Network-Driven Dynamics of Replicative Aging

网络驱动的复制老化动态

• 批准号: 10176323
• 负责人: JEFF M HASTY
• 金额: $ 55.71万
• 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 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10176323
• 关键词: Age; Aging; Animal Model; Area; Behavior; Biogenesis; Biological; Biological Process; Biology of Aging; Cell Aging; Cell Cycle Kinetics; Cell Death; Cells; Chromatin; Chronic Disease; Complex; Computer Models; Cues; Cyclic AMP-Dependent Protein Kinases; Data; Daughter; Development; Diabetes Mellitus; Disease; Equilibrium; Exhibits; Future; Genes; Genetic; Genomic Instability; Genomic Segment; Genomics; Goals; HDAC4 gene; Healthcare; Heterochromatin; Lead; Longevity; Malignant Neoplasms; Measurement; Measures; Mediating; Microfluidics; Modeling; Molecular; 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; cell injury; deletion analysis; genetic resource; genetic testing; healthy aging; insight; interdisciplinary approach; longevity gene; microfluidic technology; molecular dynamics; molecular modeling; mortality; network models; novel therapeutic intervention; phenomenological models; predictive modeling; 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. !

项目概要 细胞衰老是一个复杂的生物过程，与许多疾病相关，例如癌症、 糖尿病和神经退行性疾病。延缓衰老的新治疗方法有望 减轻全球慢性病的医疗负担。然而，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衰老背后的复杂分子机制 过程中，因为基因相同的细胞有多种内在的衰老原因，而且衰老的速度也有很大差异。 老化。此外，尽管许多单基因对寿命有深远的影响，但它们如何相互作用和 基因调控网络中的功能调节衰老以及这些相互作用如何动态变化 衰老过程中的情况仍然很大程度上未知。为了克服这些挑战，我们开发了高通量 微流控技术可在整个复制寿命期间跟踪分子过程的动态 单个酿酒酵母细胞。在拟议的研究中，这些动态测量技术将 与计算模型相结合，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。在目标 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，并根据这些数据构建一个现象学模型 衰老过程的模型，在此基础上我们将建立保守的 Sir2 和蛋白质的机制模型 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 目标 2，我们将开发 Sir2 调节的分子网络的机制模型来预测其动态和 衰老过程中的调节作用。将进行高通量单细胞分析来跟踪 Sir2 调控基因并测试模型预测。在目标 3 中，我们将系统地描述 PKA- 调节衰老过程中的应激反应，并开发机械模型来量化和预测其影响 环境因素对衰老的影响。我们将系统地研究压力反应的动态和贡献 不同环境扰动下的基因。这些实验测量将用于测试 预测，完善模型，更重要的是，提供对潜在的基本机制的深入了解 老化的环境控制。为了实现这些目标，我们组建了一支强大的跨学科团队 具有互补专业知识的研究人员，他们将协同工作来解决 从系统生物学角度研究衰老生物学。 ！