Machine learning applications in systems metabolic engineering

Kim, Gi Bae; Kim, Won Jun; Kim, Hyun Uk; Lee, Sang Yup

doi:10.1016/j.copbio.2019.08.010

Cited by 129 publications

(66 citation statements)

References 56 publications

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“…In order to predict appropriate and significant target genes for plant metabolic pathway dynamics, the role of machine learning techniques is essentially need. Efficient and optimal system metabolic engineering now a days is driven by high throughput transcriptomics, proteomics and metabolomics data mining and analysis (Kim et al, 2020;Dasgupta et al, 2020). Consequently, modern machine learning algorithms including deep learning approaches can facilitate metabolic engineers to analyse these bio big data, helps in predicting the efficient pathways for metabolic engineering.…”

Section: Role Of Artificial Intelligence In Plant Metabolic Pathway Ementioning

confidence: 99%

An outlook on metabolic pathway engineering in crop plants

Sahoo

Mohapatra²,

Mishra

2020

Arch. Agric. Environ. Sci.

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To produce the essential secondary metabolites, plants are the major and important target source materials for conducting the high-profile metabolic engineering studies. Metabolic pathway engineering of both microorganism targets and plants target contribute towards important drug discovery. In order to efficiently work out in advanced plant metabolic pathway engineering techniques, a detailed knowledge and expertise is essentially needed regarding the plant cell physiology and the mechanics of plant metabolism. Mathematical and statistical models to scale and map the genome for integrative metabolic pathway activity, signal transduction mechanism in the genome, gene regulation and the networks of protein-protein interaction can provide the in-depth knowledge to work efficiently on plant metabolic pathway engineering studies. Incorporation of omics data into these statistical and mathematical models is crucial in the case of drug discovery using the plant system. Recently, artificial intelligence concept and approaches are experimentally applied for efficient and accurate metabolic engineering in plants.

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Section: Role Of Artificial Intelligence In Plant Metabolic Pathway Ementioning

confidence: 99%

An outlook on metabolic pathway engineering in crop plants

Sahoo

Mohapatra²,

Mishra

2020

Arch. Agric. Environ. Sci.

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“…With more and more systemic biological data becoming available, the methods for extracting valuable information and enlightening biological designs from such datasets become the next technological challenge; at the same time, this has also created many opportunities for advances in machine learning. 69,70 For example, machine learning algorithms have been developed to predict promoter strength 71 and natural product structures 72,73 from genome sequences, to predict function from molecular structure information, 74 to aid the directed evolution of proteins, 75,76 to predict base editing outcomes, 77 to accelerate metabolic pathway design and optimization, [78][79][80] and to streamline analytical chemistry data processing during the test step of strain engineering. 81,82 These are just a few examples where machine learning has significantly improved the efficiency of otherwise…”

Section: Data Science and Machine Learningmentioning

confidence: 99%

Future trends in synthetic biology in Asia

et al. 2021

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Synthetic biology research and technology translation has garnered increasing interest from the governments and private investors in Asia, where the technology has great potential in driving a sustainable bio-based economy. This Perspective reviews the latest developments in the key enabling technologies of synthetic biology and its application in bio-manufacturing, medicine, food and agriculture in Asia. Asia-centric strengths in synthetic biology to grow the bio-based economy, such as advances in genome editing and the presence of biofoundries combined with the availability of natural resources and vast markets, are also highlighted. The potential barriers to the sustainable development of the field, including inadequate infrastructure and policies, with suggestions to overcome these by building public-private partnerships, more effective multilateral collaborations and well-developed governance framework, are presented. Finally, the roles of technology, education and regulation in mitigating potential biosecurity risks are examined. Through these discussions, stakeholders from different groups, including academia, industry and government, are expectantly better positioned to contribute towards the establishment of innovation and bioeconomy hubs in Asia.

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“…Cell-free system offers us the flexibility to debottleneck pathway limitations and quickly access antiviral compounds. Computational approaches and machine learning techniques will be integrated to analyze the large volume of multi-omics datasets, predict and design efficient enzymes and pathways, and largely enhance our ability to screen mutant strains and identify the favorable productive phenotypes as well as accelerate the design-build-test-learn cycle of strain engineering [ 64 , 65 , 66 ]. It is anticipated that microbial metabolic engineering will enter a fascinating era and make significant contributions to antiviral drug discovery and development in the near future.…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

A roadmap to engineering antiviral natural products synthesis in microbes

2020

Current Opinion in Biotechnology

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Machine learning applications in systems metabolic engineering

Cited by 129 publications

References 56 publications

An outlook on metabolic pathway engineering in crop plants

An outlook on metabolic pathway engineering in crop plants

Future trends in synthetic biology in Asia

A roadmap to engineering antiviral natural products synthesis in microbes

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