The
design and optimization of biological systems is an inherently
complex undertaking that requires careful balancing of myriad synergistic
and antagonistic variables. However, despite this complexity, much
synthetic biology research is predicated on One Factor at A Time (OFAT)
experimentation; the genetic and environmental variables affecting
the activity of a system of interest are sequentially altered while
all other variables are held constant. Beyond being time and resource
intensive, OFAT experimentation crucially ignores the effect of interactions
between factors. Given the ubiquity of interacting genetic and environmental
factors in biology this failure to account for interaction effects
in OFAT experimentation can result in the development of suboptimal
systems. To address these limitations, an increasing number of studies
have turned to Design of Experiments (DoE), a suite of methods that
enable efficient, systematic exploration and exploitation of complex
design spaces. This review provides an overview of DoE for synthetic
biologists. Key concepts and commonly used experimental designs are
introduced, and we discuss the advantages of DoE as compared to OFAT
experimentation. We dissect the applicability of DoE in the context
of synthetic biology and review studies which have successfully employed
these methods, illustrating the potential of statistical experimental
design to guide the design, characterization, and optimization of
biological protocols, pathways, and processes.