Network-Driven Dynamics of Replicative Aging

网络驱动的复制老化动态

• 批准号: 9369740
• 负责人: JEFF M HASTY
• 金额: $ 54.31万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9369740
• 关键词: Age; Aging; Aging-Related Process; Animal Model; Area; Behavior; Biogenesis; Biological; Biological Process; Biology of Aging; Cell Aging; Cell Cycle Kinetics; Cell Death; Cells; Chromatin; Chronic Disease; Complex; Computer Simulation; Cues; Cyclic AMP-Dependent Protein Kinases; Data; Daughter; Development; Diabetes Mellitus; Disease; Equilibrium; Exhibits; Future; Genes; Genetic; Genetic screening method; Genomic Instability; Genomic Segment; Genomics; Goals; HDAC4 gene; Healthcare; Heterochromatin; Lead; Longevity; Malignant Neoplasms; Measurement; Measures; Mediating; Microfluidics; Modeling; Molecular; Molecular Models; Morbidity - disease rate; Morphology; Neurodegenerative Disorders; Nutrient; Pathway interactions; Pattern; Phenotype; Population; Population Study; Process; Protein Dynamics; Regulation; Regulator Genes; Research; Research Personnel; Ribosomes; Risk Factors; Role; Saccharomyces cerevisiae; Signal Pathway; Stochastic Processes; Stress; Study models; Systems Biology; Technology; Testing; Time; Work; Yeast Model System; anti aging; base; biological adaptation to stress; biological systems; deletion analysis; genetic resource; healthy aging; insight; interdisciplinary approach; longevity gene; molecular dynamics; molecular modeling; mortality; network models; novel therapeutic intervention; phenomenological models; response; single cell analysis

Project Summary Cellular aging is a complex biological process, associated with many diseases, such as cancer, diabetes, and neurodegenerative diseases. New therapeutic approaches to slow aging hold promise for reducing global healthcare burdens of chronic diseases. However, the development of these approaches requires a deep understanding of the mechanisms of aging, which remains a challenging goal. Static population-based studies are insufficient to reveal sophisticated molecular mechanisms that underlie the aging process, because genetically identical cells have various intrinsic causes of aging and widely different rates of aging. Furthermore, although many single genes have profound effects on lifespan, how they interact and function within gene regulatory networks to regulate aging and how these interactions change dynamically during aging remain largely unknown. To overcome these challenges, we have developed high-throughput microfluidic technologies to track the dynamics of molecular processes throughout the replicative lifespans of single S.cerevisiae cells. In the proposed research, these dynamic measurement technologies will be integrated with computational modeling to systematically characterize and quantify the collective dynamic behaviors of aging-related molecular networks. In Aim 1, we will quantitatively characterize the phenotypic changes associated with distinct causes of cell aging and, based on these data, construct a phenomenological model of the aging process, upon which we will build mechanistic models of the conserved Sir2 and protein kinase A (PKA)-regulated molecular networks, both of which are deeply connected to aging. In particular, in Aim 2, we will develop a mechanistic model of the Sir2-regulated molecular network to predict its dynamics and regulatory roles during aging. High-throughout single-cell analysis will be performed to track the dynamics of Sir2-regulated genes and test the model predictions. In Aim 3, we will systematically characterize the PKA- regulated stress response during aging and develop a mechanistic model to quantify and predict the effects of environmental cues on aging. We will systematically examine the dynamics and contribution of stress response genes under various environmental perturbations. These experimental measurements will be used to test the predictions, refine the model, and more importantly, provide insight into the basic mechanisms underlying the environmental control of aging. To accomplish these aims, we have assembled a strong interdisciplinary team of investigators with complementary expertise, who will work synergistically to tackle fundamental questions in the biology of aging from a systems biology perspective. !

项目摘要 细胞衰老是一个复杂的生物学过程，与许多疾病（例如癌症）有关 糖尿病和神经退行性疾病。新的治疗方法缓慢衰老有望 减轻慢性疾病的全球医疗保健负担。但是，这些方法的发展 需要深入了解衰老机制，这仍然是一个具有挑战性的目标。静止的 基于人群的研究不足以揭示出衰老的基础的复杂分子机制 过程，因为遗传上相同的细胞具有衰老的各种固有原因，并且速率广泛不同 老化。此外，尽管许多单个基因对寿命具有深远影响，但它们的相互作用方式和 基因调节网络中的功能以调节衰老以及这些相互作用如何动态变化 在衰老期间，仍未知。为了克服这些挑战，我们开发了高通量 微流体技术在整个复制寿命中跟踪分子过程的动力学 单一经菌细胞。在拟议的研究中，这些动态测量技术将是 与计算建模集成以系统地表征和量化集体动态 与衰老相关的分子网络的行为。在AIM 1中，我们将定量表征表型 与细胞衰老的不同原因相关的变化，并基于这些数据构建现象学 老化过程的模型，我们将在其上构建保守的SIR2和蛋白质的机械模型 激酶A（PKA）调节的分子网络，两者都与衰老密切相关。特别是在 AIM 2，我们将开发一个由SIR2调节的分子网络的机械模型，以预测其动力学和 衰老期间的调节作用。将进行高通道单细胞分析以跟踪 SIR2调节的基因并测试模型预测。在AIM 3中，我们将系统地表征PKA- 在衰老过程中调节压力反应，并开发一种机械模型来量化和预测 衰老的环境线索。我们将系统地检查压力反应的动态和贡献 在各种环境扰动下的基因。这些实验测量将用于测试 预测，完善模型，更重要的是，提供了对依据基本机制的见解 衰老的环境控制。为了实现这些目标，我们组建了一个强大的跨学科团队 具有互补专业知识的调查人员的研究，他们将协同工作以解决基本问题 从系统生物学的角度来看，衰老的生物学。 呢