What Can Networks Tell Us About Aging?

关于衰老，网络可以告诉我们什么？

• 批准号: 1243295
• 负责人: Tijana Milenkovic
• 金额: $ 19.99万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1243295&HistoricalAwards=false
• 关键词: What; Networks; Tell; Us; About

The US is growing older because of millions of baby boomers who already started turning 65. Since susceptibility to diseases increases with age, studying molecular causes of aging gains importance. Human lifespan is long, which, in addition to ethical constraints, makes studying human aging difficult. Therefore, aging is studied in simpler ?model? species, e.g., baker?s yeast. Then, the knowledge about aging is transferred from model species to human. Thus far, this transfer has been restricted to genomic sequence comparison, by identifying regions of similarity between sequences of genes in different species (which are believed to be a consequence of functional relationships between the sequences), and by transferring the knowledge from a gene in model species to a sequence-similar gene in human. However, genes (that is, their protein products) carry out biological function by interacting in complex networked ways with one another, instead of acting alone. Hence, it has been argued in the post-genomic era that the wirings among genes in cellular networks could give biological insights over and above sequences of individual genes. Thus, this project hypothesizes that, analogous to genomic sequence research, biological network research will impact our understanding of aging. For example, since not all genes implicated in aging in model species have sequence-similar genes in human, restricting comparison to sequence may limit the transfer of aging-related knowledge to human. Network comparison can help, as it can find regions of similarities between networks of different species and allow for a transfer of the knowledge between such regions.Intellectual merit: Unlike genomic sequence research, biological network research is in its infancy, for the following reasons. Many network problems (including network comparison) are computationally intractable, and hence, efficient approximate (or heuristic) solutions are needed. The function of many genes remains unknown, and hence, it must be discovered from other, better-characterized genes. Even though cells evolve over time, current methods for analyzing systems-level biological networks deal only with their static representations, because dynamic biological network data can not be obtained easily with current biotechnologies, and because there is a lack of efficient methods for dynamic network analysis. Current biological networks are noisy, with many missing and spurious links, due to limitations of biotechnologies as well as human biases during data collection; thus, methods for network de-noising need to be developed. Hence, this project aims to use sensitive measures of network structure (or topology) to develop new heuristic computational methods for efficient network analysis, which can cope with the complexity of functionally uncharacterized, dynamic, and noisy biological networks. Also, it aims to help in understanding the processes of human aging by enabling exploitation of biological network data. Specifically, the new methods will be used to: transfer the knowledge about aging from model species to human to complement the knowledge obtained from sequence; study dynamic human biological networks (obtained computationally by combining current static networks with age-specific gene expression data) to learn about how cells change with age; and de-noise current networks to produce higher-confidence results.Broader impacts: Understanding aging is of societal importance. Since network research spans many domains, the proposed methods will be implemented into open-source research software, which will also serve as an educational tool. Integration of research and education will be promoted further by training interdisciplinary scientists through novel courses on network research. Research supervision will be offered to K-12, undergraduate, and graduate students, focusing on minorities and women. Interdisciplinary collaborations will be encouraged to allow for wide distribution of the proposed ideas and results.

美国正在变老，因为数百万婴儿潮一代已经开始进入65岁。由于对疾病的易感性随着年龄的增长而增加，因此研究衰老的分子原因变得重要。人类的寿命很长，除了道德约束外，这使得研究人类衰老变得困难。因此，老化是在更简单的研究？模特儿？物种，例如，baker？s酵母。然后，关于衰老的知识从模型物种转移到人类。到目前为止，这种转移仅限于基因组序列比较，通过鉴定不同物种中基因序列之间的相似性区域（这被认为是序列之间的功能关系的结果），以及通过将知识从模型物种中的基因转移到人类中的序列相似基因。然而，基因（即它们的蛋白质产物）通过以复杂的网络方式相互作用而不是单独作用来实现生物功能。因此，在后基因组时代，有人认为细胞网络中基因之间的连接可以提供超越单个基因序列的生物学见解。因此，该项目假设，类似于基因组序列研究，生物网络研究将影响我们对衰老的理解。例如，由于并非模型物种中所有与衰老有关的基因都与人类中的序列相似，因此限制与序列的比较可能会限制衰老相关知识向人类的转移。网络比较可以帮助，因为它可以找到不同物种网络之间的相似区域，并允许在这些区域之间转移知识。智力价值：与基因组序列研究不同，生物网络研究处于起步阶段，原因如下。许多网络问题（包括网络比较）在计算上是难以处理的，因此，需要有效的近似（或启发式）解决方案。许多基因的功能仍然是未知的，因此，它必须从其他更好的特征基因中发现。尽管细胞随着时间的推移而进化，但目前用于分析系统级生物网络的方法仅处理它们的静态表示，因为动态生物网络数据不能用当前生物技术容易地获得，并且因为缺乏用于动态网络分析的有效方法。由于生物技术的限制以及数据收集过程中的人为偏见，目前的生物网络是嘈杂的，有许多缺失和虚假的链接;因此，需要开发网络去噪的方法。因此，该项目旨在使用网络结构（或拓扑结构）的敏感措施来开发新的启发式计算方法，以进行有效的网络分析，这可以科普功能未表征，动态和嘈杂的生物网络的复杂性。此外，它旨在通过利用生物网络数据来帮助理解人类衰老的过程。具体而言，新方法将用于：将关于衰老的知识从模型物种转移到人类，以补充从序列中获得的知识;研究动态人类生物网络（通过将当前静态网络与年龄特异性基因表达数据相结合，以计算方式获得），以了解细胞如何随年龄变化;并对当前网络进行降噪处理，以产生更高置信度的结果。理解老龄化具有社会重要性。由于网络研究跨越许多领域，因此所提出的方法将被实施到开源研究软件中，该软件也将作为教育工具。通过新颖的网络研究课程培训跨学科科学家，进一步促进研究与教育的融合。研究监督将提供给K-12，本科和研究生，重点是少数民族和妇女。将鼓励跨学科合作，以便广泛传播所提出的想法和成果。