Cross-talk and interference enhance information capacity of a signaling pathway

TL;DR

This paper shows that transcription factors interacting at overlapping DNA sites can increase information flow in gene regulatory networks by correlating their noise, especially when signals are interdependent, challenging traditional views of biological noise.

Contribution

It introduces a novel information-theoretic framework demonstrating how TF cross-talk and correlated noise enhance signaling capacity in noisy gene networks.

Findings

Correlated noise between TFs increases information transmission.

Interdependent input signals improve signaling efficiency.

Cross-regulation can be inferred from binding-site analysis.

Abstract

A recurring motif in gene regulatory networks is transcription factors (TFs) that regulate each other, and then bind to overlapping sites on DNA, where they interact and synergistically control transcription of a target gene. Here, we suggest that this motif maximizes information flow in a noisy network. Gene expression is an inherently noisy process due to thermal fluctuations and the small number of molecules involved. A consequence of multiple TFs interacting at overlapping binding-sites is that their binding noise becomes correlated. Using concepts from information theory, we show that in general a signaling pathway transmits more information if 1) noise of one input is correlated with that of the other, 2) input signals are not chosen independently. In the case of TFs, the latter criterion hints at up-stream cross-regulation. We demonstrate these ideas for competing TFs and feed-forward gene regulatory modules, and discuss generalizations to other signaling pathways. Our results challenge the conventional approach of treating biological noise as uncorrelated fluctuations, and present a systematic method for understanding TF cross-regulation networks either from direct measurements of binding noise, or bioinformatic analysis of overlapping binding-sites.