A stochastic framework to model axon interactions within growing neuronal populations.
A stochastic framework to model axon interactions within growing neuronal populations.
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DOI：
10.1371/journal.pcbi.1006627
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发表时间：
2018-12
影响因子：
4.3
通讯作者：
Descombes X
中科院分区：
文献类型：
--
作者：
Razetti A;Medioni C;Malandain G;Besse F;Descombes X
发育中的大脑的封闭和拥挤的环境对神经元细胞施加了空间限制，这些神经元细胞已经进化出个体和集体策略来优化其生长。这些包括将神经元组织成群体，将它们的轴突延伸到共同的目标区域。个体轴突如何在这些群体中相互作用以优化神经支配目前尚不清楚，并且难以在体内进行实验分析。在这里，我们开发了一个三维轴突生长的随机模型，考虑到空间环境的限制，相邻轴突之间的物理相互作用，和分支的形成。这个通用的，预测性和鲁棒性的模型，当喂养的参数估计的真实的神经元从果蝇的大脑，使轴突种群的增长背后的机械原理的研究。首先，它提供了一个新的解释的多样性的生长和分支模式在体内观察群体的遗传相同的神经元。其次，它发现轴突分支可能是一种策略，优化轴突在高轴突密度环境中与其他轴突竞争的整体生长。这个框架的灵活性，将使人们有可能调查的规则轴突生长和再生的背景下，各种神经元群体。了解神经元细胞如何在发育中的大脑中建立具有特定功能的复杂电路是当前的一个主要挑战。在过去的几年里，已经取得了巨大的进展，以精确地解决大脑解剖和解剖控制建立精确的神经元网络的机制。然而，由于大脑的极端复杂性，在体内研究神经元如何相互作用以及如何与它们的物理环境相互作用以在发育期间支配目标区域仍然是实验上困难的。在这里，我们已经开发了一个框架，它集成了一个动态的三维数学模型的单轴突生长的参数估计从体内生长的神经元和整个人口的生长轴突的模拟。我们的模型的涌现属性，使研究的机械原理的轴突人口的增长在发育中的大脑。具体来说，我们的研究结果突出了机械相互作用对个体和集体轴突生长的影响，并揭示了分支如何调节这一过程。
The confined and crowded environment of developing brains imposes spatial constraints on neuronal cells that have evolved individual and collective strategies to optimize their growth. These include organizing neurons into populations extending their axons to common target territories. How individual axons interact with each other within such populations to optimize innervation is currently unclear and difficult to analyze experimentally in vivo. Here, we developed a stochastic model of 3D axon growth that takes into account spatial environmental constraints, physical interactions between neighboring axons, and branch formation. This general, predictive and robust model, when fed with parameters estimated on real neurons from the Drosophila brain, enabled the study of the mechanistic principles underlying the growth of axonal populations. First, it provided a novel explanation for the diversity of growth and branching patterns observed in vivo within populations of genetically identical neurons. Second, it uncovered that axon branching could be a strategy optimizing the overall growth of axons competing with others in contexts of high axonal density. The flexibility of this framework will make it possible to investigate the rules underlying axon growth and regeneration in the context of various neuronal populations. Understanding how neuronal cells establish complex circuits with specific functions within a developing brain is a major current challenge. Over the last past years, enormous progress has been done to precisely resolve brain anatomy and to dissect the mechanisms controlling the establishment of precise neuronal networks. However, due to the extreme complexity of the brain, it is still experimentally difficult to investigate in vivo how neurons interact with each other and with their physical environments to innervate target territories during development. Here, we have developed a framework that integrates a dynamic 3D mathematical model of single axonal growth with parameters estimated from neurons grown in vivo and simulations of entire populations of growing axons. The emergent properties of our model enable the study of the mechanistic principles underlying the growth of axonal population in developing brains. Specifically, our results highlight the impact of mechanical interactions on both individual and collective axon growth, and uncover how branching regulate this process.
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