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.

项目概要 衰老等生理过程必须由跨越多个时间的活动和事件引起 秤。尽管这些过程很重要，但其动态却很难测量和研究。在多细胞 作为衰老的模型生物，自由生活的线虫秀丽隐杆线虫是研究最深入的线虫之一，然而，大多数 衰老研究仅限于简单的结果，例如寿命，完全忽略了过程 从而发生老化。这种限制部分是由于缺乏经济且可扩展的技术来获取 衰老过程中的详细数据（使用记录个体的行为和神经动态） 传统方法非常昂贵）；此外，还没有一个完善的理论（和 计算）方法来模拟衰老行为并在短期和长期之间建立联系 动力学。缺乏这些工具导致对机制的描述和理解非常有限 健康寿命，甚至在像秀丽隐杆线虫这样优秀的遗传模型系统中也是如此。该应用程序的目标是定义 衰老过程中的行为状态和动态，并检查行为的神经起源 动力学。首先，将设计高通量实验系统和强大的计算管道。 然后，这些工具将用于表征衰老过程中的短期和长期行为动态，特别是在 对食物供应的反应会导致寿命和健康的调节。将建立模型来连接 神经动力学到行为动力学，模型将解决它是否是神经调节 导致行为状态变化和不同的长期生理结果的动态。这 所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 个体数量并使用衰老过程中的随机动力系统模型进行充分表征；这是 这也是行为动力学模型第一次与神经动力学模型联系起来并由神经动力学模型解释。 拟议的工作意义重大，因为它产生了工具和见解。从科学角度来说，能力 定义内部状态并了解衰老轨迹，特别是深入了解大脑的神经起源 在各种长寿诱导条件下观察到的动态，将指出影响寿命的潜在策略 健康老龄化。从技术上讲，我们设想高通量行为记录平台和 神经成像流程将在秀丽隐杆线虫之外发挥作用，并且理论和计算流程 可以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。丰富的衰老行为数据集也将使其他衰老研究人员受益。