ITR: A Twin-Framework To Analyze, Model and Design Robust, Complex Networks Using Biological and Computational Principles

题目：利用生物学和计算原理分析、建模和设计稳健、复杂网络的双框架自然网络（如互联网、电网或细胞的基因调控回路）遭受意外或故意攻击的脆弱性是一个重要的研究领域。迄今为止，大多数工作都集中在对现有静态网络的观察或计算机模拟上，因为大多数自然网络很难通过实验来操纵。调控出芽酵母（一种单细胞生物）细胞核中RNA分子合成的复杂分子机制是一个自然网络的现实世界实例，可以通过定义的遗传操作在实验中进行扰动，并且这些扰动的结果可以通过当前的基因组技术在分子水平上以前所未有的精度进行研究。该项目的目标是为网络健壮性开发一个生物学驱动的计算框架。为了实现这一目标，我们采用了一个足够复杂的生物学实验平台——酵母产孢的基因调控网络作为实验网络模型。系统基因敲除突变（相当于节点移除）和调控位点缺失突变（相当于边缘移除）正被用作主动改变网络的工具。基因调控网络结构中这些扰动的影响正在全基因组转录谱水平上进行分析。目前正在开发用于大规模网络的通用数据模型。用数据模型模拟了生物网络的相关实验行为。通过数学建模和测试来探索通过进化选择的复杂网络架构的设计原则。从这些研究中获得的见解将对复杂网络设计中的防御策略有价值，其中包括通信技术，灾难响应以及设计抵御计划攻击的健壮通信基础设施。

EIA- 0205061Ray, Animesh Keck Graduate Institute TITLE: A Twin-Framework To Analyze, Model and Design Robust, Complex Networks Using Biological and Computational PrinciplesVulnerability of natural networks (such as the Internet, power supply grid, or gene regulatory circuits of cells) to accidental or deliberate attack is an important area of study. To date most work has focused on observations of existing static networks or on computer simulations, because most natural networks are difficult to manipulate experimentally. The complex molecular machinery regulating the synthesis of RNA molecules in the nucleus of budding yeast, a single-celled organism, is a real-world instance of a natural network that can be experimentally perturbed by defined genetic manipulations, and the results of these perturbations can be studied at the molecular level with unprecedented accuracy by current genomic techniques. The goal of this project is to develop a biology-driven computational framework for network robustness. To achieve this goal, a biological test-bed of sufficiently complexity, the gene regulatory network of sporulation in yeast, has been adopted as an experimental network model. Systematic gene knockout mutations (equivalent to node removal), and regulatory site deletion mutations (equivalent to edge removal), are being used as tools to actively alter the network. Effects of these perturbations in the gene regulatory network architecture are being analyzed at the level of whole-genome transcriptional profiles. A generic data model for large-scale networks is being developed. The relevant experimental behavior of the biological network is being emulated with the data model. Design principles underlying the architecture of complex networks selected through evolution are being probed through mathematical modeling and testing. Insights obtained from these studies will be valuable for defensive strategies in complex network design, with implications in, among others, communication technology, disaster response, and in designing robust communication infrastructure resistant to planned attacks.