Optimization of designing multiple genes encoding the same protein based on NSGA-II for efficient execution on GPUs
In synthetic biology, it is a challenge to increase the production of target proteins by maximizing their expression levels. In order to augment expression levels, we need to focus on both homologous recombination and codon adaptation, which are estimated by three objective functions, namely HD (Ham...
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| Format: | Article |
| Language: | English |
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AIMS Press
2023-07-01
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| Series: | Electronic Research Archive |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/era.2023270?viewType=HTML |
| _version_ | 1827791766586130432 |
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| author | Donghyeon Kim Jinsung Kim |
| author_facet | Donghyeon Kim Jinsung Kim |
| author_sort | Donghyeon Kim |
| collection | DOAJ |
| description | In synthetic biology, it is a challenge to increase the production of target proteins by maximizing their expression levels. In order to augment expression levels, we need to focus on both homologous recombination and codon adaptation, which are estimated by three objective functions, namely HD (Hamming distance), LRCS (length of repeated or common substring) and CAI (codon adaptation index). Optimizing these objective functions simultaneously becomes a multi-objective optimization problem. The aim is to find satisfying solutions that have high codon adaptation and a low incidence of homologous recombination. However, obtaining satisfactory solutions requires calculating the objective functions multiple times with many cycles and solutions. In this paper, we propose an approach to accelerate the method of designing a set of CDSs (CoDing sequences) based on NSGA-II (non-dominated sorting genetic algorithm II) on NVIDIA GPUs. The implementation accelerated by GPUs improves overall performance by 187.5$ \times $ using $ 100 $ cycles and $ 128 $ solutions. Our implementation allows us to use larger solutions and more cycles, leading to outstanding solution quality. The improved implementation provides much better solutions in a similar amount of time compared to other available methods by 1.22$ \times $ improvements in hypervolume. Furthermore, our approach on GPUs also suggests how to efficiently utilize the latest computational resources in bioinformatics. Finally, we discuss the impacts of the number of cycles and the number of solutions on designing a set of CDSs. |
| first_indexed | 2024-03-11T17:51:33Z |
| format | Article |
| id | doaj.art-0014fb2a7db94b538129ee98350868ff |
| institution | Directory Open Access Journal |
| issn | 2688-1594 |
| language | English |
| last_indexed | 2024-03-11T17:51:33Z |
| publishDate | 2023-07-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Electronic Research Archive |
| spelling | doaj.art-0014fb2a7db94b538129ee98350868ff2023-10-18T01:14:43ZengAIMS PressElectronic Research Archive2688-15942023-07-013195313533910.3934/era.2023270Optimization of designing multiple genes encoding the same protein based on NSGA-II for efficient execution on GPUsDonghyeon Kim0Jinsung Kim1School of Computer Science and Engineering, Chung-Ang University, Seoul, South KoreaSchool of Computer Science and Engineering, Chung-Ang University, Seoul, South KoreaIn synthetic biology, it is a challenge to increase the production of target proteins by maximizing their expression levels. In order to augment expression levels, we need to focus on both homologous recombination and codon adaptation, which are estimated by three objective functions, namely HD (Hamming distance), LRCS (length of repeated or common substring) and CAI (codon adaptation index). Optimizing these objective functions simultaneously becomes a multi-objective optimization problem. The aim is to find satisfying solutions that have high codon adaptation and a low incidence of homologous recombination. However, obtaining satisfactory solutions requires calculating the objective functions multiple times with many cycles and solutions. In this paper, we propose an approach to accelerate the method of designing a set of CDSs (CoDing sequences) based on NSGA-II (non-dominated sorting genetic algorithm II) on NVIDIA GPUs. The implementation accelerated by GPUs improves overall performance by 187.5$ \times $ using $ 100 $ cycles and $ 128 $ solutions. Our implementation allows us to use larger solutions and more cycles, leading to outstanding solution quality. The improved implementation provides much better solutions in a similar amount of time compared to other available methods by 1.22$ \times $ improvements in hypervolume. Furthermore, our approach on GPUs also suggests how to efficiently utilize the latest computational resources in bioinformatics. Finally, we discuss the impacts of the number of cycles and the number of solutions on designing a set of CDSs.https://www.aimspress.com/article/doi/10.3934/era.2023270?viewType=HTMLprotein encodingmulti-objective optimizationbioengineeringgpu computingnsga-ii |
| spellingShingle | Donghyeon Kim Jinsung Kim Optimization of designing multiple genes encoding the same protein based on NSGA-II for efficient execution on GPUs Electronic Research Archive protein encoding multi-objective optimization bioengineering gpu computing nsga-ii |
| title | Optimization of designing multiple genes encoding the same protein based on NSGA-II for efficient execution on GPUs |
| title_full | Optimization of designing multiple genes encoding the same protein based on NSGA-II for efficient execution on GPUs |
| title_fullStr | Optimization of designing multiple genes encoding the same protein based on NSGA-II for efficient execution on GPUs |
| title_full_unstemmed | Optimization of designing multiple genes encoding the same protein based on NSGA-II for efficient execution on GPUs |
| title_short | Optimization of designing multiple genes encoding the same protein based on NSGA-II for efficient execution on GPUs |
| title_sort | optimization of designing multiple genes encoding the same protein based on nsga ii for efficient execution on gpus |
| topic | protein encoding multi-objective optimization bioengineering gpu computing nsga-ii |
| url | https://www.aimspress.com/article/doi/10.3934/era.2023270?viewType=HTML |
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