High-throughput mathematical analysis identifies Turing networks for patterning with equally diffusing signals
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The Turing reaction-diffusion model explains how identical cells can self-organize to form spatial patterns. It has been suggested that extracellular signaling molecules with different diffusion coefficients underlie this model, but the contribution of cell-autonomous signaling components is largely unknown. We developed an automated mathematical analysis to derive a catalog of realistic Turing networks. This analysis reveals that in the presence of cell-autonomous factors, networks can form a pattern with equally diffusing signals and even for any combination of diffusion coefficients. We provide a software (available at http://www.RDNets.com) to explore these networks and to constrain topologies with qualitative and quantitative experimental data. We use the software to examine the self-organizing networks that control embryonic axis specification and digit patterning. Finally, we demonstrate how existing synthetic circuits can be extended with additional feedbacks to form Turing reaction-diffusion systems. Our study offers a new theoretical framework to understand multicellular pattern formation and enables the wide-spread use of mathematical biology to engineer synthetic patterning systems.
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MARCON, Luciano, Xavier DIEGO, James SHARPE, Patrick MÃœLLER, 2016. High-throughput mathematical analysis identifies Turing networks for patterning with equally diffusing signals. In: eLife. eLife Sciences Publications. 2016, 5, e14022. eISSN 2050-084X. Available under: doi: 10.7554/eLife.14022BibTex
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year={2016},
doi={10.7554/eLife.14022},
title={High-throughput mathematical analysis identifies Turing networks for patterning with equally diffusing signals},
volume={5},
journal={eLife},
author={Marcon, Luciano and Diego, Xavier and Sharpe, James and Müller, Patrick},
note={Article Number: e14022}
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