Systems biology approaches integrated with artificial intelligence for optimized metabolic engineering
Metabolic engineering aims to maximize the production of bio-economically important substances (compounds, enzymes, or other proteins) through the optimization of the genetics, cellular processes and growth conditions of microorganisms. This requires detailed understanding of underlying metabolic pa...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2020-12-01
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| Series: | Metabolic Engineering Communications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214030120300493 |
| _version_ | 1818340966586646528 |
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| author | Mohamed Helmy Derek Smith Kumar Selvarajoo |
| author_facet | Mohamed Helmy Derek Smith Kumar Selvarajoo |
| author_sort | Mohamed Helmy |
| collection | DOAJ |
| description | Metabolic engineering aims to maximize the production of bio-economically important substances (compounds, enzymes, or other proteins) through the optimization of the genetics, cellular processes and growth conditions of microorganisms. This requires detailed understanding of underlying metabolic pathways involved in the production of the targeted substances, and how the cellular processes or growth conditions are regulated by the engineering. To achieve this goal, a large system of experimental techniques, compound libraries, computational methods and data resources, including multi-omics data, are used. The recent advent of multi-omics systems biology approaches significantly impacted the field by opening new avenues to perform dynamic and large-scale analyses that deepen our knowledge on the manipulations. However, with the enormous transcriptomics, proteomics and metabolomics available, it is a daunting task to integrate the data for a more holistic understanding. Novel data mining and analytics approaches, including Artificial Intelligence (AI), can provide breakthroughs where traditional low-throughput experiment-alone methods cannot easily achieve. Here, we review the latest attempts of combining systems biology and AI in metabolic engineering research, and highlight how this alliance can help overcome the current challenges facing industrial biotechnology, especially for food-related substances and compounds using microorganisms. |
| first_indexed | 2024-12-13T15:51:18Z |
| format | Article |
| id | doaj.art-1d246dca0e564eb49a4e1e162f8245a2 |
| institution | Directory Open Access Journal |
| issn | 2214-0301 |
| language | English |
| last_indexed | 2024-12-13T15:51:18Z |
| publishDate | 2020-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Metabolic Engineering Communications |
| spelling | doaj.art-1d246dca0e564eb49a4e1e162f8245a22022-12-21T23:39:28ZengElsevierMetabolic Engineering Communications2214-03012020-12-0111e00149Systems biology approaches integrated with artificial intelligence for optimized metabolic engineeringMohamed Helmy0Derek Smith1Kumar Selvarajoo2Singapore Institute of Food and Biotechnology Innovation (SIFBI), Agency for Science, Technology and Research (A∗STAR), Singapore, SingaporeSingapore Institute of Food and Biotechnology Innovation (SIFBI), Agency for Science, Technology and Research (A∗STAR), Singapore, SingaporeSingapore Institute of Food and Biotechnology Innovation (SIFBI), Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore; Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore (NUS), Singapore, Singapore; Corresponding author. Singapore Institute of Food and Biotechnology Innovation (SIFBI), Agency for Science, Technology and Research (A∗STAR), 61 Biopolis Drive, #04-14, 138673, SingaporeMetabolic engineering aims to maximize the production of bio-economically important substances (compounds, enzymes, or other proteins) through the optimization of the genetics, cellular processes and growth conditions of microorganisms. This requires detailed understanding of underlying metabolic pathways involved in the production of the targeted substances, and how the cellular processes or growth conditions are regulated by the engineering. To achieve this goal, a large system of experimental techniques, compound libraries, computational methods and data resources, including multi-omics data, are used. The recent advent of multi-omics systems biology approaches significantly impacted the field by opening new avenues to perform dynamic and large-scale analyses that deepen our knowledge on the manipulations. However, with the enormous transcriptomics, proteomics and metabolomics available, it is a daunting task to integrate the data for a more holistic understanding. Novel data mining and analytics approaches, including Artificial Intelligence (AI), can provide breakthroughs where traditional low-throughput experiment-alone methods cannot easily achieve. Here, we review the latest attempts of combining systems biology and AI in metabolic engineering research, and highlight how this alliance can help overcome the current challenges facing industrial biotechnology, especially for food-related substances and compounds using microorganisms.http://www.sciencedirect.com/science/article/pii/S2214030120300493Systems biologyArtificial intelligenceMachine learningMetabolic engineeringFood industry |
| spellingShingle | Mohamed Helmy Derek Smith Kumar Selvarajoo Systems biology approaches integrated with artificial intelligence for optimized metabolic engineering Metabolic Engineering Communications Systems biology Artificial intelligence Machine learning Metabolic engineering Food industry |
| title | Systems biology approaches integrated with artificial intelligence for optimized metabolic engineering |
| title_full | Systems biology approaches integrated with artificial intelligence for optimized metabolic engineering |
| title_fullStr | Systems biology approaches integrated with artificial intelligence for optimized metabolic engineering |
| title_full_unstemmed | Systems biology approaches integrated with artificial intelligence for optimized metabolic engineering |
| title_short | Systems biology approaches integrated with artificial intelligence for optimized metabolic engineering |
| title_sort | systems biology approaches integrated with artificial intelligence for optimized metabolic engineering |
| topic | Systems biology Artificial intelligence Machine learning Metabolic engineering Food industry |
| url | http://www.sciencedirect.com/science/article/pii/S2214030120300493 |
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