DBTL-X Workshop
Advancing automation, analytical workflows, and AI integration to keep Rice at the forefront of synthetic biology.
Increased throughput in the design-build-test-learn (DBTL) cycle is essential to enabling Rice to remain at the forefront of synthetic biology. Central to improving throughput is the development of automated methods for building and testing engineered genetic circuits, biomolecules, and cell lines. These methodologies enhance efficiency and minimize human error, allowing for more consistent and reliable outcomes. Additionally, it is critical to integrate these data with biophysical models and AI/ML algorithms to enable researchers to build and learn more effectively and glean insights that can transform future designs.
Featured Funded Teams & Workflows
The Rice Synthetic Biology Institute has funded six teams to advance automation and analytical workflows for:
Pooled optical screening to link genetic circuit design to spatiotemporal cellular behavior.
Microbiome-scale horizontal gene transfer (HGT) measurement to enable engineering without domestication and the development of predictive HGT models.
Transcriptional activity reporting paired with biophysical models to link promoter architecture to bursting dynamics in mammalian cells.
Low-cost, sequence-validated, clonal genes on MoClo-compatible plasmids independent of length.
Isothermal barcoding of plasmids to expand the accessibility of large library screening for applications such as gene circuit characterization, in vitro binding assays, and enzyme variant screening.
Accelerated building and testing of microbial display systems via generalizable methods.
Shared Equipment & Resources: Workflows and analytic methods that leverage shared equipment in the Genetic Design and Engineering Center (GDEC) and the SynBio Core will also be presented during the workshop.
All career levels and students are welcome.
This event is free, but registration is required and space is limited.