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Stochastic Turing patterns in a synthetic bacterial population.

David Karig1,2, K Michael Martini3, Ting Lu4

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Proceedings of the National Academy of Sciences of the United States of America
|June 13, 2018
PubMed
Summary
This summary is machine-generated.

Stochastic Turing theory explains biological pattern formation, even with varying diffusion rates. This study engineered bacteria to demonstrate these patterns, validating the theory for morphogenesis.

Keywords:
Turing patternsbiofilmsignaling moleculesstochastic gene expressionsynthetic biology

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Area of Science:

  • Developmental Biology
  • Systems Biology
  • Chemical Biology

Background:

  • The origin of biological form (morphogenesis) is a fundamental scientific problem.
  • Alan Turing's 1952 model proposed pattern formation via reaction-diffusion systems with specific activator-inhibitor properties, but these conditions are rarely met in nature.
  • The role of Turing instabilities in biological pattern formation has been debated due to these strict requirements.

Purpose of the Study:

  • To investigate the viability of a recently extended stochastic Turing theory for biological pattern formation.
  • To experimentally validate if stochasticity in activator-inhibitor systems can overcome the limitations of classical Turing instabilities.
  • To explore the potential for a unified understanding of biological morphogenesis.

Main Methods:

  • Genetically engineered a synthetic bacterial population to create a stochastic activator-inhibitor system.
  • Utilized a synthetic pattern-forming gene circuit to destabilize a homogenous bacterial lawn.
  • Analyzed spatial correlations of experimentally generated patterns and compared them with theoretical predictions.

Main Results:

  • The engineered system produced disordered patterns on a scale larger than individual cells.
  • The observed patterns exhibited tunable features.
  • Experimental spatial correlations quantitatively matched the predictions of the stochastic Turing theory.
  • The theory successfully predicted pattern formation across a broader range of parameters, including relaxed diffusion coefficient ratios.

Conclusions:

  • Stochasticity in activator-inhibitor systems enables Turing-type pattern formation under more realistic biological conditions.
  • This mechanism provides a potential explanation for a wide array of biological patterns.
  • The findings support a unified view of morphogenesis driven by stochastic gene expression and dynamical instabilities.