Mathematical Frameworks for Biological Modular Networks

这个项目的目标是了解生物网络的进化原理，通过这些原理，生物网络获得了它们的模块化结构、健壮性和维持生命的各种功能。虽然我们对自然选择的一般规律已经知道很多，但有必要对网络的大规模属性进行分析，以揭示通过进化而出现的其他相关模式。从蛋白质-蛋白质相互作用网络到代谢途径，在理论水平上描述生物学系统的困难阻碍了我们对生物网络的理解。该项目的目标是用来自非平衡系统和复杂性的新概念来解决生物复杂网络中的紧急现象这一主题，并将这些理论概念系统地转化为实用的功能模块模型。为了实现这一目标，PI将通过聚类和重整化方法研究功能模块(为实现给定功能而协作的蛋白质簇)。这个项目的智力价值在于基于自相似测量的独特定量方法，开发该方法是为了阐明在组成分子网络水平上体现的进化原理。这一新的数学方案将在生物学、社会学和计算机科学交界处的新兴交叉学科领域找到广泛的适用性。该项目的更广泛影响包括课程开发、代表人数不足的少数族裔学生的参与以及国际和跨学科合作。促进少数族裔本科生和研究生参与研究环境将是该计划的优先事项。该项目还将汇集复杂性和统计物理学、生物信息学和生物学方面的国际领先小组。

The objective of this project is to understand evolutionary principles by which biological networks have acquired their modular architecture, robustness and preservation of diverse functions to sustain life. While much is known about the general rules of natural selection, an analysis of the large-scale properties of networks is necessary to reveal other relevant patterns that emerge through evolution. The difficulty in describing systems in biology, from protein-protein interaction networks to metabolic pathways, on a theoretical level has hampered the development of our understanding of biological networks. The goal of this project is to tackle the theme of emergent phenomena in biological complex networks with novel concepts from non-equilibrium systems and complexity, and to systematically transform these theoretical concepts into practically applicable models of functional modules. In order to achieve this goal, the PI will study functional modules (clusters of proteins cooperating towards achieving a given function) by clustering and renormalization methods. The intellectual merit of this project rests on the unique quantitative methodology, based on the self-similarity measure, developed to elucidate the evolutionary principles embodied at the level of networks of constituent molecules. This novel mathematical scheme will find wide applicability in the newly rising interdisciplinary fields at the interfaces of biology, sociology, and computer sciences. The broader impacts of this project include curriculum development, involvement of underrepresented minority students, and international and interdisciplinary collaborations. Promoting the involvement of minority undergraduate and graduate students in research environments will be a priority of this program. The project will also bring together international leading groups in Complexity and Statistical Physics, Bioinformatics, and Biology.