Optimizing information transmission in optogenetic Wnt signaling

TL;DR

This study explores how to optimize information transfer in optogenetically controlled Wnt signaling pathways, revealing that discrete encoding strategies can maximize information capacity and improve cellular decision-making.

Contribution

It introduces an optimal encoding framework for Wnt signaling, demonstrating that discrete signals enhance information transmission beyond binary switches.

Findings

Discrete encoding surpasses 1 bit of information capacity.

Optimal encoding varies with pathway noise levels.

Continuous codes emerge in low-noise conditions.

Abstract

Populations of cells regulate gene expression in response to external signals, but their ability to make reliable collective decisions is limited by both intrinsic noise in molecular signaling and variability between individual cells. In this work, we use optogenetic control of the canonical Wnt pathway as an example to study how reliably information about an external signal is transmitted to a population of cells, and determine an optimal encoding strategy to maximize information transmission from Wnt signals to gene expression. We find that it is possible to reach an information capacity beyond 1 bit only through an appropriate, discrete encoding of signals: using either no Wnt, a short Wnt pulse, or a sustained Wnt signal. By averaging over an increasing number of outputs, we systematically vary the effective noise in the pathway. As the effective noise decreases, the optimal encoding comprises more discrete input signals. These signals do not need to be fine-tuned to achieve near-optimal information transmission. The optimal code transitions into a continuous code in the small-noise limit, which can be shown to be consistent with the Jeffreys prior. We visualize the performance of different signal encodings using decoding maps. Our results suggest optogenetic Wnt signaling allows for regulatory control beyond a simple binary switch, and provides a framework to apply ideas from information processing to single-cell in vitro experiments.