Plasmid-borne prokaryotic gene expression: Sources of variability and quantitative system characterization

Abstract

One aim of synthetic biology is to exert systematic control over cellular behavior, either for medical purposes or to "program" microorganisms. An engineering approach to the design of biological controllers demands a quantitative understanding of the dynamics of both the system to be controlled and the controllers themselves. Here we focus on a widely used method of exerting control in bacterial cells: plasmid vectors bearing gene-promoter pairs. We study two variants of the simplest such element, an unregulated promoter constitutively expressing its gene, against the varying genomic background of four Escherichia coli cell strains. Absolute protein numbers and rates of expression vary with both cell strain and plasmid type, as does the variability of expression across the population. Total variability is most strongly coupled to the cell division process, and after cell size is scaled away, plasmid copy number regulation emerges as a significant effect. We present simple models that capture the main features of the system behavior. Our results confirm that complex interactions between plasmids and their hosts can have significant effects on both expression and variability, even in deliberately simplified systems.

Fine-tuning gene networks using simple sequence repeats

“…The context in which a gene network such as the bistable switch is expressed can dramatically affect the performance of the network (4,36), but its effect is, in general, poorly understood and not easily incorporated into modeling gene network performance. To illustrate this point, we transformed each variant of the rbSSR-BSS library into a second lacI − strain of E. coli-BW25113 ΔlacI-and performed the same initial characterization assay described above.…”

An active intracellular device to prevent lethal disease outcomes in virus‐infected bacterial cells

“…5). The mismatch between the experiments and model for both cell fate decisions and EGFP numbers suggest an overestimation in the model of the levels of CI plus CIts dimers present, possibly indicating a lack of accuracy in our literature-derived parameter values, or a lack of information on the effects of the cellular context (Bagh et al, 2008;Kaern et al, 2003;Shearwin, 2009;St-Pierre and Endy, 2008;Tan et al, 2009), cellular noise (Dunlop et al, 2008;Kaern et al, 2005;Munsky et al, 2009), or temperature (Lakha et al, 2009;Lo et al, 2006). As parts in synthetic biology become standardized and better characterized (Canton et al, 2008;Kelly et al, 2009), with a better understanding of these effects, models may improve further in their predictive abilities.…”

“…The average EGFP number per cell was quantified using spectrofluorimetry, by comparing the fluorescence output of standard solutions of purified EGFP (BioVision). The details of this method can be found elsewhere (Bagh et al, 2008).…”

“…The rate constant of EGFP production was calculated from quantified EGFP numbers using a method described elsewhere (Bagh et al, 2008). In the RC1H and RC1L controllers, we used the same sequence as the P RM -CI sequence in the wild type; therefore, published literature values for wild-type CI were used to describe the rate of CIts expression.…”

SynBioSS-Aided Design of Synthetic Biological Constructs

“…The second assumption is arguably of limited use in synthetic biology, where the goal is to engineer time-dependent responses of outputs, such as the flipping of a bistable switch, to well-defined time-profiles of inputs. The third assumption has been unequivocally proven wrong for biomolecular systems, with the study of cellular populations (Bagh et al , 2008; Blake et al , 2003; Rosenfeld et al , 2005). …”