Towards a hybrid model-driven platform based on flux balance analysis and a machine learning pipeline for biosystem design
Metabolic modeling and machine learning (ML) are crucial components of the evolving next-generation tools in systems and synthetic biology, aiming to unravel the intricate relationship between genotype, phenotype, and the environment. Nonetheless, the comprehensive exploration of integrating these t...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-03-01
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| Series: | Synthetic and Systems Biotechnology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405805X23001114 |
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| author | Debiao Wu Feng Xu Yaying Xu Mingzhi Huang Zhimin Li Ju Chu |
| author_facet | Debiao Wu Feng Xu Yaying Xu Mingzhi Huang Zhimin Li Ju Chu |
| author_sort | Debiao Wu |
| collection | DOAJ |
| description | Metabolic modeling and machine learning (ML) are crucial components of the evolving next-generation tools in systems and synthetic biology, aiming to unravel the intricate relationship between genotype, phenotype, and the environment. Nonetheless, the comprehensive exploration of integrating these two frameworks, and fully harnessing the potential of fluxomic data, remains an unexplored territory. In this study, we present, rigorously evaluate, and compare ML-based techniques for data integration. The hybrid model revealed that the overexpression of six target genes and the knockout of seven target genes contribute to enhanced ethanol production. Specifically, we investigated the influence of succinate dehydrogenase (SDH) on ethanol biosynthesis in Saccharomyces cerevisiae through shake flask experiments. The findings indicate a noticeable increase in ethanol yield, ranging from 6 % to 10 %, in SDH subunit gene knockout strains compared to the wild-type strain. Moreover, in pursuit of a high-yielding strain for ethanol production, dual-gene deletion experiments were conducted targeting glycerol-3-phosphate dehydrogenase (GPD) and SDH. The results unequivocally demonstrate significant enhancements in ethanol production for the engineered strains Δsdh4Δgpd1, Δsdh5Δgpd1, Δsdh6Δgpd1, Δsdh4Δgpd2, Δsdh5Δgpd2, and Δsdh6Δgpd2, with improvements of 21.6 %, 27.9 %, and 22.7 %, respectively. Overall, the results highlighted that integrating mechanistic flux features substantially improves the prediction of gene knockout strains not accounted for in metabolic reconstructions. In addition, the finding in this study delivers valuable tools for comprehending and manipulating intricate phenotypes, thereby enhancing prediction accuracy and facilitating deeper insights into mechanistic aspects within the field of synthetic biology. |
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| id | doaj.art-336fa4e2a8254caf956c0bfea2910a81 |
| institution | Directory Open Access Journal |
| issn | 2405-805X |
| language | English |
| last_indexed | 2025-03-22T03:57:25Z |
| publishDate | 2024-03-01 |
| publisher | KeAi Communications Co., Ltd. |
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| series | Synthetic and Systems Biotechnology |
| spelling | doaj.art-336fa4e2a8254caf956c0bfea2910a812024-04-28T10:56:51ZengKeAi Communications Co., Ltd.Synthetic and Systems Biotechnology2405-805X2024-03-01913342Towards a hybrid model-driven platform based on flux balance analysis and a machine learning pipeline for biosystem designDebiao Wu0Feng Xu1Yaying Xu2Mingzhi Huang3Zhimin Li4Ju Chu5State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, People's Republic of ChinaState Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, People's Republic of ChinaState Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, People's Republic of ChinaCorresponding author.; State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, People's Republic of ChinaState Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, People's Republic of ChinaState Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, People's Republic of ChinaMetabolic modeling and machine learning (ML) are crucial components of the evolving next-generation tools in systems and synthetic biology, aiming to unravel the intricate relationship between genotype, phenotype, and the environment. Nonetheless, the comprehensive exploration of integrating these two frameworks, and fully harnessing the potential of fluxomic data, remains an unexplored territory. In this study, we present, rigorously evaluate, and compare ML-based techniques for data integration. The hybrid model revealed that the overexpression of six target genes and the knockout of seven target genes contribute to enhanced ethanol production. Specifically, we investigated the influence of succinate dehydrogenase (SDH) on ethanol biosynthesis in Saccharomyces cerevisiae through shake flask experiments. The findings indicate a noticeable increase in ethanol yield, ranging from 6 % to 10 %, in SDH subunit gene knockout strains compared to the wild-type strain. Moreover, in pursuit of a high-yielding strain for ethanol production, dual-gene deletion experiments were conducted targeting glycerol-3-phosphate dehydrogenase (GPD) and SDH. The results unequivocally demonstrate significant enhancements in ethanol production for the engineered strains Δsdh4Δgpd1, Δsdh5Δgpd1, Δsdh6Δgpd1, Δsdh4Δgpd2, Δsdh5Δgpd2, and Δsdh6Δgpd2, with improvements of 21.6 %, 27.9 %, and 22.7 %, respectively. Overall, the results highlighted that integrating mechanistic flux features substantially improves the prediction of gene knockout strains not accounted for in metabolic reconstructions. In addition, the finding in this study delivers valuable tools for comprehending and manipulating intricate phenotypes, thereby enhancing prediction accuracy and facilitating deeper insights into mechanistic aspects within the field of synthetic biology.http://www.sciencedirect.com/science/article/pii/S2405805X23001114Metabolic modelingMachine learningFlux balance analysisBiosystems designSaccharomyces cerevisiaeSuccinate dehydrogenase |
| spellingShingle | Debiao Wu Feng Xu Yaying Xu Mingzhi Huang Zhimin Li Ju Chu Towards a hybrid model-driven platform based on flux balance analysis and a machine learning pipeline for biosystem design Synthetic and Systems Biotechnology Metabolic modeling Machine learning Flux balance analysis Biosystems design Saccharomyces cerevisiae Succinate dehydrogenase |
| title | Towards a hybrid model-driven platform based on flux balance analysis and a machine learning pipeline for biosystem design |
| title_full | Towards a hybrid model-driven platform based on flux balance analysis and a machine learning pipeline for biosystem design |
| title_fullStr | Towards a hybrid model-driven platform based on flux balance analysis and a machine learning pipeline for biosystem design |
| title_full_unstemmed | Towards a hybrid model-driven platform based on flux balance analysis and a machine learning pipeline for biosystem design |
| title_short | Towards a hybrid model-driven platform based on flux balance analysis and a machine learning pipeline for biosystem design |
| title_sort | towards a hybrid model driven platform based on flux balance analysis and a machine learning pipeline for biosystem design |
| topic | Metabolic modeling Machine learning Flux balance analysis Biosystems design Saccharomyces cerevisiae Succinate dehydrogenase |
| url | http://www.sciencedirect.com/science/article/pii/S2405805X23001114 |
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