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Network control principles predict neuron function in the Caenorhabditis elegans connectome

机译:网络控制原理预测Caenorhabditis elegans Concentome中的神经元功能

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摘要

Recent studies on the controllability of complex systems offer a powerful mathematical framework to systematically explore the structure-function relationship in biological, social, and technological networks. Despite theoretical advances, we lack direct experimental proof of the validity of these widely used control principles. Here we fill this gap by applying a control framework to the connectome of the nematode Caenorhabditis elegans, allowing us to predict the involvement of each C. elegans neuron in locomotor behaviours. We predict that control of the muscles or motor neurons requires 12 neuronal classes, which include neuronal groups previously implicated in locomotion by laser ablation, as well as one previously uncharacterized neuron, PDB. We validate this prediction experimentally, finding that the ablation of PDB leads to a significant loss of dorsoventral polarity in large body bends. Importantly, control principles also allow us to investigate the involvement of individual neurons within each neuronal class. For example, we predict that, within the class of DD motor neurons, only three (DD04, DD05, or DD06) should affect locomotion when ablated individually. This prediction is also confirmed; single cell ablations of DD04 or DD05 specifically affect posterior body movements, whereas ablations of DD02 or DD03 do not. Our predictions are robust to deletions of weak connections, missing connections, and rewired connections in the current connectome, indicating the potential applicability of this analytical framework to larger and less well-characterized connectomes.
机译:最近关于复杂系统可控性的研究提供了强大的数学框架,以系统地探索生物,社会和技术网络中的结构功能关系。尽管有理论推进,但我们缺乏直接的实验证明这些广泛使用的控制原则的有效性。在这里,我们通过将控制框架应用于Nematode caenorhabditis elegis的Concentome,允许我们预测每个C.秀丽隐杆线的参与在机车行为中。我们预测肌肉或运动神经元的控制需要12个神经元类,其包括先前通过激光消融的神经元基团,以及先前未表征的神经元PDB。我们通过实验验证这一预测,发现PDB的消融导致大体弯曲中的无背叶极性的显着损失。重要的是,控制原则也允许我们调查每个神经甲类别中单个神经元的累积。例如,我们预测,在DD电动机神经元的类别中,只有三个(DD04,DD05或DD06)应该在单独消融时影响运动量。这种预测也得到证实; DD04或DD05的单电池消融特异性影响后体运动,而DD02或DD03的消融则不会。我们的预测是对当前连接中的弱连接,缺失连接和重新连接的缺失,表明该分析框架的潜在适用性较大,缺少较少的Connectomes。

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