Synthetic
Biology has overcome many of the early challenges facing
the field and is entering a systems era characterized by adoption
of Design-Build-Test-Learn (DBTL) approaches. The need for automation
and standardization to enable reproducible, scalable, and translatable
research has become increasingly accepted in recent years, and many
of the hardware and software tools needed to address these challenges
are now in place or under development. However, the lack of connectivity
between DBTL modules and barriers to access and adoption remain significant
challenges to realizing the full potential of lab automation. In this
review, we characterize and classify the state of automation in synthetic
biology with a focus on the physical automation of experimental workflows.
Though fully autonomous scientific discovery is likely a long way
off, impressive progress has been made toward automating critical
elements of experimentation by combining intelligent hardware and
software tools. It is worth questioning whether total automation that
removes humans entirely from the loop should be the ultimate goal,
and considerations for appropriate automation versus total automation
are discussed in this light while emphasizing areas where further
development is needed in both contexts.