Network-Driven Dynamics of Replicative Aging

项目摘要 细胞衰老是一个复杂的生物过程，与许多疾病有关，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 需要对衰老的机制有深刻的了解，这仍然是一个具有挑战性的目标。静电 基于人群的研究不足以揭示导致衰老的复杂的分子机制 过程，因为遗传上相同的细胞有各种老化的内在原因和非常不同的比率 衰老。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 在基因调控网络中调节衰老的功能以及这些相互作用如何动态变化 在衰老过程中，很大程度上仍不清楚。为了克服这些挑战，我们开发了高吞吐量 微流控技术跟踪分子过程的整个复制生命周期的动力学 单个酿酒酵母细胞。在拟议的研究中，这些动态测量技术将被 与计算模型相结合，系统地描述和量化集体动态 衰老相关分子网络的行为。在目标1中，我们将对表型进行定量表征 与细胞老化的不同原因相关的变化，并基于这些数据构建了现象学的 衰老过程的模型，我们将在此基础上建立保守的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 目的2，我们将建立一个sir2调控的分子网络的机制模型，以预测其动力学和 衰老过程中的调节作用。将执行高通量单细胞分析以跟踪 Sir2调节基因，并测试模型预测。在目标3中，我们将系统地描述PKA- 在衰老过程中调节应激反应，并开发一个机制模型来量化和预测 关于衰老的环境线索。我们将系统地研究应激反应的动力学和贡献。 在各种环境扰动下的基因。这些实验测量将被用来测试 预测，改进模型，更重要的是，提供对潜在的 老龄化的环境控制。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调查人员，他们将协同工作，解决基本问题 从系统生物学的角度研究衰老的生物学。 好了！

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. !