Bacteria
Picture by IBM Research

Research expertise

Dr Colin Johnson, Dr Alex Freitas and Dr Dominique Chu hold these subjects as their areas of expertise:

  • Bio-inspired computing – genetic algorithms, swarm intelligence, artificial neural networks
  • Systems Biology and Bioinformatics
  • Theory and applications of information visualisation
  • Data mining and knowledge discovery
  • Salience sensitive control
  • Attention, affect and addiction

Areas of interest

Dr Colin Johnson is also interested in ‘Computing and Mathematics in Medicine and Biology’ and ‘Natural Science as Metaphor’

Cross-disciplinary research and projects

Stochastic models of gene expression by Dr Dominique Chu

This project aims to understand how noise impacts gene expression. A focus of this project so far has been to understand how the “computational” properties of genes and gene networks are affected by stochastic fluctuations.

Understanding the role of fimbriation in E.coli by Dr Dominique Chu

This project is in collaboration with the Blomfield group. The main aim of this project is to develop computational and mathematical models of the genetic regulation of type-I fimbriae in E. coli.

A Synergistic Integration of Natural and Artificial Immunology for the Prediction of Hierarchical Protein Function by Dr Alex Freitas

Funded By:   Engineering and Physical Sciences Research Council (Ref. No. EP/D501377/1)
Period:          01/02/2006 to 31/07/2008
A Synergistic Integration of Natural and Artificial Immunology for the Prediction of Hierarchical Protein Function.
In collaboration with 3 institutions: University of Kent, University of York and Edward Jenner Institute for Vaccine Research.

Bio-inspired Classification and Data Mining Algorithms for Bioinformatics by Dr Colin Johnson and Dr Alex Freitas

Funded By:    Interreg IIIA (European Regional Development Fund) (Ref. No. 162/025/361)
Period:           2005 – 2009
In collaboration with 2 institutions: Universite du Littoral Cote d’Opale (ULCO), France, and University of Kent.

Publications

Dominique Chu, Chu. Shih-Chi, and Mostafa Barigou. Qualitative models of particle de-agglomeration. Powder Technology, 195:6, July 2009.

Dominique Chu, Nicolae Radu Zabet. Models of transcription factor binding: Sensitivity of activation functions to model assumptions. Boris Mitavskiy Journal of Theoretical Biology, 257(3):419-429, April 2009.

D. Chu. Modes of evolution in a parasite-host interaction: Dis-entangling factors determining the evolution of regulated fimbriation in e. coli. Biosystems, 95(1):67-74, December 2008.

D Chu, J Roobol, and I C Blomfield. A Theoretical Interpretation of the Transient Sialic Acid Toxicity of a nanR Mutant of Escherichia coli. Journal of Molecular Biology, 375:875-889, January 2008.

D. Chu. The evolution of group-level pathogenic traits. Journal of Theoretical Biology, 253(2):355-362, January 2008.

D Chu and I C Blomfield. Orientational Control is an Efficient Control Mechanism for Phase Switching in the E coli fim System. Journal of Theoretical Biology, 244(3):541-551, January 2007.

D. Chu, J. Rowe, and H-C. Lee. Evaluation of the current models for the evolution of bacterial DNA uptake signal sequences. Journal of Theoretical Biology, 238(1):157-166, January 2006.

A.A. Freitas and A. C.P.L.F. de Carvalho. A Tutorial on Hierarchical Classification with Applications in Bioinformatics. In: D. Taniar (Ed.) Research and Trends in Data Mining Technologies and Applications, pp. 175-208. Idea Group, 2007.

F. Otero, M. Segond, A.A. Freitas, C.G. Johnson, D. Robilliard, C. Fonlupt. An empirical evaluation of the effectiveness of different types of predictor attributes in protein function prediction. In: A. Abraham, A.-E. Hassanien, V. Snael (Eds.) Foundations of Computational Intelligence
– Vol 5. Studies in Computational Intelligence 205, pp. 339-357. Springer, 2009.

M. Iqbal, A.A. Freitas and C.G. Johnson. A hybrid rule-induction/likelihood-ratio based approach for predicting protein-protein interactions. In: C.L. Mumford and L.C. Jain (Eds.) Computational Intelligence: collaboration, fusion and emergence, pp. 623-637. Intelligent Systems Reference Library, Vol. 1. Springer, 2009.

M.N. Davies, A. Secker, A.A. Freitas, M. Mendao, J. Timmis and D.R. Flower. On the hierarchical classification of G Protein-Coupled Receptors. Bioinformatics Vol. 23, No. 23, 1 December 2007, pp. 3113-3118.

E.S. Correa, A.A. Freitas and C.G. Johnson. Particle swarm for attribute selection in Bayesian classification: an application to protein function prediction. Journal of Artificial Evolution and Applications –special issue on Particle Swarms: The Second Decade, Vol. 2008, Article ID 876746, 12 pages.

E.P. Costa, A.C. Lorena, A.C.P.L.F. Carvalho, A.A. Freitas and H. Holden. Comparing several approaches for hierarchical classification of proteins with decision trees. In: M.-F. Sagot and M.E.M.T. Walter (Eds.) Advances in Bioinformatics and Computational Biology, Lecture Notes in Bioinformatics 4643, pp. 126-137, Springer, 2007.

M. Iqbal, A.A. Freitas, C.G. Johnson and M. Vergassola. Message-passing algorithms for the prediction of protein domain interactions from protein-protein interaction data. Bioinformatics Vol. 24, No. 18, 15 September 2008, pp. 2064-2070.

M.N. Davies, A. Secker, A.A. Freitas, E. Clark, J. Timmis and D.R. Flower. Optimizing amino acid groupings for GPCR classification. Bioinformatics Vol. 24, No. 18, 15 September 2008, pp. 1980-1986.

M.N. Davies, A. Secker, M. Halling-Brown, D.S. Moss, A.A. Freitas, J. Timmis, E. Clark and D.R. Flower. GPCRTree: online hierarchical classification of GPCR function. BMC Research Notes 2008, 1:67 (21 August 2008). 5 pages.

M.N. Davies, A. Secker, A.A. Freitas, J. Timmis, E. Clark and D.R. Flower. Alignment-independent techniques for protein classification. Current Proteomics, Vol. 5, No. 4, Dec. 2008, pp. 217-223

N. Holden and A.A. Freitas. Hierarchical classification of protein function with ensembles of rules and particle swarm optimisation. Soft Computing – special issue on Evolutionary and Metaheuristic-based Data Mining, Vol. 13, No. 3, Feb. 2009, pp. 259-272.

G.L. Pappa and A.A. Freitas. Automatically evolving rule induction algorithms tailored to the prediction of postsynaptic activity in proteins. Intelligent Data Analysis, Vol. 13, No. 2, 2009, pp. 243-259.

A. Secker, M.N. Davies, A.A. Freitas, J. Timmis, E. Clark and D.R. Flower. An artificial immune system for clustering amino acids in the context of protein function classification. J. Mathematical Modelling and Algorithms, Vol. 8, June 2009, Special Issue on Artificial Immune Systems, pp. 103-123.

N. Holden and A.A. Freitas. Improving the Performance of Hierarchical Classification with Swarm Intelligence. In: E. Marchiori and J.H. Moore (Eds.) Proc. Sixth European Conf. on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBio-2008), Lecture Notes in Computer Science 4973, pp. 48-60. Springer, 2008. ISBN-10: 3-540-78756-9. ISBN-13: 978-3-540-78756-3.

M. Iqbal, A.A. Freitas and C.G. Johnson. Protein Interaction Inference using Particle Swarm Optimization Algorithm. In: E. Marchiori and J.H. Moore (Eds.) Proc. Sixth European Conf. on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBio-2008), Lecture Notes in Computer Science 4973, pp. 61-70. Springer, 2008.

E.P. Costa, A.C. Lorena, A.C.P.L.F. Carvalho and A.A. Freitas. Top-down hierarchical ensembles of classifiers for predicting G-Protein-Coupled-Receptor functions. In: A.L.C. Bazzan, M. Craven and N.F. Martins (Eds.), Advances in Bioinformatics and Computational Biology, Lecture Notes in Bioinformatics 5167, pp. 35-46. Springer, 2008.

R.T. Alves, M.R. Delgado and A.A. Freitas. Multi-label hierarchical classification of protein functions with artificial immune systems. In: A.L.C. Bazzan, M. Craven and N.F. Martins (Eds.), Advances in Bioinformatics and Computational Biology,, Lecture Notes in Bioinformatics 5167, pp. 1-12. Springer, 2008.

F.E.B. Otero, A.A. Freitas and C.G. Johnson. A hierarchical classification ant colony algorithm for predicting gene ontology terms. In: C. Pizzuti, M.D. Ritchie and M. Giacobini (Eds.) Proc. 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBio-2009), Lecture Notes in Computer Science 5483, pp.68-79.

Colin G. Johnson, Jacki P. Goldman, and William J. Gullick.S imulating complex intracellular processes using object-oriented computational modelling. Progress in Biophysics and Molecular Biology, 86(3):379-406, November 2004.

Jacki P. Goldman, William J. Gullick, and Colin G. Johnson. Individual-based simulation of the clustering behaviour of epidermal growth factor receptors. Scientific Programming, 12(1):25-43, January 2004.