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动态位置信息:模式形成机制与梯度驱动系统中的精度

Dynamic positional information: Patterning mechanism versus precision in gradient-driven systems.

机构信息

Complexity Science Hub (CSH), Vienna, Austria; Department of Molecular Evolution & Development, University of Vienna, Vienna, Austria.

Department of Genetics, University of Cambridge, Cambridge, United Kingdom.

出版信息

Curr Top Dev Biol. 2020;137:219-246. doi: 10.1016/bs.ctdb.2019.11.017. Epub 2019 Dec 24.

Abstract

There is much talk about information in biology. In developmental biology, this takes the form of "positional information," especially in the context of morphogen-based pattern formation. Unfortunately, the concept of "information" is rarely defined in any precise manner. Here, we provide two alternative interpretations of "positional information," and examine the complementary meanings and uses of each concept. Positional information defined as Shannon information helps us understand decoding and error propagation in patterning systems. General relativistic positional information, in contrast, provides a metric to assess the output of pattern-forming mechanisms. Both interpretations provide powerful conceptual tools that do not compete, but are best used in combination to gain a proper mechanistic understanding of robust patterning.

摘要

生物学领域中充斥着大量有关信息的讨论。在发育生物学中,信息以“位置信息”的形式呈现,尤其是在形态发生素为基础的模式形成的背景下。不幸的是,“信息”这一概念很少以任何精确的方式来定义。在这里,我们提供了“位置信息”的两种解释,并研究了每个概念的互补意义和用途。作为香农信息的位置信息有助于我们理解模式形成系统中的解码和误差传播。相比之下,广义相对论的位置信息提供了一种度量方法,以评估形成模式机制的输出。这两种解释都提供了强大的概念工具,它们不会相互竞争,而是最好结合使用,以获得对稳健模式形成的适当机制理解。

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