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In silico design and automated learning to boost next-generation smart biomanufacturing
Pablo Carbonell1,2, Rosalind Le Feuvre1, Eriko Takano1
1Manchester Synthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM) and Future Biomanufacturing Research Hub, Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK.
Biofoundries are accelerating the production of sustainable compounds through automated design, build, test, and learn (DBTL) cycles. Advanced in silico tools and machine learning are key to developing next-generation smart biomanufacturing platforms.
Area of Science:
- Synthetic Biology
- Biotechnology
- Biomanufacturing
Background:
- Growing demand for bio-based compounds from sustainable sources necessitates advanced biomanufacturing platforms.
- Biofoundries worldwide are integrating and automating the design, build, test, and learn (DBTL) cycle to speed up production.
- Recent advancements include developing producer strains for material monomers, achieving industrial titers within 90 days.
Purpose of the Study:
- To discuss new in silico design tools and the role of machine learning in advancing automated biofoundries.
- To explore how these technologies expedite the DBTL cycle and enable rapid biomanufacturing.
- To outline the future of biofoundries operating under fully automated DBTL cycles.
Main Methods:
- Integration and automation of DBTL steps in biofoundry centers.
- Development of novel in silico design tools for strain screening and prototyping.
- Application of machine learning algorithms to high-dimensional data generated by biofoundry operations.
Main Results:
- Significant reduction in delivery time for biomanufacturing prototypes, with some strains nearing industrial titers in under 90 days.
- In silico tools are enhancing the design phase of the DBTL pipeline.
- Automated learning from large datasets accelerates the DBTL cycle.
Conclusions:
- Future biofoundries will feature fully automated DBTL cycles driven by in silico planning and cloud-based design.
- Connectivity of biomanufacturing devices and virtualization platforms are crucial for automation.
- Machine learning and automated worklist generation will enable high adaptability and rapid design changes for smart biomanufacturing.
