Allosteric molecules serve as regulators of cellular activity across all domains of life . We present a general theory of allosteric transcriptional regulation that permits quantitative predictions for how physiological responses are tuned to environmental stimuli. To test the model 's predictive power, we apply it to the specific case of the ubiquitous simple repression motif in bacteria . We measure the fold-change in gene expression at different inducer concentrations in a collection of strains that span a  range of repressor copy numbers and operator binding strengths . After inferring the inducer dissociation constants using data from one of these strains, we show the broad reach of the model by predicting the induction profiles of all other strains  . Finally, we derive an expression for the free energy of allosteric transcription factors which enables us to collapse the data from all of our experiments onto a single master curve , capturing the diverse phenomenology of the induction profiles.
Allosteric regulation is found across all domains of life , yet we still lack simple, predictive theories that directly link the experimentally tunable parameters of a system to its input-output response. To that end, we present a general theory of allosteric transcriptional regulation using the Monod-Wyman-Changeux model. We rigorously test this model using the ubiquitous simple repression motif in bacteria by first predicting the behavior of strains that span a large range of repressor copy numbers and DNA binding strengths and then constructing and measuring their response. Our model not only accurately captures the induction profiles of these strains but also enables us to derive analytic expressions for key properties such as the dynamic range and EC_{50 . Finally, we derive an expression for the free energy of allosteric repressors which enables us to collapse our experimental data onto a single master curve that captures the diverse phenomenology of the induction profiles.
R
molecules serve as regulators of cellular activity
regulation is found
R
. We
, yet we still lack simple, predictive theories that directly link the experimentally tunable parameters of a system to its input-output response. To that end, we
R
that permits quantitative predictions for how physiological responses are tuned to environmental stimuli. To test the model 's predictive power, we apply it to the specific case of the
using the Monod-Wyman-Changeux model. We rigorously test this model using the
R
. We measure the fold-change in gene expression at different inducer concentrations in a collection
by first predicting the behavior
A
large
R
operator binding strengths . After inferring the inducer dissociation constants using data from one of these strains, we show the broad reach of the model by predicting
DNA binding strengths and then constructing and measuring their response. Our model not only accurately captures
R
all other strains
these strains but also enables us to derive analytic expressions for key properties such as the dynamic range and
A
EC_{50
R
transcription factors
repressors
R
the data from all of our experiments
our experimental data
R
, capturing
that captures