Information content and optimization of self-organized developmental systems

Area of Science:

• Developmental biology
• Systems biology
• Information theory

Background:

• Biological systems self-organize spatial patterns of distinct cell fates for proper function.
• Reproducibility in pattern formation requires controlling intrinsic and extrinsic fluctuations.
• A principled framework is lacking to quantify the performance of stochastic self-organizing systems.

Purpose of the Study:

• Introduce an information-theoretic measure to quantify self-organized fate specification in embryonic development.
• Provide a normative theory for developmental circuits by optimizing this measure.
• Develop a common information-theoretic language for classifying developmental systems.

Main Methods:

• Developed an information-theoretic measure for self-organized fate specification.
• Decomposed information content into positional and correlational contributions.
• Applied the measure to models of lateral inhibition, cell type proportioning, and reaction-diffusion systems.

Main Results:

• The proposed measure quantifies the total information content of cell fate patterns.
• The measure successfully decomposes information into positional and correlational components.
• Optimization of the measure provides insights into the performance of developmental circuits.

Conclusions:

• The information-theoretic framework offers a quantitative approach to understanding stochastic self-organization in development.
• This approach facilitates the classification of diverse developmental systems.
• It provides a unified language to organize signaling processes in developmental biology.