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A framework and model system to investigate linear system behavior in Escherichia coli.
- Author(s): Hajimorad, Meghdad;
- Gray, Paul R;
- Keasling, Jay D
- et al.
Published Web Locationhttps://doi.org/10.1186/1754-1611-5-3
BackgroundThe ability to compose biological systems from smaller elements that act independently of the other upon assembly may help make the forward engineering of biological systems practical. Engineering biology in this manner is made difficult by the inherent nonlinear response of organisms to genetic devices. Devices are inevitably coupled to one another in the cell because they share the same transcriptional machinery for expression. Thus, new properties can emerge when devices that had been characterized in isolation are expressed concurrently. We show in this report that, similar to physical systems, the Escherichia coli (E. coli) transcriptional system can exhibit linear behavior under "small" perturbation conditions. This, in turn, allows devices to be treated as independent modules.
ResultsWe developed a framework and model system consisting of three devices to investigate linear system behavior in E. coli. Our framework employed the transfer curve concept to determine the amount of nonlinearity elicited by the E. coli transcriptional system in response to the devices. To this effect, the model system was quantitatively characterized using real-time quantitative PCR to produce device transfer curves (DTCs). Two of the devices encoded the bacterial neomycin phosphotransferase II (nptII) and chloramphenicol acetyl transferase (cat), while the third encoded the jellyfish-originating green fluorescent protein (gfp). The gfp device was the most nonlinear in our system, with nptII and cat devices eliciting linear responses. Superposition experiments verified these findings, with independence among the three devices having been lost when gfp was present at copy numbers above the lowest one used.
ConclusionsWe show that linear system behavior is possible in E. coli. Elucidation of the mechanism underlying the nonlinearity observed in gfp may lead to design rules that ensure linear system behavior, enabling the accurate prediction of the quantitative behavior of a system assembled from individually characterized devices. Our work suggests that biological systems follow principles similar to physical ones, and that concepts borrowed from the latter (such as DTCs) may be of use in the characterization and design of biological systems.
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