Experimentally monitoring the kinematics of branching network growth is a tricky task, given the complexity of the structures generated in three dimensions. One option is to drive the network in such a way as to obtain two-dimensional growth, enabling a collection of independent images to be obtained. The density of the network generates ambiguous structures, such as overlaps and meetings, which hinder the reconstruction of the chronology of connections. In this paper, we propose a general method for global network reconstruction. Each network connection is defined by a unique label, enabling it to be tracked in time and space. In this work, we distinguish between lateral and apical branches on the one hand, and extremities on the other. Finally, we reconstruct the network after identifying and eliminating overlaps. This method is then applied to the model filamentous fungus Podospora anserina to reconstruct its growing thallus. We derive criteria for differentiating between apical and lateral branches. We find that the outer ring is favorably composed of apical branches, while densification within the network comes from lateral branches. From this, we derive the specific dynamics of each of the two types. Finally, in the absence of any latency phase during growth initiation, we can reconstruct a time based on the equality of apical and lateral branching collections. This makes it possible to directly compare the growth dynamics of different thalli.

中文翻译：

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全面识别增长和分支网络的时空结构


鉴于三维结构的复杂性，实验监测分支网络增长的运动学是一项棘手的任务。一种选择是以获得二维增长的方式驱动网络，从而能够获得独立图像的集合。网络的密度会产生模糊的结构，例如重叠和会议，这阻碍了连接时间顺序的重建。在本文中，我们提出了一种通用的全局网络重构方法。每个网络连接都由一个唯一的标签定义，使其能够在时间和空间上进行跟踪。在这项工作中，我们一方面区分了外侧支和顶端分支，另一方面区分了四肢。最后，我们在识别并消除重叠后重建网络。然后将该方法应用于模型丝状真菌 Podospora anserina 以重建其生长的菌体。我们得出了区分根尖支和侧支的标准。我们发现外环有利地由顶端分支组成，而网络内的致密化来自侧支。由此，我们得出了两种类型中每一种的特定动力学。最后，在生长开始期间没有任何潜伏期的情况下，我们可以根据顶端和横向分支集合的相等性重建一个时间。这使得直接比较不同菌体的生长动态成为可能。