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A Numerical Evo-Devo Synthesis for the Identification of Pattern-Forming Factors

机译:一种数值EVO-DEVO合成用于识别图案形成因子

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摘要

Animals display extensive diversity in motifs adorning their coat, yet these patterns have reproducible orientation and periodicity within species or groups. Morphological variation has been traditionally used to dissect the genetic basis of evolutionary change, while pattern conservation and stability in both mathematical and organismal models has served to identify core developmental events. Two patterning theories, namely instruction and self-organisation, emerged from this work. Combined, they provide an appealing explanation for how natural patterns form and evolve, but in vivo factors underlying these mechanisms remain elusive. By bridging developmental biology and mathematics, novel frameworks recently allowed breakthroughs in our understanding of pattern establishment, unveiling how patterning strategies combine in space and time, or the importance of tissue morphogenesis in generating positional information. Adding results from surveys of natural variation to these empirical-modelling dialogues improves model inference, analysis, and in vivo testing. In this evo-devo-numerical synthesis, mathematical models have to reproduce not only given stable patterns but also the dynamics of their emergence, and the extent of inter-species variation in these dynamics through minimal parameter change. This integrative approach can help in disentangling molecular, cellular and mechanical interaction during pattern establishment.
机译:动物在装饰涂层的图案中显示出广泛的多样性,但这些模式在物种或群体中具有可重复的取向和周期性。形态学变异传统上用于解剖进化变化的遗传基础,而数学和有机体模型的模式守恒和稳定性旨在识别核心发展事件。这项工作中出现了两种图案化理论,即指导和自我组织。结合,他们提供了一种吸引人的解释,了解自然模式如何形成和发展,但这些机制的体内因素仍然难以捉摸。通过桥接发展生物学和数学,新颖的框架最近允许突破我们对模式建立的理解,揭示图案策略在空间和时间内结合的方式,或组织形态发生在产生位置信息中的重要性。从对这些实证建模对话的自然变化的调查添加结果改善了模型推断,分析和体内测试。在这种EVO-DEVO - 数值合成中,数学模型不仅给予稳定的模式而且还通过最小参数变化来再现它们的出现的动态,以及它们的种类间变化的程度。这种综合方法可以帮助在模式建立期间解开分子,细胞和机械相互作用。

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