| Title |
Deep learning for metabolic pathway design |
| ID_Doc |
25307 |
| Authors |
Ryu, G; Kim, GB; Yu, T; Lee, SY |
| Title |
Deep learning for metabolic pathway design |
| Year |
2023 |
| Published |
|
| DOI |
10.1016/j.ymben.2023.09.012 |
| Abstract |
The establishment of a bio-based circular economy is imperative in tackling the climate crisis and advancing sustainable development. In this realm, the creation of microbial cell factories is central to generating a variety of chemicals and materials. The design of metabolic pathways is crucial in shaping these microbial cell factories, especially when it comes to producing chemicals with yet-to-be-discovered biosynthetic routes. To aid in navi-gating the complexities of chemical and metabolic domains, computer-supported tools for metabolic pathway design have emerged. In this paper, we evaluate how digital strategies can be employed for pathway prediction and enzyme discovery. Additionally, we touch upon the recent strides made in using deep learning techniques for metabolic pathway prediction. These computational tools and strategies streamline the design of metabolic pathways, facilitating the development of microbial cell factories. Leveraging the capabilities of deep learning in metabolic pathway design is profoundly promising, potentially hastening the advent of a bio-based circular economy. |
| Author Keywords |
Deep learning; Enzyme discovery; Machine learning; Metabolic pathway design; Systems metabolic engineering |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:001086006300001 |
| WoS Category |
Biotechnology & Applied Microbiology |
| Research Area |
Biotechnology & Applied Microbiology |
| PDF |
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