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

EIA-0205061Ray，Animesh Keck研究生院标题：使用生物和计算原理分析、建模和设计稳健、复杂网络的双框架自然网络(如互联网、供电系统或细胞的基因调控电路)对意外或故意攻击的脆弱性是一个重要的研究领域。到目前为止，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进行操作。调控发芽酵母细胞核中RNA分子合成的复杂分子机制是一个真实世界的自然网络，可以通过特定的遗传操作在实验上进行扰动，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。这个项目的目标是开发一个生物驱动的网络健壮性计算框架。为了实现这一目标，采用了一个足够复杂的生物试验台--酵母产孢子基因调控网络作为实验网络模型。系统性基因敲除突变(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。这些扰动对基因调控网络结构的影响正在全基因组转录图谱的水平上进行分析。一个用于大规模网络的通用数据模型正在开发中。利用该数据模型对生物网络的相关实验行为进行了仿真。通过数学建模和测试，正在探索通过进化选择的复杂网络体系结构的基本设计原则。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具有价值，尤其对通信技术、灾难响应以及设计可抵御计划攻击的强大通信基础设施具有重要意义。

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.