CAREER: Self-Organizing Systems: Engineering and Understanding Robust Collective Behavior

本研究探讨自组织系统：（1）如何设计自我修复的分布式计算系统，（2）如何理解多细胞生物学中的鲁棒性。目标是提供对多细胞生物系统中鲁棒性和适应性的基本原理的基本理解，并演示如何将这些原理应用于计算系统。计算机科学的许多领域严重依赖于从并行基底轻松编程鲁棒集体行为的能力，从传统的计算机网络到模块化机器人。这项研究将显著提高我们设计自组织、自愈和适应的计算系统的能力。这项研究还将通过提供组织发育和疾病的新见解来影响生物学。这项研究有两个主要目标：（1）使用来自多细胞器官（例如心脏）中的自我维持和自我修复行为的见解来设计用于嵌入式分布式系统（例如无线传感器网络）的算法（2）基于实验数据开发细胞水平行为的计算模型，可以预测上皮组织中新的系统级特性。这些努力将有助于更深入地了解如何从大量相同编程的不可靠代理的分散交互中实现全球鲁棒性。

This research investigates self-organizing systems: (1) how to engineer self-repairing distributed computing systems, (2) how to understand robustness in multicellular biology. The goal is to providea fundamental understanding of the basic principles underlying robustness and adaptability in multicellular biological systems, and demonstrate how these principles can be applied to computing systems.Many areas in computer science critically depend on the ability to easily program robust collective behavior from massively-parallel substrates, from traditional computer networks to modular robotics.This research will significantly improve our ability to design computing systems that self-organize, self-heal, and adapt. This research will also impact biology by providing novel insights intotissue development and disease.This research has two main thrusts: (1) using insights from self-maintaining and self-healing behavior in multicellular organs, such as the heart, to design algorithms for embedded distributed systems, such as wireless sensor networks (2) developing computational models of cell level behavior, based on experimental data, that can predict novel system-level properties in epithelial tissues. These efforts will contribute to a deeper understanding of how global robustness is achieved from the decentralized interactions of largenumbers of identically-programmed, unreliable agents.