Amorphous computation, random graphs and complex biological networks

非晶计算、随机图和复杂生物网络

基本信息

批准号：
EP/D00232X/1
负责人：
Netta Cohen
金额：
$ 78.09万
依托单位：
University of Leeds
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2006
资助国家：
英国
起止时间：
2006 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FD00232X%2F1
关键词：
Amorphous computation random graphs complex

项目摘要

In this ``information age'', computation, communication and massive information handling have become the bread and butter of modern society. Internet networks, the web, and popular peer-to-peer networks are all examples of the transition we are witnessing from local, centralised computers to massive distributed networks of relatively low-power individual resources. These are our first glimpses of the amorphous computers of the future. More generally, amorphous computers include any large-scale network of computational units or processes that are connected through a flexible and constantly changing network of interactions. These may be swarms of microscopic robots or large sensor-arrays that monitor climate or pollution. The critically important feature common to these kinds of self-organising distributed systems is that the desired computation emerges and is not explicitly preprogrammed.The transition to amorphous computing brings with it enormous potential as well as risk (such as the virus epidemics that plague the internet). To exploit the advantages and avoid the dangers of amorphous computing, fundamentally new ways of coping with complexity are needed. To do so we plan to develop appropriate mathematical models and tools, on the one hand, and to derive appropriate engineering principles inspired by successful systems, on the other.One of the unifying features of amorphous computers is their active network structure. Thus, a natural mathematical entity for their description is the graph: a structure with nodes (processors) and edges (connections). Since by their very nature, the network structure of amorphous computers is non-prescribed, the study of random graphs is especially promising. To extend the theory of random graphs to real-world applications, new mathematics needs to be developed, including new families of random graphs, new tools for simulating their growth and dynamics and new methods for analysing the dynamics that takes place on these graphs. A key part of this proposal is the development of these tools and their application to specific models of amorphous computers, and ultimately to real systems (such as P2P networks and sensor arrays).One of the challenges of amorphous computing is to find useful analogies that provide insight into the requirements, capabilities and limitations of the systems at hand. In this proposal, we will draw inspiration from biological systems and the powerful computation they perform. Computational aspects of biological functions are found in almost any task: from evolution, though development, to information processing, and are evident on every level of organisation, including macro-molecules (e.g., protein folding), cells (e.g., regulatory networks of proteins and genes) and higher (neural networks and nervous systems). Built of microscopic, noisy and relatively unreliable components, biological systems are surprisingly effective and efficient. Unlike human-engineered computers, they are also dynamic and highly adaptive machines. They are typically distributed and decentralised, with each component following a set of local rules based on its environment to determine its actions. It is the emergence of a functional and coherent whole from an ensemble of simple and unreliable elements that we would like to capture for our own engineering purposes.

在这个``信息时代''中，计算，沟通和大量信息处理已成为现代社会的面包和黄油。互联网网络，网络和流行的点对点网络都是我们目睹的过渡，从本地，集中计算机到相对低功耗的个体资源的大规模分布式网络。这些是我们对未来无定形计算机的首次瞥见。更一般而言，无定形计算机包括通过灵活且不断变化的交互网络连接的任何计算单元或过程网络。这些可能是监测气候或污染的大型机器人或大型传感器阵列。这些类型的自组织分布式系统常见的至关重要的特征是，所需的计算出现并且没有明确预编程。向无定形计算的过渡带来了巨大的潜力和风险（例如，互联网上的病毒流行病）。为了利用优势并避免无定形计算的危险，需要从根本上应对复杂性的新方法。为此，我们计划一方面开发适当的数学模型和工具，并在另一方面得出适当的工程原理，另一方面是由成功系统启发的适当工程原理。无定形计算机的统一功能之一是它们的主动网络结构。因此，其描述的自然数学实体是图形：带有节点（处理器）和边缘（连接）的结构。由于其本质，非晶计算机的网络结构是非规定的，因此随机图的研究尤其有前途。为了将随机图的理论扩展到现实世界的应用程序，需要开发新的数学，包括随机图的新家族，用于模拟其增长和动态的新工具以及用于分析这些图表上发生动态的新方法。该提案的关键部分是开发这些工具及其在特定模型的无定形计算机模型中的应用，最终对真实系统（例如P2P网络和传感器阵列）。无定形计算的挑战之一是找到有用的类比，可以洞悉手头系统的需求，功能和限制。在此提案中，我们将从生物系统及其执行强大的计算中汲取灵感。在几乎任何任务中都发现了生物学功能的计算方面：从进化，虽然开发到信息处理，并且在组织的每个级别上都很明显，包括宏观分子（例如蛋白质折叠），细胞（例如蛋白质和基因的调节网络）和较高的（神经网络和神经网络和神经系统）。生物系统由显微镜，嘈杂和相对不可靠的组件建立，令人惊讶地有效。与人工设计的计算机不同，它们也是动态的和高度适应性的机器。它们通常是分布和分散的，每个组件遵循基于其环境的一组本地规则以确定其行动。这是我们想出于自己的工程目的捕获的简单和不可靠的元素的合奏的功能和连贯的整体。