Design: software-enabled approaches and includes design at all levels, including sequences, engineering approaches, workflows, componentry, organisms etc. Modelling at all levels informs design approaches.
Build: includes combinatorial assembly of DNA-encoded componentry, typically based on standardised syntax and using high-throughput automated robots, as well as genetic modification of organisms (unless cell-free systems are in use), and picking/storage of generated strains. Quality control (sequencing etc.) is often included (or may be incorporated in the Test component of the cycle)
Test: Testing of engineered constructs and strains. Typically high throughput methods as first pass testing (e.g. colony picking robots, FACS, microtitre plates, and high-throughput analytical chemistry to select for markers/reporters/phenotypes). Once initial selection is performed, deeper testing (potentially including bioreactor/scale-up assessments) of a smaller number of strains is then performed. The Test phase generates significant amounts of data.
Learn: Data and experience from the DBT phases are collated and examined for learnings. Mechanistic modeling as well as artificial intelligence approaches such as machine learning may be used to extract value from data sets. Learnings are use to generate novel testable hypotheses and incorporated into the next DBT cycle.
Scale-Up: Scale-up involves moving from lab scale experiments towards commercial manufacturing. This process has its own D-B-T-L cycles and typically involves up-scaling fermentation capacity, including bioprocess engineering and further bioengineering to suit the bioprocess.