CAA: Self-organization and Robustness in Evolving Biological Networks

CAA：进化生物网络中的自组织和鲁棒性

• 批准号: 0615660
• 负责人: Hernan Makse
• 金额: $ 15万
• 依托单位: CUNY City College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2008-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0615660&HistoricalAwards=false
• 关键词: CAA; Self; organization; Robustness; Evolving

The robustness of biological systems is thought to arise from the interaction among its constituents, proteins, DNA, RNA and small molecules, which form an intricate web of molecules. Because of the recent progress in biological research and computation, the long-standing efforts to get a global perspective on the cellular phenomena seem to be witnessing a turning point. However, the task of elucidating design principles has been hampered by formidable amount of information even in simple model organisms, such as E. coli and yeast. The project seeks a fruitful insight from the massive analyses of typical cellular networks of protein-protein interaction and metabolism, and initiates novel statistical analyses originally developed by the same research group in the more general context of complex networks. A preliminary observation is that the correlations in node connectivity of various cellular networks are very unique compared to those seen in other non-biological systems, which reacts the inherent organizing principles of functional network formation. Fractal scaling and topological self-similarity in biological networks will provide yet unprecedented perspective on our view of biological complexity. When those networks are observed with varying scales, they consistently show the self-replicating pattern of fractal with finite fractal dimensions, which has a direct implication on the structural stability and growth mechanism of the network. In particular, the potential relevance of the scale transformation to the evolutionary process now starts to be appreciated, where the evolutionary pathways mimic the growth of the network. However, it still remains unclear how the emerging topological properties of biological networks was achieved in the long history of evolution and how it is related with the error-tolerance level of the network, on which the research will be concentrated.The intellectual merit of the project stems from the generic quantitative methodology based on the statistical analysis of self-similar structures widely occurring in Nature. Moreover the novel scheme of fractality in biological networks will find direct applications in the newly rising interdisciplinary fields at the interfaces of biology, sociology, and computer sciences. At the same time, once enough evidence supporting the new scheme has been accumulated, it will provide a unique tool for assessing the fidelity of existing network data.Broader Impacts: The main impact of the project includes the curriculum development and involvement of underrepresented minority students in science. The project will develop of an interdisciplinary course on complex networks in biology and other disciplines which will be tightly integrated with the research plan.

人们认为，生物系统的鲁棒性是由于其成分，蛋白质，DNA，RNA和小分子之间的相互作用而产生的，这些分子形成了复杂的分子网。由于生物学研究和计算的最新进展，要获得全球视野的长期努力似乎正在见证一个转折点。但是，即使在简单的模型生物（例如大肠杆菌和酵母）中，阐明设计原理的任务也受到了大量信息的阻碍。该项目从对蛋白质 - 蛋白质相互作用和代谢的典型细胞网络的大规模分析中寻求富有成果的见解，并启动了同一研究小组在复杂网络的更一般环境中最初开发的新型统计分析。初步观察是，与其他非生物系统中所看到的相比，各种蜂窝网络的节点连接性的相关性非常独特，这反应了功能网络形成的固有组织原理。生物网络中的分形缩放和拓扑自相似性将对我们对生物复杂性的看法提供前所未有的观点。当这些网络以不同的尺度观察时，它们始终显示出具有有限分形尺寸的分形的自我复制模式，这直接影响了网络的结构稳定性和生长机制。特别是，现在开始理解量表转换与进化过程的潜在相关性，而进化途径模仿了网络的增长。但是，仍然尚不清楚生物网络的新兴拓扑特性如何在悠久的进化史以及它与网络的易耐受性水平相关的情况下，将研究集中在该网络的智力水平上。此外，生物网络中的新型分形方案将在生物学，社会学和计算机科学界面的新跨学科领域中找到直接应用。同时，一旦积累了支持新计划的足够证据，它将提供一个独特的工具来评估现有网络数据的保真度。BRODERIMPACTS：该项目的主要影响包括课程发展和代表性不足的少数族裔学生在科学方面的参与。该项目将在生物学和其他学科的复杂网络上开发跨学科课程，该课程将与研究计划紧密融合。