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Sequencing enabling design and learning in synthetic biology
Pierre-Aurélien Gilliot1, Thomas E Gorochowski2
1School of Biological Sciences, University of Bristol, Life Sciences Building Tyndall Avenue, Bristol BS8 1TQ, UK.
Current Opinion in Chemical Biology
|August 11, 2020
Summary
Sequencing technologies are enabling synthetic biology by supporting the creation of new biological parts and systems. This approach also provides data for machine learning, advancing predictive bioengineering and real-world applications.
Area of Science:
- Genomics and Molecular Biology
- Synthetic Biology
- Bioengineering
Background:
- Nucleic acid sequencing (DNA and RNA) has transformed biological understanding.
- Synthetic biology leverages biological systems for new functions.
- De novo biological design is an emerging frontier.
Purpose of the Study:
- To demonstrate how sequencing supports synthetic biology.
- To highlight sequencing's role in creating biological parts and systems.
- To explore sequencing's contribution to machine learning in bioengineering.
Main Methods:
- Utilizing sequencing technologies for biological data acquisition.
- Applying machine learning algorithms to design rules.
- Analyzing advancements in sequencing for synthetic biology applications.
Main Results:
- Sequencing facilitates the creation of novel biological parts and systems.
- Vast datasets from sequencing enable machine learning for predictive bioengineering.
- Current applications represent early-stage potential.
Conclusions:
- Sequencing is a foundational tool for de novo biological design.
- Machine learning, powered by sequencing data, is key to advancing predictive bioengineering.
- Future advances will likely expand sequencing's role in real-world synthetic biology.
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