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Details of Grant 

EPSRC Reference: EP/D501377/1
Title: A Synergistic Integration of Natural and Artificial Immunology for the Prediction of Hierarchical Protein Functions
Principal Investigator: Freitas, Professor A
Other Investigators:
Timmis, Professor J Flower, Dr D
Researcher Co-Investigators:
Project Partners:
Department: Sch of Computing
Organisation: University of Kent
Scheme: Standard Research (Pre-FEC)
Starts: 01 February 2006 Ends: 30 November 2008 Value (£): 434,120
EPSRC Research Topic Classifications:
New & Emerging Comp. Paradigms Theoretical biology
EPSRC Industrial Sector Classifications:
Pharmaceuticals and Biotechnology
Related Grants:
Panel History:  
Summary on Grant Application Form
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.
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Organisation Website: http://www.kent.ac.uk