  Given the stochastic nature of gene expression, genetically identical cells
exposed to the same environmental inputs will produce different outputs. This
heterogeneity has been hypothesized to have consequences for how cells are able
to survive in changing environments. Recent work has explored the use of
information theory as a framework to understand the accuracy with which cells
can ascertain the state of their surroundings. Yet the predictive power of
these approaches is limited and has not been rigorously tested using precision
measurements. To that end, we generate a minimal model for a simple genetic
circuit in which all parameter values for the model come from independently
published data sets. We then predict the information processing capacity of the
genetic circuit for a suite of biophysical parameters such as protein copy
number and protein-DNA affinity. We compare these parameter-free predictions
with an experimental determination of protein expression distributions and the
resulting information processing capacity of E. coli cells. We find that our
minimal model captures the scaling of the cell-to-cell variability in the data
and the inferred information processing capacity of our simple genetic circuit
up to a systematic deviation.
