Cell-free expression (CFE) systems are a staple of biomanufacturing, but their variability–even when identical protocols are followed–can affect qualitative assessments of genetic components. The implications of that variability, however, have been poorly understood.
Now, researchers at the U.S. Army’s Aberdeen Proving Ground in Maryland and the University of Minnesota have identified the point at which variability becomes problematic.
The team assessed DNA titrations for seven genetic circuits of increasing complexity. When three or more proteins were expressed, “normal variability could disrupt the reuse of prototyping results,” they wrote in a recent paper in Synthetic Biology. In systems with one or two proteins, qualitative performance was reasonably consistent after normalizing the circuits across conditions, despite significant quantitative variability.
“Quantitative reproducibility can be alarmingly poor when attempted in another lab,” corresponding author Patricia Buckley, PhD, biochemistry branch chief of DEVCOM CBC, Aberdeen Proving Ground, tells GEN. The logical solution is to use systems that are reliable despite variability. But, she says, “In synthetic biology, such systems often don’t exist.” Therefore, they must be developed.
Variability in complex genetic systems
Automation, a common approach, doesn’t necessarily reduce variability in complex genetic circuits. For example, challenges with the acoustic liquid handling instruments “led to many failed experiments,” the researchers wrote. Specific issues included inconsistencies in droplet dispensation that the instruments did not report.
“The variability of early-stage research when implemented in new labs is rarely measured or reported, if the research can be reproduced at all,” Buckley notes. “We believe it’s important to develop systems that are more robust to variability.” That starts with measuring variability.
The next step is to build biological control systems that reject disturbances. “One example is the final circuit we tested in the paper, which did indeed appear to reject some disturbances caused by the variability observed between the attempts to repeat the experiments,” she says.
That experiment compared variations between open- and closed-loop circuits for the complete integral circuit. In the open-loop circuit, 42% of the variance was explained by condition (primarily random noise) rather than DNA concentration that caused variance in the other experiments. In the closed loop case, 0.5% of the variation was explained by the condition, and 76% of the variation was unexplained. The small percentage of variation caused by condition suggests the closed-loop circuit is functioning as it should.
“Overall, this remains an under-explored area of research,” Buckley says. This early step shows the point beyond which more careful calibration of raw CFE activity is required.