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Updated: Mar 22, 2026

Patterning of Microorganisms and Microparticles through Sequential Capillarity-assisted Assembly

Patterning of Microorganisms and Microparticles through Sequential Capillarity-assisted Assembly

Published on: November 4, 2021

High-throughput mathematical analysis identifies Turing networks for patterning with equally diffusing signals.

Luciano Marcon¹, Xavier Diego^2,3, James Sharpe^2,3,4

¹Friedrich Miescher Laboratory of the Max Planck Society, Tübingen, Germany.

|April 9, 2016

Summary

This summary is machine-generated.

Cell-autonomous factors enable pattern formation in Turing reaction-diffusion systems, even with identical diffusion rates. This research provides a new framework for understanding multicellular self-organization and engineering synthetic patterning systems.

Keywords:

S. cerevisiae Turing patterns computational biology developmental biology differential diffusivity diffusion-driven instability mouse pattern formation self-organization stem cells systems biology zebrafish

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A Versatile Method of Patterning Proteins and Cells

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Published on: February 26, 2017

Area of Science:

Systems Biology
Developmental Biology
Mathematical Biology

Background:

The Turing reaction-diffusion model explains pattern formation from uniform cellular states.
Extracellular signaling molecules with differing diffusion rates are traditionally thought essential.
The role of cell-autonomous signaling in Turing pattern formation remains largely unexplored.

Purpose of the Study:

To investigate the contribution of cell-autonomous signaling components to Turing reaction-diffusion systems.
To develop a mathematical framework for identifying realistic Turing networks.
To enable the engineering of synthetic patterning systems.

Main Methods:

Automated mathematical analysis to derive a catalog of Turing networks.
Development of software (RDNets.com) for exploring and constraining network topologies.
Application of the software to study embryonic axis specification and digit patterning.

Main Results:

Cell-autonomous factors allow Turing pattern formation with equally diffusing signals.
Pattern formation is possible for any combination of diffusion coefficients when cell-autonomous factors are present.
Existing synthetic circuits can be modified to create Turing reaction-diffusion systems.

Conclusions:

Cell-autonomous signaling significantly expands the conditions under which Turing patterns can form.
The developed framework and software facilitate the study and engineering of multicellular self-organization.
This work bridges theoretical understanding with practical applications in synthetic biology.