CAA: Self-organization and Robustness in Evolving Biological Networks

生物系统的健壮性被认为来自于其组成成分、蛋白质、DNA、RNA和小分子之间的相互作用，它们形成了一个复杂的分子网络。由于最近在生物研究和计算方面的进展，长期以来对细胞现象的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，如大肠杆菌和酵母，也有大量的信息阻碍了阐明设计原则的任务。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更一般的复杂网络背景下开发的新颖统计分析。初步观察发现，与其他非生物系统相比，各种细胞网络中节点连接的相关性是非常独特的，这反映了功能网络形成的固有组织原则。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看法提供前所未有的视角。在不同尺度下观察这些网络时，它们一致表现出有限维分形的自复制模式，这直接影响了网络的结构稳定性和生长机制。特别是，尺度转换与进化过程的潜在关联现在开始受到重视，其中进化途径模仿了网络的增长。然而，生物网络的新兴拓扑特性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之间的关系，这些都是目前尚不清楚的，这将是今后研究的重点。该项目的智力价值源于基于对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，生物网络中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跨学科领域。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少数民族学生参与科学。该项目将开发一门跨学科课程，研究生物学和其他学科的复杂网络，这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.