A Synergistic Integration of Natural and Artificial Immunology for the Prediction of Hierarchical Protein Functions

自然免疫学和人工免疫学的协同整合用于预测分层蛋白质功能

• 批准号: EP/D501377/1
• 负责人: Alex Freitas
• 金额: $ 55.32万
• 依托单位: University of Kent
• 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%2FD501377%2F1
• 关键词: Synergistic; Integration; Natural; Artificial; Immunology

At present biologists are producing very large amounts of data about genes, as a result of a number of automated experiments. A large part of this data refers to proteins, which are the products made by genes. That is, one can think of the genome (the entire set of genes of an organism) as an, encoded text that is decoded to produce proteins. Genes are passive elements, but proteins are active elements, i.e. they perform a variety of functions which are essential to the survival of any organism. The very large amount of data about protein functions currently available is very valuable, because it can potentially lead to a better understanding and treatment of diseases, design of more effective medical drugs, etc. However, in order to harvest the potential of this large amount of data, we need to use intelligent data analysis (or data mining ) techniques that mine (analyse) the data and transform it into useful knowledge, e.g., knowledge specifying which kinds of protein functions are more related to a given kind of disease.This project is inter-disciplinary, because it integrates biology and computer science. From a biology point of view, the project will focus on predicting the functions of a very important kind of protein, which is the target for a large number of medical drugs on the market. From a computer science point of view, the general goal of the project is to automatically discover knowledge from biological data, using intelligent data mining techniques implemented in a computer. In particular, this project will use one kind of intelligent data mining technique called artificial immune systems , which are essentially computer programs that work in a way inspired by the natural immune system. The latter is actually a very sophisticated system, evolved by nature, that allows our body to identify and fight a number of pathogens and invaders. It turns out that the natural immune system is very clever in recognising a very large number of harmful body invaders and developing an appropriate immune response for each kind of invader. The immune system exihibits many interesting properties such as learning, adaptation, and memory of invaders recognised in the past (which speeds up the immune response when the same invader is encountered again). The challenge is to identify which of the many properties of the natural immune system are suitable as an inspiration to design an intelligent artificial immune system for the problem of mining protein data. In order to address this challenge, this project will involve collaboration between computer scientists and biologists. The project will develop a computational model (a kind of computer simulation ) of some properties of the natural immune system, which will allows us to better understand that complex system. This understanding will be used to develop a novel data mining computer program inspired by the natural immune system. These two developments - the computational model and the data mining program - will be done in parallel and with a lot of feedback and interaction between the corresponding research teams, leading to novel contributions to both natural immunology and computer science.

目前，生物学家通过大量的自动化实验产生了大量关于基因的数据。这些数据中有很大一部分涉及蛋白质，蛋白质是基因制造的产物。也就是说，人们可以将基因组（生物体的整套基因）视为一种编码文本，解码后产生蛋白质。基因是被动的元素，但蛋白质是主动的元素，也就是说，它们执行对任何生物体的生存至关重要的各种功能。目前可获得的关于蛋白质功能的非常大量的数据是非常有价值的，因为它可以潜在地导致更好地理解和治疗疾病，设计更有效的医疗药物等。然而，为了收获这些大量数据的潜力，我们需要使用智能数据分析。（或数据挖掘）技术，挖掘（分析）数据并将其转换为有用的知识，例如，知识指定哪种蛋白质的功能与某种疾病更相关。这个项目是跨学科的，因为它整合了生物学和计算机科学。从生物学的角度来看，该项目将专注于预测一种非常重要的蛋白质的功能，这是市场上大量医疗药物的目标。从计算机科学的角度来看，该项目的总体目标是使用计算机中实现的智能数据挖掘技术，从生物数据中自动发现知识。特别是，该项目将使用一种称为人工免疫系统的智能数据挖掘技术，它本质上是受自然免疫系统启发的计算机程序。后者实际上是一个非常复杂的系统，由自然进化而来，使我们的身体能够识别和对抗一些病原体和入侵者。事实证明，天然免疫系统非常聪明，能够识别大量有害的身体入侵者，并为每种入侵者开发适当的免疫反应。免疫系统表现出许多有趣的特性，如学习、适应和对过去识别的入侵者的记忆（当再次遇到相同的入侵者时，这会加快免疫反应）。挑战在于确定自然免疫系统的许多特性中哪些适合作为设计智能人工免疫系统的灵感，以解决挖掘蛋白质数据的问题。为了应对这一挑战，该项目将涉及计算机科学家和生物学家之间的合作。该项目将开发一个自然免疫系统某些特性的计算模型（一种计算机模拟），这将使我们能够更好地理解这个复杂的系统。这种理解将被用来开发一种受自然免疫系统启发的新型数据挖掘计算机程序。这两个发展-计算模型和数据挖掘程序-将并行进行，并在相应的研究团队之间进行大量的反馈和互动，从而为自然免疫学和计算机科学做出新的贡献。

项目成果

An interdisciplinary perspective on artificial immune systems

• DOI: 10.1007/s12065-007-0004-2
• 发表时间: 2008-01
• 期刊: Evolutionary Intelligence
• 影响因子: 2.6
• 作者: J. Timmis;P. Andrews;Nick D. L. Owens;Edward Clark

GPCRTree: online hierarchical classification of GPCR function.

• DOI: 10.1186/1756-0500-1-67
• 发表时间: 2008-08-21
• 期刊: BMC research notes
• 影响因子: 1.8
• 作者: Davies MN;Secker A;Halling-Brown M;Moss DS;Freitas AA;Timmis J;Clark E;Flower DR
• 通讯作者: Flower DR

Alignment-Independent Techniques for Protein Classification

• DOI: 10.2174/157016408786733770
• 发表时间: 2008-12-01
• 期刊: Current Proteomics
• 影响因子: 0.8
• 作者: Davies, Matthew N.;Secker, Andrew;Flower, Darren R.
• 通讯作者: Flower, Darren R.