Modeling the control of planar cell polarity.
Modeling the control of planar cell polarity.
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DOI：
10.1002/wsbm.138
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发表时间：
2011-09
期刊：
影响因子：
--
通讯作者：
Tomlin CJ
中科院分区：
文献类型：
--
作者：
Axelrod JD;Tomlin CJ
越来越多的医学上重要的发育缺陷和疾病机制可以追溯到平面细胞极性（PCP）途径的破坏。 PCP 系统沿着与其顶底轴正交的轴极化上皮片中的细胞。对果蝇的研究提出了控制 PCP 的模块化系统的概念。 PCP 信号模块的组件以及与其相互作用的效应器系统共同发挥作用，产生紧急模式。实验方法允许在特定的细胞组中操纵单个 PCP 信号分子；这些干预措施不仅在亚细胞水平上扰乱目标细胞的极化，而且还在多细胞水平上扰乱极性模式，通常以特有的方式影响附近的细胞。原则上，这类实验应该允许人们推断模块内部和模块之间的架构，但分子相互作用和组织水平模式之间的关系非常复杂，以至于无法直观理解。数学建模已成为解决这些问题的重要工具。这篇综述探讨了局部信号假说的出现，并描述了局部细胞间信号如何与方向提示相结合，从而产生全局模式。我们将讨论数学建模在指导和解释实验结果方面所发挥的关键作用，并推测 PCP 数学建模的未来作用。不同抽象层次的数学模型已经并将继续以不同的方式为理解 PCP 信号传导的调节做出贡献。
A growing list of medically important developmental defects and disease mechanisms can be traced to disruption of the Planar Cell Polarity (PCP) pathway. The PCP system polarizes cells in epithelial sheets along an axis orthogonal to their apical-basal axis. Studies in the fruitfly, Drosophila, have led to the concept of a modular system controlling PCP. The components of the PCP signaling modules, and the effector systems with which they interact, function together to produce emergent patterns. Experimental methods allow the manipulation of individual PCP signaling molecules in specified groups of cells; these interventions not only perturb the polarization of the targeted cells at a subcellular level, but also perturb patterns of polarity at the multicellular level, often affecting nearby cells in characteristic ways. These kinds of experiments should, in principle, allow one to infer the architecture within and between modules, but the relationships between molecular interactions and tissue-level pattern are sufficiently complex that they defy intuitive understanding. Mathematical modeling has been an important tool to address these problems. This review explores the emergence of a local signaling hypothesis, and describes how a local intercellular signal, coupled with a directional cue, can give rise to global pattern. We will discuss the critical role mathematical modeling has played in guiding and interpreting experimental results, and speculate about future roles for mathematical modeling of PCP. Mathematical models at varying levels of abstraction have and are expected to continue contributing in distinct ways to understanding the regulation of PCP signaling.