COSMIC-Rules is a novel rule-based computing system of microbial interactions and communications. It simulates evolutionary processes within populations of virtual bacteria. The model incorporates three levels: the bacterial genome, the bacterial cell and an environment inhabited by such cells. The virtual environment in COSMIC-Rules can contain multiple substances, whose relative toxicity or nutrient status is specified by the genome of the bacteria. Each substance may be distributed uniformly or in a user-defined manner. The organisms in COSMIC-Rules possess individually-defined physical locations, size, cell division status and genomes. Genes and/or gene systems are represented by abstractions that can subsume otherwise complex phenotypes. Central to COSMIC-Rules is a simplified representation of bacterial species, each containing a functional genome and including, where desired, extrachromosomal elements such as plasmids, bacteriophages and/or transposible elmements. The computer representation is based on bit string matching and is a widely applicable model of biological recognition systems. This representation permits, for example, the modelling of protein-protein interactions, receptor-ligand interactions and DNA-DNA transactions. COSMIC-Rules is intended to inform studies on bacterial adaptation and evolution, and to predict behaviour of populations of pathogenic bacteria and their viruses. The framework is constructed for parallel execution across a large number of machines and efficiently utilises a 64 processor development cluster. It will run on any Grid system with the minimum of requirements and has successfully tested simulations with millions of bacteria, of multiple species and utilising multiple substrates. The model may be used for large-scale simulations where a genealogical record for individual organisms is required or considered useful.
COSMIC (COSMIC Homepage) is the precursor to COSMIC-Rules, sharing many of the features and with added details. COSMIC uses a less abstract genotype to phenotype mapping, with individual gene transcription leading to individual proteins. In effect, it created spatially organised populations of gene products within each cell, inside a population of cells distributed hetrogeniously over the environment. The cost of this added complexity was computation time, simulating hundreds rather than millions of cells was practical. However, from the biologists point of view, such a small population size is of little use. As a result, this project defined a complexity/realism cut-off point that we know to avoid for many years - until modern, simple, computers can match the performance of living systems as simple as a single cell.
Besides the above biological projects, I am knowledgable on the subjects of software development, Linux (configuration, admin and customisation to the point of writing kernel modules), TCP/IP networking , electronic design, Wifi security and VoIP (mostly Asterisk and SIP). As a result, quite a few large and small projects have been documented over the years.
Of special interest is sshdfilter, a heuristic blocker of ssh brute force attacks.
The original (and very dated) home page lists some personal projects and personal thoughts from many years ago, when it was the fashion to have a web page.
Gregory, R., Saunders, J.R., and Saunders, V.A., 2010. Rule-based simulation of temperate bacteriophage infection: restriction-modification as a limiter to infection in bacterial populations. BioSystems, 100, 166-177.
Gregory, R., Saunders, J.R., and Saunders, V.A., 2008. Rule-based modelling of conjugative plasmid transfer and incompatibility. BioSystems. 91, 201-215.
Gregory, R., Saunders, V.A., Saunders, J.R., 2007. Rule-based Computing System of Microbial Interactions and Communications : Evolution in virtual bacterial populations. BioSystems, 91, 216-230.
Gregory, R., Saunders, J.R. and Saunders V.A., (2006). The Paton Individual Based Model legacy. Biosystems, 85, 45-54.
Gregory, R., Vlachos, C., Paton, R.C., Palmer, J.W., Wu, Q.H. and Saunders, J.R., (2004). Computing Bacterial Evolvability using Individual-based Models. In Molecular Computational Models: Unconventional Approaches. Gheorghe, M. (Ed.). Idea Group, Hershey, USA.
Paton, R.C., Gregory, R., Vlachos, C., Palmer, J.W., Saunders, J.R., Wu, Q.H. (2004). Evolvable Social Agents for Bacterial Systems Modelling. IEEE Nano Biosciences. 39 p.
Gregory, R., Paton, R.C., Saunders, J.R. and Wu, Q.H., (2004). A model of bacterial adaptability based on multiple scales of interaction, in Paton, R., Bolouri, H., Holcombe, M., Parish, J. H. and Tateson, R., (Eds.) Computation in Cells and Tissues Perspectives and Tools of Thought, Series in Natural Computing, Springer: Heidelberg.
R., Paton, R.C.,
Saunders, J.R. and Wu, Q.H., (2004). Parallelising a model of bacterial
interaction and evolution, Biosystems, 76, pp.121-131.
Gregory, R. (2004). COSMIC: A Model of Cellular Genetic Interaction and Evolution. Ph.D thesis. The University of Liverpool. 245 p.
Vlachos, C., Gregory, R., Paton, R.C., Saunders, J.R., Wu, Q.H. (2003). Indivual-Based Modelling of Bacterial Ecologies and Evolution. Comparative & Functional Genomics. Vol. 5, 1, pp. 100-104.
Gregory, R. (2002) "An Individual Based Model for Simulating Bacterial Evolution", accepted contribution to Evolvability and Individuality Workshop, University of Hertfordshire, 18-20 September 2002
Gregory, R. Paton, R.C., Wu, Q.H. & Saunders, J.R. (2001), "Computing Microbial Interactions and Communications in Real Life", paper presented to 4th International Conference on Information Processing in Cells and Tissues (IPCAT), Leuven, August.
Gregory, R. (2001) "Computing Microbial Interactions and Communications", Invited talk to Department of Microbiology, University of Wales, Cardiff, July.