The parallel genetic algorithm for designing DNA randomizations in a combinatorial protein experiment

Authors:
Jacek Błażewicz;Beniamin Dziurdza;Wojciech T. Markiewicz;Ceyda Oğuz
Affiliations:
Institute of Computing Science, Poznań University of Technology, Poznań, Poland;Institute of Computing Science, Poznań University of Technology, Poznań, Poland;Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznań, Poland;Department of Industrial Engineering, Koç University, Sariyer, Istanbul, Turkey
Venue:
PPAM'05 Proceedings of the 6th international conference on Parallel Processing and Applied Mathematics
Year:
2005

Citing 1
Cited 1

Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning

Biologically-implemented genetic algorithm for protein engineering

Proceedings of the 11th Annual conference on Genetic and evolutionary computation

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Abstract

Evolutionary methods of protein engineering such as phage display have revolutionized drug design and the means of studying molecular binding. In order to obtain the highest experimental efficiency, the distributions of constructed combinatorial libraries should be carefully adjusted. The presented approach takes into account diversity–completeness trade–off and tries to maximize the number of new amino acid sequences generated in each cycle of the experiment. In the paper, the mathematical model is introduced and the parallel genetic algorithm for the defined optimization problem is described. Its implementation on the SunFire 6800 computer proves a high efficiency of the proposed approach.