Kavli Affiliate: Jos W. Zwanikken
| First 5 Authors: Tamara Mijatović, Tamara Mijatović, , ,
| Summary:
Transcription factor concentrations provide signals to cells that allow them
to regulate gene expression to make correct cell fate decisions. Calculations
for noise bounds in gene regulation suggest that clustering or cooperative
binding of transcription factors decreases signal-to-noise ratios at binding
sites. However, clustering of transcription factor molecules around binding
sites is frequently observed. We develop two complementary models for
clustering transcription factors at binding site sensors that allow us to study
information transfer from a signal, the morphogen Bicoid, to a variable
relevant to development, namely future cell fates. We find that weak
cooperativity or clustering can allow for maximal information transfer,
especially about the relevant variable. The timescale of measurement is crucial
for predicting the optimal clustering strength: for short measurements,
clustering allows for the implementation of a switch, while for long
measurements, weak clustering allows the sensor to access maximal developmental
information provided in a nonlinear signal. Finally, we find that clustering
not only facilitates information maximization about the relevant variable, but
also can allow the binding site sensors to achieve optimality in a related
optimization goal, the information bottleneck (IB) bound. While the measurement
time restricts the region on the information plane that is accessible, changes
in clustering in conjunction with changes in the binding energy can shift the
binding site along the optimal bound, and towards an optimal trade-off between
obtaining information about the signal and obtaining relevant information.
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