迈向虚拟秀丽隐杆线虫:在三维物理环境中模拟和可视化神经肌肉系统的框架。

Q2 Medicine
Andrey Palyanov, Sergey Khayrulin, Stephen D Larson, Alexander Dibert
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引用次数: 38

摘要

秀丽隐杆线虫是唯一一种已知具有神经元接线图或“连接体”的动物。在过去的三十年中,对秀丽隐杆线虫的广泛研究提供了广泛的数据,但很少有系统的方法将这些数据整合到一个动态模型中。在这里,我们提出了一个虚拟秀丽隐杆线虫的详细演示,旨在以在模拟物理环境中操作的3D动态模型的形式整合这些数据。我们目前的演示包括一个现实的柔性蠕虫体模型,肌肉系统和部分实现的腹侧神经索。我们的虚拟秀丽隐杆线虫在向运动神经元群发送神经元活动的正弦模式时,展示了成功的向前和向后运动。考虑到相对较慢的传播速度和神经元信号的衰减,我们在模型中引入了“伪神经元”来模拟简化的神经元动力学。伪神经元也为神经系统的结构和活动动态可视化提供了一种很好的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards a virtual C. elegans: a framework for simulation and visualization of the neuromuscular system in a 3D physical environment.

The nematode C. elegans is the only animal with a known neuronal wiring diagram, or "connectome". During the last three decades, extensive studies of the C. elegans have provided wide-ranging data about it, but few systematic ways of integrating these data into a dynamic model have been put forward. Here we present a detailed demonstration of a virtual C. elegans aimed at integrating these data in the form of a 3D dynamic model operating in a simulated physical environment. Our current demonstration includes a realistic flexible worm body model, muscular system and a partially implemented ventral neural cord. Our virtual C. elegans demonstrates successful forward and backward locomotion when sending sinusoidal patterns of neuronal activity to groups of motor neurons. To account for the relatively slow propagation velocity and the attenuation of neuronal signals, we introduced "pseudo neurons" into our model to simulate simplified neuronal dynamics. The pseudo neurons also provide a good way of visualizing the nervous system's structure and activity dynamics.

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来源期刊
In Silico Biology
In Silico Biology Computer Science-Computational Theory and Mathematics
CiteScore
2.20
自引率
0.00%
发文量
1
期刊介绍: The considerable "algorithmic complexity" of biological systems requires a huge amount of detailed information for their complete description. Although far from being complete, the overwhelming quantity of small pieces of information gathered for all kind of biological systems at the molecular and cellular level requires computational tools to be adequately stored and interpreted. Interpretation of data means to abstract them as much as allowed to provide a systematic, an integrative view of biology. Most of the presently available scientific journals focus either on accumulating more data from elaborate experimental approaches, or on presenting new algorithms for the interpretation of these data. Both approaches are meritorious.
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