WHole Animal Modelling (WHAM): Toward the integrated understanding of sensory motor control in C. elegans

整体动物建模（WHAM）：全面理解秀丽隐杆线虫的感觉运动控制

• 批准号: EP/J004057/1
• 负责人: Netta Cohen
• 金额: $ 151.12万
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2011
• 资助国家: 英国
• 起止时间: 2011 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FJ004057%2F1
• 关键词: WHole; Animal; Modelling; WHAM; Toward

Animals are remarkable creatures. No man-made machine even comes close in its ability to navigate complex environments, respond to rich sensory cues, learn and adapt its behaviour when encountering completely novel scenarios and much much more. But even the simplest animals, ruled by even the simplest nervous systems, can achieve this. Simple animals may not be able to play chess or balance bank statements, but there is much we can learn from them about their robust mechanisms for sensory-integration and motor control which may be of use to us in control engineering, bio-robotics and even in future brain-machine interfaces that are being developed for neuro-prosthetic applications.An excellent starting point for understanding animal behaviour is a tiny, free living 1mm long roundworm, called C. elegans. By comparison to our 100 billion nerve cells, or even a fly's 100 thousand nerve cells, this worm's entire nervous system consists of a mere 302 nerve cells. Unlike our nervous system, the worm's circuitry is hard wired and identical across individuals of the species, making it possible to study rigorously and reproducibly. Due in part to its simplicity, and in part to its ease of manipulation in the lab, this is the only animal for which this entire nervous system has been mapped in exquisite detail (to sub-cellular resolution). But despite its relative simplicity, this worm possesses many of the functions that are attributed to more complex animals, including feeding, mating, complex sensory abilities, memory and learning. It is not surprising, therefore, that the modelling of this worm has captured the imagination of physicists, computer scientists and engineers alike. The integrated modelling of C. elegans has even been proposed as one of the UK's ``Grand Challenges for Computing Research.''In this Fellowship, I will begin to integrate our understanding of C. elegans sensory motor behaviour in a single computational model. The challenge is to bridge the gap between the effectively static neural circuit architecture and the dynamic neural computation it sustains. This fellowship will enable me to deliver a step change, not only in our understanding of an important model organism, but also in advancing the science and engineering of complex systems, whether in the context of reverse engineering real world networks, or in the context of designing them.

动物是非凡的生物。在导航复杂环境、响应丰富的感官线索、在遇到全新场景时学习和适应其行为等方面，没有任何人造机器能够与之相媲美。但即使是最简单的动物，由最简单的神经系统控制，也能做到这一点。简单的动物可能无法下棋或平衡银行报表，但我们可以从它们身上学到很多关于它们强大的感觉统合和运动控制机制的知识，这些机制可能对我们在控制工程、生物机器人甚至未来脑机接口正在为神经假体应用而开发。理解动物行为的一个很好的起点是一种微小的、自由生活的 1 毫米长的蛔虫，称为线虫。与我们的1000亿个神经细胞，甚至苍蝇的10万个神经细胞相比，这种蠕虫的整个神经系统只有302个神经细胞。与我们的神经系统不同，线虫的电路是硬连线的，并且在物种个体之间是相同的，这使得严格和可重复的研究成为可能。部分由于其简单性，部分由于其在实验室中易于操作，这是唯一一种其整个神经系统已被精确绘制（亚细胞分辨率）的动物。尽管这种蠕虫相对简单，但它却拥有许多更复杂的动物所具有的功能，包括进食、交配、复杂的感官能力、记忆和学习。因此，这种蠕虫病毒的建模引起了物理学家、计算机科学家和工程师等人的想象力也就不足为奇了。线虫的集成建模甚至被提议为英国“计算研究的重大挑战”之一。在本次奖学金中，我将开始将我们对线虫感觉运动行为的理解整合到一个计算模型中。挑战在于弥合有效的静态神经电路架构与其所支持的动态神经计算之间的差距。这项奖学金将使我能够实现一步改变，不仅在我们对重要模型生物的理解方面，而且在推进复杂系统的科学和工程方面，无论是在逆向工程现实世界网络的背景下，还是在设计它们。

项目成果

A new computational method for a model of C. elegans biomechanics: Insights into elasticity and locomotion performance

线虫生物力学模型的新计算方法：洞察弹性和运动性能

• DOI: http://dx.10.48550/arxiv.1702.04988
• 发表时间: 2017
• 作者: Cohen N

Inhibition Underlies Fast Undulatory Locomotion in Caenorhabditis elegans.

抑制是秀丽隐杆线虫快速波动运动的基础。

• DOI: http://dx.10.1523/eneuro.0241-20.2020
• 发表时间: 2021
• 期刊: eNeuro
• 影响因子: 3.4
• 作者: Deng L

Supplementary Materials from Signatures of proprioceptive control in

本体感觉控制特征的补充材料

• DOI: http://dx.10.6084/m9.figshare.6993668
• 发表时间: 2018
• 作者: Denham J

A multi-scale brain map derived from whole-brain volumetric reconstructions.

来自全脑体积重建的多尺度脑图。

• DOI: http://dx.10.1038/s41586-021-03284-x
• 发表时间: 2021
• 期刊: Nature
• 影响因子: 64.8
• 作者: Brittin CA

Nematode locomotion: dissecting the neuronal-environmental loop.

线虫运动：解剖神经元环境环路。

• DOI: http://dx.10.1016/j.conb.2013.12.003
• 发表时间: 2014
• 期刊: Current opinion in neurobiology
• 影响因子: 5.7
• 作者: Cohen N