Optimization of designing multiple genes encoding the same protein based on NSGA-II for efficient execution on GPUs

Donghyeon Kim; Jinsung Kim; Donghyeon Kim; Jinsung Kim

doi:10.3934/era.2023270

Electronic Research Archive

2023, Volume 31, Issue 9: 5313-5339. doi: 10.3934/era.2023270

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Optimization of designing multiple genes encoding the same protein based on NSGA-II for efficient execution on GPUs

Donghyeon Kim ,
Jinsung Kim ^,

School of Computer Science and Engineering, Chung-Ang University, Seoul, South Korea

Academic Editor: Érika Diz-Pita

Received: 31 May 2023 Revised: 15 July 2023 Accepted: 18 July 2023 Published: 26 July 2023

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.
- protein encoding,
- multi-objective optimization,
- bioengineering,
- GPU computing,
- NSGA-II
Citation: Donghyeon Kim, Jinsung Kim. Optimization of designing multiple genes encoding the same protein based on NSGA-II for efficient execution on GPUs[J]. Electronic Research Archive, 2023, 31(9): 5313-5339. doi: 10.3934/era.2023270

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Abstract

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.

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