A C. elegans whole-brain digital twin

线虫全脑数字双胞胎

• 批准号: BB/Z514317/1
• 负责人: Netta Cohen
• 金额: $ 32.86万
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FZ514317%2F1
• 关键词: C.; elegans; whole; brain; digital

Brain research has witnessed remarkable advances in recent decades. And yet, the dynamics of neural circuits, their specification of an animal's behaviours, adaptation to context or internal state, and variability across individuals, remain poorly understood. To integrate neuronal function, circuit-level computation, and brain-wide coordination, whole-brain imaging in freely-behaving animals is essential. While daunting in most animals this technology is available and fast-maturing in the mm-long nematode, C. elegans.Despite its relative simplicity, C. elegans is a freely behaving animal that makes decisions, learns, forgets, adapts to ever-changing conditions, and engages in collective behaviour, in order to survive, forage for food and escape predation. Like all animals, it develops, sleeps and ages, and its study has proved it a powerful model system for neurobiology, neurogenetics, the neural basis of learning, plasticity and behaviour, and neurodegeneration.While the functions of many C. elegans neurons have been studied extensively, understanding the dynamics of larger circuits poses new challenges: whole-brain imaging provides essential observation of neuronal activity, but not the interactions between neurons. We therefore argue that to obtain an integrated understanding at cellular, circuit and global-brain levels requires mechanistic and explanatory models. Such models must account for brain-wide activity that emerges from the neural circuitry, as specified by an animal's connectome. To address this goal, our overall aim is to build the first digital twin of the C. elegans brain.A digital twin is a software representation of a real-world system, used as a model to predict, explain or control the system's response under different conditions. While commonly applied to engineering assets, the methodology, and the challenges (in particular, limited access to the internal working and limited observables of the outputs) suggest important commonalities with whole-brain modelling from data.Specific objectives include:AI: To develop AI tools to train a digital twin, based on whole-brain-activity data constrained by the C. elegans connectome.To apply, test and extend optimisation methods for whole-brain models of individual animals, using brain-wide activity data for >50 animals.To augment whole-brain-data and bootstrap our optimisation methods using deep neural models that learn low-dimensional representations of high-dimensional time-series (i.e. neural activity traces).To unify our framework in order to obtain families of solutions representing clusters of model animals with similar neuronal activation patterns and behavioural encoding.To develop and apply novel AI tools for training populations of models based on populations of datasets, using probabilistic and population density tools.Digital Twin: To develop biologically-grounded mechanistic models of the C. elegans brain, at cellular resolution.To implement neuronal and circuit models with appropriate grounding in C. elegans neurobiology, e.g. the conserved and variable connectome, known synaptic polarities, bilateral symmetry, etc.To test and evaluate optimised models against data and implement post-selection mechanisms for successful solutions, based on biological realism.To apply successful models in simulations to derive predictions for validation experiments and new hypotheses for future research, with focus on understanding distributed encoding and its flexibility, adaptability and variability.If successful, a digital twin will transform our understanding of the C. elegans brain, and hence, the nervous systems of other animals. This project, will put in place AI tools that bring us closer to this goal. The novel AI, and the integration of AI, simulations and complex data, will benefit the construction of other digital twins, across life and engineering sciences.

近几十年来，大脑研究取得了显着进展。然而，人们对神经回路的动态、动物行为的规范、对环境或内部状态的适应以及个体之间的变异性仍然知之甚少。为了整合神经元功能、电路级计算和全脑协调，对自由行为的动物进行全脑成像至关重要。虽然这项技术对大多数动物来说令人望而生畏，但在毫米长的线虫秀丽隐杆线虫中已经可用，并且快速成熟。尽管它相对简单，但秀丽隐杆线虫是一种行为自由的动物，可以做出决定、学习、忘记、适应不断变化为了生存、寻找食物和逃避捕食，它们会进行集体行为。像所有动物一样，它也会发育、睡眠和衰老，它的研究证明它是神经生物学、神经遗传学、学习的神经基础、可塑性和行为以及神经变性的强大模型系统。经过广泛的研究，理解更大电路的动力学提出了新的挑战：全脑成像提供了对神经元活动的基本观察，但不能观察神经元之间的相互作用。因此，我们认为，要获得细胞、电路和全球大脑水平的综合理解需要机械和解释模型。这些模型必须考虑由动物连接组指定的神经回路产生的全脑活动。为了实现这一目标，我们的总体目标是构建线虫大脑的第一个数字孪生。数字孪生是现实世界系统的软件表示，用作模型来预测、解释或控制系统在以下情况下的响应：不同的条件。虽然通常应用于工程资产，但该方法和挑战（特别是对内部工作的访问有限和输出的可观察性有限）表明了数据全脑建模的重要共性。具体目标包括：人工智能：开发人工智能基于线虫连接组限制的全脑活动数据来训练数字双胞胎的工具。使用超过 50 个动物的全脑活动数据来应用、测试和扩展个体动物全脑模型的优化方法增强全脑数据并使用深度神经模型引导我们的优化方法，这些模型学习高维时间序列的低维表示（即神经活动轨迹）。统一我们的框架以获得代表的解决方案系列具有相似神经元激活模式和行为编码的模型动物集群。开发和应用新颖的人工智能工具，使用概率和群体密度工具，基于数据集群体来训练模型群体。数字孪生：开发基于生物的模型细胞分辨率下的线虫大脑机制模型。以线虫神经生物学为基础，实现神经元和电路模型，例如保守和可变的连接组、已知的突触极性、双边对称性等。根据数据测试和评估优化模型，并基于生物现实主义实现成功解决方案的后选择机制。在模拟中应用成功的模型来得出验证实验的预测以及未来研究的新假设，重点是了解分布式编码及其灵活性、适应性和可变性。如果成功，数字孪生将改变我们对线虫大脑以及其他动物神经系统的理解。该项目将部署人工智能工具，使我们更接近这一目标。新颖的人工智能以及人工智能、模拟和复杂数据的集成将有利于生命和工程科学领域其他数字孪生的构建。