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Examination of a genetic algorithm for the application in high‐throughput downstream process development
Author(s) -
Treier Katrin,
Berg Annette,
Diederich Patrick,
Lang Katharina,
Osberghaus Anna,
Dismer Florian,
Hubbuch Jürgen
Publication year - 2012
Publication title -
biotechnology journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.144
H-Index - 84
eISSN - 1860-7314
pISSN - 1860-6768
DOI - 10.1002/biot.201200145
Subject(s) - throughput , computer science , premature convergence , noise (video) , selection (genetic algorithm) , mathematical optimization , genetic algorithm , downstream (manufacturing) , process (computing) , local optimum , convergence (economics) , global optimization , algorithm , meta optimization , population , optimization problem , biological system , mathematics , machine learning , artificial intelligence , engineering , telecommunications , operations management , image (mathematics) , demography , sociology , economics , wireless , economic growth , operating system , biology
Compared to traditional strategies, application of high‐throughput experiments combined with optimization methods can potentially speed up downstream process development and increase our understanding of processes. In contrast to the method of Design of Experiments in combination with response surface analysis (RSA), optimization approaches like genetic algorithms (GAs) can be applied to identify optimal parameter settings in multidimensional optimizations tasks. In this article the performance of a GA was investigated applying parameters applicable in high‐throughput downstream process development. The influence of population size, the design of the initial generation and selection pressure on the optimization results was studied. To mimic typical experimental data, four mathematical functions were used for an in silico evaluation. The influence of GA parameters was minor on landscapes with only one optimum. On landscapes with several optima, parameters had a significant impact on GA performance and success in finding the global optimum. Premature convergence increased as the number of parameters and noise increased. RSA was shown to be comparable or superior for simple systems and low to moderate noise. For complex systems or high noise levels, RSA failed, while GA optimization represented a robust tool for process optimization. Finally, the effect of different objective functions is shown exemplarily for a refolding optimization of lysozyme.