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      Pattern formation mechanisms of self-organizing reaction-diffusion systems

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          Abstract

          Embryonic development is a largely self-organizing process, in which the adult body plan arises from a ball of cells with initially nearly equal potency. The reaction-diffusion theory first proposed by Alan Turing states that the initial symmetry in embryos can be broken by the interplay between two diffusible molecules, whose interactions lead to the formation of patterns. The reaction-diffusion theory provides a valuable framework for self-organized pattern formation, but it has been difficult to relate simple two-component models to real biological systems with multiple interacting molecular species. Recent studies have addressed this shortcoming and extended the reaction-diffusion theory to realistic multi-component networks. These efforts have challenged the generality of previous central tenets derived from the analysis of simplified systems and guide the way to a new understanding of self-organizing processes. Here, we discuss the challenges in modeling multi-component reaction-diffusion systems and how these have recently been addressed. We present a synthesis of new pattern formation mechanisms derived from these analyses, and we highlight the significance of reaction-diffusion principles for developmental and synthetic pattern formation.

          Highlights

          • The reaction-diffusion theory has been proposed to explain self-organized pattern formation during development.

          • Recent mathematical analyses of realistic reaction-diffusion systems challenge central previous tenets in the field.

          • The novel theoretical insights can drive advances in developmental and synthetic biology.

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          Most cited references45

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          Pattern formation outside of equilibrium

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            Reaction-diffusion model as a framework for understanding biological pattern formation.

            The Turing, or reaction-diffusion (RD), model is one of the best-known theoretical models used to explain self-regulated pattern formation in the developing animal embryo. Although its real-world relevance was long debated, a number of compelling examples have gradually alleviated much of the skepticism surrounding the model. The RD model can generate a wide variety of spatial patterns, and mathematical studies have revealed the kinds of interactions required for each, giving this model the potential for application as an experimental working hypothesis in a wide variety of morphological phenomena. In this review, we describe the essence of this theory for experimental biologists unfamiliar with the model, using examples from experimental studies in which the RD model is effectively incorporated.
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              Modeling digits. Digit patterning is controlled by a Bmp-Sox9-Wnt Turing network modulated by morphogen gradients.

              During limb development, digits emerge from the undifferentiated mesenchymal tissue that constitutes the limb bud. It has been proposed that this process is controlled by a self-organizing Turing mechanism, whereby diffusible molecules interact to produce a periodic pattern of digital and interdigital fates. However, the identities of the molecules remain unknown. By combining experiments and modeling, we reveal evidence that a Turing network implemented by Bmp, Sox9, and Wnt drives digit specification. We develop a realistic two-dimensional simulation of digit patterning and show that this network, when modulated by morphogen gradients, recapitulates the expression patterns of Sox9 in the wild type and in perturbation experiments. Our systems biology approach reveals how a combination of growth, morphogen gradients, and a self-organizing Turing network can achieve robust and reproducible pattern formation.
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                Author and article information

                Contributors
                Journal
                Dev Biol
                Dev. Biol
                Developmental Biology
                Elsevier
                0012-1606
                1095-564X
                01 April 2020
                01 April 2020
                : 460
                : 1
                : 2-11
                Affiliations
                [a ]Systems Biology of Development Group, Friedrich Miescher Laboratory of the Max Planck Society, 72076, Tübingen, Germany
                [b ]Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, 02143, USA
                [c ]European Molecular Biology Laboratory, Barcelona Outstation, 08003 Barcelona, Spain
                [d ]Modeling Tumorigenesis Group, Translational Oncology Division, Eberhard Karls University Tübingen, 72076, Tübingen, Germany
                Author notes
                []Corresponding author. Systems Biology of Development Group, Friedrich Miescher Laboratory of the Max Planck Society, 72076, Tübingen, Germany. patrick.mueller@ 123456tuebingen.mpg.de
                Article
                S0012-1606(19)30377-X
                10.1016/j.ydbio.2019.10.031
                7154499
                32008805
                290e7a72-0db5-4205-91bd-0cc04bb98ff3
                © 2019 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 26 June 2019
                : 29 October 2019
                : 29 October 2019
                Categories
                Article

                Developmental biology
                Developmental biology

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