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扩散和时变形态发生梯度对双稳态基因表达边界的动态定位和精度的影响。

Implications of diffusion and time-varying morphogen gradients for the dynamic positioning and precision of bistable gene expression boundaries.

机构信息

Developmental Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.

出版信息

PLoS Comput Biol. 2021 Jun 1;17(6):e1008589. doi: 10.1371/journal.pcbi.1008589. eCollection 2021 Jun.

Abstract

The earliest models for how morphogen gradients guide embryonic patterning failed to account for experimental observations of temporal refinement in gene expression domains. Following theoretical and experimental work in this area, dynamic positional information has emerged as a conceptual framework to discuss how cells process spatiotemporal inputs into downstream patterns. Here, we show that diffusion determines the mathematical means by which bistable gene expression boundaries shift over time, and therefore how cells interpret positional information conferred from morphogen concentration. First, we introduce a metric for assessing reproducibility in boundary placement or precision in systems where gene products do not diffuse, but where morphogen concentrations are permitted to change in time. We show that the dynamics of the gradient affect the sensitivity of the final pattern to variation in initial conditions, with slower gradients reducing the sensitivity. Second, we allow gene products to diffuse and consider gene expression boundaries as propagating wavefronts with velocity modulated by local morphogen concentration. We harness this perspective to approximate a PDE model as an ODE that captures the position of the boundary in time, and demonstrate the approach with a preexisting model for Hunchback patterning in fruit fly embryos. We then propose a design that employs antiparallel morphogen gradients to achieve accurate boundary placement that is robust to scaling. Throughout our work we draw attention to tradeoffs among initial conditions, boundary positioning, and the relative timescales of network and gradient evolution. We conclude by suggesting that mathematical theory should serve to clarify not just our quantitative, but also our intuitive understanding of patterning processes.

摘要

早期的形态发生梯度引导胚胎模式形成的模型未能解释基因表达域中时间细化的实验观察结果。在该领域的理论和实验工作之后,动态位置信息已成为讨论细胞如何将时空输入转化为下游模式的概念框架。在这里,我们表明扩散决定了双稳态基因表达边界随时间推移的数学手段,因此细胞如何解释来自形态发生浓度的位置信息。首先,我们引入了一种度量标准,用于评估在基因产物不扩散但形态发生浓度随时间变化的系统中边界位置的可重复性或精度。我们表明梯度的动力学会影响最终模式对初始条件变化的敏感性,较慢的梯度会降低敏感性。其次,我们允许基因产物扩散,并将基因表达边界视为具有局部形态发生浓度调制的传播波前。我们利用这种观点将 PDE 模型近似为一个 ODE,该模型捕获了边界随时间的位置,并使用果蝇胚胎中 Hunchback 模式形成的现有模型来证明该方法。然后,我们提出了一种设计,该设计采用反平行形态发生梯度来实现准确的边界定位,并且对缩放具有鲁棒性。在我们的整个工作中,我们提请注意初始条件、边界定位和网络和梯度演化的相对时间尺度之间的权衡。最后,我们建议数学理论不仅应该澄清我们的定量理解,还应该澄清我们对模式形成过程的直观理解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f47/8195430/78157a2fa37e/pcbi.1008589.g001.jpg

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