%0 Journal Article
%T A framework and model system to investigate linear system behavior in Escherichia coli
%A Meghdad Hajimorad
%A Paul R Gray
%A Jay D Keasling
%J Journal of Biological Engineering
%D 2011
%I BioMed Central
%R 10.1186/1754-1611-5-3
%X We 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.We 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.Engineering biological systems with predictable, quantitative behavior is currently a challenging problem. Presently, this requires months (at times years) of trial-and-error type of experiments, with the engineering of functional systems being more akin to art than engineering [1]. Synthetic biology aims to develop foundational principles and technologies that will enable the systematic forward engineering of biological systems [2-4]. In particular, synthetic biology aims to develop frameworks that apply the engineering principles of abstracti
%U http://www.jbioleng.org/content/5/1/3