AI Article Synopsis
- A new design approach using Genetic Algorithms (GA) is introduced for modeling genes with multiple autonomous regulatory modules.
- This method features specialized crossover operators that maintain successful genetic combinations by utilizing local homology between parental gene strings.
- The approach is evaluated through real-life systems biology problems, showcasing its effectiveness over traditional GA methods in specific DNA evolution cases.
Article Abstract
A new approach to design a dynamic model of genes with multiple autonomous regulatory modules by evolutionary computations is proposed. The approach is based on Genetic Algorithms (GA), with new crossover operators especially designed for these purposes. The new operators use local homology between parental strings to preserve building blocks found by the algorithm. The approach exploits the subbasin-portal architecture of the fitness functions suitable for this kind of evolutionary modeling. This architecture is significant for Royal Road class fitness functions. Two real-life Systems Biology problems with such fitness functions are implemented here: evolution of the bacterial promoter rrnPl and of the enhancer of the Drosophila even-skipped gene. The effectiveness of the approach compared to standard GA is demonstrated on several benchmark and real-life tasks.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2949972 | PMC |
| http://dx.doi.org/10.1016/j.procs.2010.04.111 | DOI Listing |
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