscillator design. In particular, it is shown that the proposed approach performs better than the traditional GAs in the sense that a cheaper structure of genetic oscillators can be obtained.

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