Inferring multi-scale dynamics underlying behavior in aging C. elegans

推断衰老线虫行为背后的多尺度动力学

• 批准号: 10638631
• 负责人: Gordon Joseph Berman
• 金额: $ 57万
• 依托单位: GEORGIA INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2028-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10638631
• 关键词: Aging; Animal Behavior; Animal Model; Animals; Automobile Driving; Behavior; Behavioral; Biological; Biological Models; Biological Phenomena; Biological Process; Brain; Caenorhabditis elegans; Calcium; Cell model; Communities; Computer software; Cues; Data; Data Set; Development; Dimensions; Disease; Engineering; Environment; Event; Food; Genes; Genetic; Genetic Models; Goals; Health; Human; Image; Individual; Intermittent fasting; Intervention; Link; Longevity; Measurement; Measures; Metabolic; Methods; Modeling; Mus; Nematoda; Neurons; Neurosciences; Outcome; Output; Pathology; Physiological; Physiological Processes; Physiology; Process; Research; Research Personnel; Scheme; System; Techniques; Technology; Time; Work; age related; biological systems; computational pipelines; data quality; data-driven model; design; dynamic system; experience; experimental study; feeding; healthspan; healthy aging; high dimensionality; in vivo; innovation; insight; instrumentation; machine learning method; model organism; mutant; nervous system disorder; neural; neural circuit; neurogenetics; neuroimaging; nonhuman primate; response; sex; theories; tool

Project Summary Physiological processes such as aging must arise from activities and events spanning multiple time scales. Although important, the dynamics of these processes are difficult to measure and study. In multicellular model organisms for aging, the freely living nematode C. elegans is among the best studied, and yet, most of the aging studies are limited to simple outcomes such as lifespan and completely ignore the process through which aging occurs. This limitation is in part due to the lack of economical and scalable technologies to acquire detailed data during the aging process (recording behavior and neural dynamics on individual basis using conventional approaches are very expensive); further, there has not been a well-established theoretical (and computational) approach to model the aging behavior and make connections between short- and long-term dynamics. The lack of these tools result in the very limited description and understanding of the mechanisms of healthspan, even in excellent genetic model systems as C. elegans. The goal of this application is to define behavioral states and dynamics in the aging process and examine the neural origins of the behavioral dynamics. First, high-throughput experimental systems and robust computational pipelines will be engineered. The tools will then be used to characterize short- and long-term behavior dynamics in aging, especially in response to food availability that results in modulations of longevity and health. Models will be built to connect the neural dynamics to behavioral dynamics, and the models will address whether it is modulations in neural dynamics that lead to changes in behavioral states and different long-term physiological outcomes. The proposed project is innovative, because it is the first time multi-scale behavioral dynamics is recorded in large number of individuals and fully characterized using stochastic dynamical system models in aging process; it is also the first time that behavioral dynamical models are connected to and explained by neural dynamical models. The proposed work is significant, because of both the tools and insights it generates. Scientifically, the ability to define internal states and understand the aging trajectory, especially with insights to the neural origin of the observed dynamics under a variety of longevity-inducing conditions, will point to potential strategies to influence healthy aging. Technologically, we envision that both the high-throughput behavioral recording platform and the neural imaging pipeline will be useful beyond C. elegans, and that the theoretical and computational pipelines can potentially be generalized to many other experimental systems, including mice, non-human primates, and humans. The rich data set for aging behavior will also benefit other aging researchers.

项目摘要 生理过程，如衰老，必须由跨越多个时间的活动和事件引起 比例。尽管很重要，但这些过程的动态很难衡量和研究。在多蜂窝网络中 作为衰老的模式生物，自由生活的线虫线虫是研究得最好的生物之一，然而，大多数 衰老研究仅限于寿命等简单结果，而完全忽略了整个过程 哪种情况会发生老化。这种限制在一定程度上是由于缺乏经济和可扩展的技术来获得 老化过程中的详细数据(使用以下工具记录个人行为和神经动力学 传统的方法非常昂贵)；此外，还没有一个完善的理论(和 计算)建模老化行为并在短期和长期之间建立联系的方法 动力学。由于缺乏这些工具，导致对这些机制的描述和理解非常有限 健康寿命，即使在像线虫这样优秀的遗传模型系统中也是如此。此应用程序的目标是定义 衰老过程中的行为状态和动力学，并考察行为的神经来源 动力学。首先，将设计高通量的实验系统和强大的计算管道。 然后，这些工具将被用来表征衰老过程中的短期和长期行为动态，特别是在 对食物供应的反应，导致长寿和健康的调节。将构建模型以连接 从神经动力学到行为动力学，模型将解决它是否是神经调节 导致行为状态改变和不同长期生理结果的动力学。这个 提出的项目是创新的，因为这是第一次大规模记录多尺度行为动力学 老龄化过程中的个体数量，并使用随机动力系统模型进行充分表征；它是 也是第一次将行为动力学模型与神经动力学模型联系起来并由其解释。 这项拟议的工作意义重大，因为它产生了工具和见解。从科学上讲，能力 定义内部状态并了解衰老轨迹，特别是对 在各种长寿诱导条件下观察到的动态变化，将指向潜在的影响策略 健康的衰老。在技术上，我们设想高通量的行为记录平台和 神经成像管道将用于线虫以外的领域，而理论和计算管道 可以潜在地推广到许多其他实验系统，包括小鼠、非人类灵长类动物和 人类。丰富的老龄化行为数据集也将使其他老龄化研究人员受益。