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

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 merlot redbud tree for sale 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 read more understand multicellular pattern formation and enables the wide-spread use of mathematical biology to engineer synthetic patterning systems.