Positional information and information flows in dynamic tissues.

Area of Science:

• Developmental Biology
• Systems Biology
• Information Theory

Background:

• Embryonic development involves complex processes of information storage, transmission, and transformation to generate spatial patterns.
• Positional information (PI) is crucial for understanding pattern formation, but its quantification has been limited to static tissues due to cell motion.

Purpose of the Study:

• To develop a novel framework for quantifying PI in dynamic embryonic tissues.
• To decompose mutual information into information flows representing PI preservation, loss, and generation.
• To analyze information-theoretic signatures of developmental processes like cell sorting and mixing.

Main Methods:

• Decomposition of mutual information between cell positions and properties over time into information flows.
• Application of the framework to whole-embryo cell trajectory data from *Drosophila*, mouse, and zebrafish gastrulation.
• Analysis of tissue flows as dynamical systems to understand pattern formation mechanisms.

Main Results:

• The framework reveals information-theoretic signatures of developmental processes directly from data.
• Quantification of cell mixing and derivation of bounds on PI preservation in dynamic tissues.
• Demonstration that morphogenesis structures cell mixing, preferentially preserving specific patterns.

Conclusions:

• The developed framework enables information-theoretic quantification of PI in dynamic tissues, overcoming limitations of previous methods.
• The study provides insights into how tissue dynamics and morphogenesis influence pattern formation.
• The framework offers tools for tracing generated PI to its sources and distinguishing between different pattern-formation mechanisms.