Mapping DNA sequence to transcription factor binding energy in vivo
Fig 4
Energy matrix predictions can be used to design phenotypic responses.
Phenotypic parameters exhibit trade-offs as ΔεR is varied. A: The values of the leakiness, saturation, and dynamic range are plotted as a function of transcription factor binding energy, ΔεR, for a strain with repressor copy number R = 130. Different values of ΔεR exhibit combinations of different phenotypic properties. Several operators were chosen whose predicted binding energies (squares) result in a range of phenotypes. B: The value of the [EC50] is plotted as a function of ΔεR for a strain with R = 130. The [EC50] decreases as the value of ΔεR increases. C-H: Operators with different values of ΔεR were chosen to have varying induction responses based on the phenotypic trade-offs shown in (A) and (B). The fold-change is shown for each operator as IPTG concentrations are varied. The fold-change data are overlaid with the predicted induction curve (solid) and an induction curve plotted using the measured binding energy for the operator (dashed). Shown are the predicted binding energy (where the error represents the standard deviation of predictions) and the fitted binding energy (where the superscripts and subscripts represent the 95% confidence intervals of the fits).