CAA: Self-organization and Robustness in Evolving Biological Networks

生物系统的鲁棒性被认为是由其组成部分，蛋白质，DNA，RNA和小分子之间的相互作用产生的，它们形成了一个复杂的分子网络。由于生物学研究和计算的最新进展，长期以来对细胞现象进行全球视角的努力似乎正在见证一个转折点。然而，即使在简单的模式生物中，如E。大肠杆菌和酵母菌。该项目从蛋白质-蛋白质相互作用和代谢的典型细胞网络的大量分析中寻求富有成效的见解，并启动了最初由同一研究小组在复杂网络的更一般背景下开发的新的统计分析。一个初步的观察是，在节点连接的各种细胞网络的相关性是非常独特的相比，在其他非生物系统中看到的，这反应的内在组织原则的功能网络的形成。生物网络中的分形尺度和拓扑自相似性将为我们对生物复杂性的认识提供前所未有的视角。当这些网络在不同尺度下观察时，它们一致地显示出具有有限分维的分形自我复制模式，这对网络的结构稳定性和生长机制有直接的影响。特别是，规模转换与进化过程的潜在相关性现在开始受到重视，其中进化路径模仿网络的增长。然而，目前尚不清楚生物网络的新兴拓扑性质是如何在漫长的进化历史中实现的，以及它与网络的容错水平如何相关，这将是研究的重点。该项目的智力价值源于基于自然界中广泛存在的自相似结构的统计分析的通用定量方法。此外，生物网络分形的新方案将直接应用于生物学、社会学和计算机科学等新兴交叉领域。同时，一旦积累了足够的证据支持新方案，它将为评估现有网络数据的保真度提供一个独特的工具。更广泛的影响：该项目的主要影响包括课程开发和代表性不足的少数民族学生参与科学。该项目将开发一门关于生物学和其他学科复杂网络的跨学科课程，该课程将与研究计划紧密结合。

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.