2021 Global Biofoundries Alliance Webinar Series
1-2pm Singapore (GMT+8)/ 4-5pm Sydney (AEDT)/
9-10pm PST (previous day)
Chueh Loo Poh
Engineering Biology Lab, SynCTI, NUS
Collectively developing open source software solutions for synthetic biology.
Biofoundries equipped with advance automation and high-throughput tools are being established globally to scale the complexity of engineering biology. As these automated labs handle significant amount of samples, software solutions are vital to support the many specialized tasks along the Design-Build-Test-Learn cycle, e.g., batch DNA design and assembly, modelling for design, sample and data tracking, and data analysis, among others. However, software solutions still have not fully matched the advancement in hardware automation. Although biofoundries faced many common challenges, there is often wheel-reinvention where similar software solutions are being developed in parallel by various labs. Thus, it will be advantageous to collectively develop such commonly used solutions. In this talk, I will present about our recent effort and experience at GBA involving several biofoundries in creating a standardised, open-source Python package, SynBiopython, where software tools will be developed and integrated. We envisage it to serve as a starting point for software development projects within biofoundries around the world.
Esteban Marcellin Saldana
Boosting learning through testing: UQ, Amyris collaboration
The emergence of inexpensive, base-perfect genome editing is revolutionising biology. Modern industrial biotechnology combines automation with analytics and data integration to build high-throughput automated strain designs. Biofoundries replace the slow processes used to build strains using an automated design–build–test cycle. However, testing and hence learning remains relatively shallow. Here, using high throughput proteomics and metabolomics in instrumented reactors, rather than endpoint measurements in plates, we increased the depth of characterization to feed models of cellular physiology and obtain highly predictive mathematical models. The data obtained from the various ‘omics was integrated into a kinetic model to predict bottlenecks in production and design strains using the Amyris Biofoundry. The data rich strain design approach proves that multiomics can enhance strain design and accelerate the learning in biofoundries. The approach also provides a framework to store and integrate multiomics data into models for automated strain engineering.
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