Variability in cell-free expression reactions can impact qualitative genetic circuit characterization.
Katherine A RheaNathan D McDonaldStephanie D ColeVincent NoireauxMatthew W LuxPatricia E BuckleyPublished in: Synthetic biology (Oxford, England) (2022)
Cell-free expression systems provide a suite of tools that are used in applications from sensing to biomanufacturing. One of these applications is genetic circuit prototyping, where the lack of cloning is required and a high degree of control over reaction components and conditions enables rapid testing of design candidates. Many studies have shown utility in the approach for characterizing genetic regulation elements, simple genetic circuit motifs, protein variants or metabolic pathways. However, variability in cell-free expression systems is a known challenge, whether between individuals, laboratories, instruments, or batches of materials. While the issue of variability has begun to be quantified and explored, little effort has been put into understanding the implications of this variability. For genetic circuit prototyping, it is unclear when and how significantly variability in reaction activity will impact qualitative assessments of genetic components, e.g. relative activity between promoters. Here, we explore this question by assessing DNA titrations of seven genetic circuits of increasing complexity using reaction conditions that ostensibly follow the same protocol but vary by person, instrument and material batch. Although the raw activities vary widely between the conditions, by normalizing within each circuit across conditions, reasonably consistent qualitative performance emerges for the simpler circuits. For the most complex case involving expression of three proteins, we observe a departure from this qualitative consistency, offering a provisional cautionary line where normal variability may disrupt reliable reuse of prototyping results. Our results also suggest that a previously described closed loop controller circuit may help to mitigate such variability, encouraging further work to design systems that are robust to variability. Graphical Abstract.