Marcon, Luciano, 1983-Diego, XavierSharpe, JamesMüller, Patrick2017-01-162017-01-162016Marcon L, Diego Iñiguez J, Sharpe J, Müller P. High-throughput mathematical analysis identifies Turing networks for patterning with equally diffusing signals. eLife. 2016;5:e14022. DOI: 10.7554/eLife.140222050-084Xhttp://hdl.handle.net/10230/27905The 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.application/pdfeng© Copyright Marcon et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.info:eu-repo/semantics/articlehttp://dx.doi.org/10.7554/eLife.14022Differential diffusivityDiffusion-driven instabilityMousePattern formationS. cerevisiaeSelf-organizationTuring patternsZebrafishinfo:eu-repo/semantics/openAccess