Stochastic Dynamics and Noise Control in Patterning Systems

描述：组织内信号梯度的形成是动物发育的一个基本方面。形态原指定具有不同身份的细胞，使它们根据信号浓度遵循不同的发育程序。形态生物质面临的主要挑战之一是处理不同水平的随机效应，如细胞外空间信号的时空波动或信号转导中的噪声。模式化系统如何克服随机波动？不同层次的这种随机效应是如何相互作用的？噪音真的能带来好处吗？哪些调控策略造成了基因表达的清晰边界？当边界被锐化时，如何在合理的时间窗口内将其放置在正确的空间位置？通过建模和实验研究的结合，这项工作在斑马鱼后脑的节段(菱形突起)形成的背景下解决了这些根本的重要问题。在发育中的脊椎动物中枢神经系统中，维生素A衍生物维甲酸(RA)形成多个节段，这些节段构成神经发生的最终模式，其信号缺陷导致疾病。斑马鱼RA信号的独特方面使其成为研究发育模式随机效应的有趣的脊椎动物模型系统。这项拟议研究的长期目标是了解组织模式中随机动力学的一般机制。其中一个核心假设是，形态生成系统中的噪声不一定会增加图案化中的不确定性，实际上可以用来改善边界锐化。其他机制，如细胞分选和额外的形态因子，对于在短时间内将边界放置在正确的位置至关重要。体内RA梯度及其下游反应的随机动力学将通过荧光寿命成像显微镜(FLIM)与荧光RA反应转基因相结合来定量。特别是，噪声将被扰动并在体内进行测量，随机相互作用将使用新的多尺度建模框架进行研究。基因表达边界的三个关键属性将被仔细审查：敏锐性、准确性和可变性。这三个性质之间的新关系和它们之间的权衡，以及控制发展模式中随机波动的一般原则将被获得。这些将有助于更好地理解胚胎发育过程中的随机效应和出生缺陷的原因。

DESCRIPTION: The formation of signaling gradients within tissues is a fundamental aspect of animal development. Morphogens specify cells with different identities, causing them to follow distinct developmental programs depending on signal concentration. One of the major challenges for morphogens is to deal with stochastic effects at various levels, such as spatial and temporal fluctuations of the signal in extracellular space or noise in signal transduction. How does a patterning system overcome stochastic fluctuations? How do such stochastic effects at different levels interact? Can noise actually be beneficial? What regulatory strategies create sharp boundaries of gene expression? How can a boundary be placed in the correct spatial location within a reasonable time window while it is sharpening? Through a combination of modeling and experimental studies, this work addresses these fundamentally important questions in the context of formation of segments (rhombomeres) in the zebrafish hindbrain. In the developing vertebrate central nervous system, the vitamin A derivative retinoic acid (RA) patterns multiple segments that underlie the eventual patterns of neurogenesis and defects in its signaling cause disease. The unique aspects of RA signaling in zebrafish make it an intriguing vertebrate model system for studying stochastic effects for developmental patterning. The long-term goal of the proposed research is to understand generic mechanisms of stochastic dynamics in tissue patterning. One of the central hypotheses is that noise in a morphogen system does not necessarily increase uncertainty in patterning and can actually be utilized to improve boundary sharpening. Other mechanisms such as cell sorting and additional morphogens, can be critically important in placing the boundary at the correct location within a short time period. The stochastic dynamics of RA gradient and its downstream responses in vivo will be quantified through Fluorescence Lifetime Imaging Microscopy (FLIM) combined with fluorescent RA responsive transgenics. In particular, noise will be perturbed and measured in vivo, and stochastic interactions will be investigated using novel multiscale modeling frameworks. Three key properties of gene expression boundaries will be scrutinized: sharpness, accuracy, and variability. New relationships among these three properties and the tradeoffs between them, as well as general principles for controlling stochastic fluctuations in developmental patterning will be obtained. These will lead to a better understanding of stochastic effects during embryonic development and the causes of birth defects.