Nonlocal decoding of positional and correlational information during development

TL;DR

This paper uses a Bayesian framework to analyze how nonlocal cell communication and spatial correlations can improve the precision of positional information during tissue development.

Contribution

It introduces a theoretical model showing how morphogen correlations and cellular geometry influence positional inference and proposes algorithms and chemical schemes for optimal decoding.

Findings

Correlational information enhances developmental precision.

Upper bounds on information gain from nonlocal readout.

Algorithms and chemical schemes approximate optimal inference.

Abstract

In many developmental systems, cells differentiate into a tissue by reading out morphogen concentration fields, a process fundamentally limited by noise. How much can the precision of this process be improved by nonlocal information, e.g., via cell-cell communication? Using a Bayes-optimal framework, we show that positional inference depends crucially on morphogen spatial correlations and on the ``structural prior'' that encodes the geometry of the cellular lattice performing the readout. We derive upper bounds on positional information gain due to nonlocal readout and identify signal processing algorithms that approximate optimal positional inference, as well as simple chemical reaction schemes which implement such algorithms. Our theory suggests that correlational information can be exploited to significantly enhance developmental precision.