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Finite cell-size effects on protein variability in Turing patterned tissues.

Javier Buceta^1,2

¹Department of Bioengineering, Lehigh University, Iacocca Hall, 111 Research Drive, Bethlehem, PA 18015, USA jbuceta@gmail.com.

Journal of the Royal Society, Interface

|September 1, 2017

Summary

This study introduces a framework to measure noise in protein expression within Turing patterned tissues. It distinguishes granular noise from biochemical noise, aiding in understanding developmental pattern formation.

Keywords:

Turing development noise patterning tissue

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Area of Science:

Developmental Biology
Systems Biology
Biophysics

Background:

Turing patterns are crucial for generating biological structures.
Protein expression variability can arise from various sources, impacting pattern fidelity.
Quantifying noise sources is essential for understanding developmental robustness.

Purpose of the Study:

To develop a framework for characterizing protein expression variability in Turing patterned tissues.
To introduce and quantify 'granular noise' arising from finite cell size.
To differentiate between granular noise and biochemical noise.

Main Methods:

Development of a theoretical framework to analyze noise sources.
Introduction of the nearest-neighbors autocorrelation function to measure granular noise.
In silico simulations of growing tissues with activator-inhibitor dynamics.

Main Results:

The relative importance of noise sources depends on cell size, domain size, pattern amplitude, and biochemical fluctuation intensity.
The framework successfully distinguishes between granular and biochemical noise.
Identification of protein species that best buffer stochasticity.

Conclusions:

The developed framework enables precise measurement and differentiation of noise contributions in tissue patterning.
This work provides tools to identify key instructive signals in systems exhibiting Turing instability.
The findings are relevant for understanding periodic pattern formation during development.