Reconstructing Dynamic Promoter Activity Profiles from Reporter Gene Data

Accurate
characterization of promoter activity is important when
designing expression systems for systems biology and metabolic engineering
applications. Promoters that respond to changes in the environment
enable the dynamic control of gene expression without the necessity
of inducer compounds, for example. However, the dynamic nature of
these processes poses challenges for estimating promoter activity.
Most experimental approaches utilize reporter gene expression to estimate
promoter activity. Typically the reporter gene encodes a fluorescent
protein that is used to infer a constant promoter activity despite
the fact that the observed output may be dynamic and is a number of
steps away from the transcription process. In fact, some promoters
that are often thought of as constitutive can show changes in activity
when growth conditions change. For these reasons, we have developed
a system of ordinary differential equations for estimating dynamic
promoter activity for promoters that change their activity in response
to the environment that is robust to noise and changes in growth rate.
Our approach, inference of dynamic promoter activity (PromAct), improves
on existing methods by more accurately inferring known promoter activity
profiles. This method is also capable of estimating the correct scale
of promoter activity and can be applied to quantitative data sets
to estimate quantitative rates.