Infobiotics
Infobiotics is the synergy of executable biology,
evolutionary and machine learning methods, mesoscopic simulation
techniques and experimental data for a more principled practice
of origins of life, bioinformatics, computational systems and
synthetic biology research.
As part of our infobiotics efforts we have developed a series
of structural bioinformatics servers:
ProCKSI is a
decision support system for protein structure comparison that
computes structural similarities using a variety of measures to
produce a consensus. It contains tools for visualising, analysing
and easily comparing all results, linking to external resources
for further information and literature about protein structures.
These services are part of a greater investigation of a suitable
framework/architecture for very large scale protein structure
comparison, clustering and analysis in parallel and distributed
environments, involving the evaluation and selection of, optimal
middleware software, database model, programming libraries, tools
and algorithms.
PSP server contains a
collection of web services that predict Protein Structure Prediction (PSP) sub-problems such as
coordination number, solvent accessibility or recursive convex
hull using Learning Classifier Systems. These subproblems are
structural features of protein residues that contain information
about the end product of the folding process. These features are
related to the density of packing of different parts of a protein
or how buried/exposed, far/close to the surface are different
residues within a protein.
The Infobiotics PSP benchmarks repository
contains an adjustable real-world family of benchmarks suitable for testing the scalability of
classification/regression methods. When we test a machine learning method we usually choose a test suite containing
datasets with a broad set of characteristics, as we are interested in knowing how the learning method reacts to
a veriety of scenarios. The PSP field provides us with a whole family of real-world classification/regression
problems that can be adjusted almost arbitrarily in terms of number of variables, number of classes, class balance,
etc. Thus, these datasets are an ideal benchmark suite for data mining methods.
The ultimate goal of systems biology is the development of
executable in silico models of cells and organisms, while
synthetic biology aims to implement, in vitro/vivo,
organisms whose behaviour is engineered. P systems,
computing with membranes, abstract the structure and function of
the living cell into a formalism upon which we are building a
multi-scale modelling environment. By applying discrete,
numerical simulation algorithms to P system models of
quorum sensing in the
bacterial pathogen Pseudomonas aeruginosa and
root development in Arabidopsis thaliana,
we aim to understand the stochastic processes
governing these model organisms, and guide laboratory
experiments. In addition we use evolutionary optimisation and
machine learning (Genetic Programming, Learning Classifier Systems,
Support Vector Machines, etc) to estimate parameters and discover
structures that match observed behaviour in cellular networks
with the intention of isolating these modules for use in the design
of synthetic organisms. Dissipative Particle Dynamics is used for simulating
(proto-)membranes.
Recent Publications
Systems/Synthetic Biology:
Romero-Campero FJ, Twycross J, Bennett M , Camara M, Krasnogor N. "Modular Assembly of Cell Systems Biology Models Using P Systems" [paper] presented at Prague International Workshop on Membrane Computing 2nd June 2008, Prague, Czech Republic.
Romero-Campero FJ, Blakes J, Camara M, Krasnogor N. "A systems analysis of the AHL Quorum Sensing system in Pseudomonas aeruginosa" [poster] presented at ESF-UB Conference in Biomedicine - Systems Biology 12-17 April 2008, Sant Feliu de Guixols, Spain.
Twycross J, Ubeda-Tomas S, Kramer E, Bennett M, Krasnogor N. "A Tissue-level Model of Auxin Transport in the Arabidopsis thaliana Root" [poster] presented at ESF-UB Conference in Biomedicine - Systems Biology 12-17 April 2008, Sant Feliu de Guixols, Spain.
Romero-Campero FJ, Blakes J, Cao H, Camara M, Krasnogor N. "A modular and stochastic approach to the study of gene circuits using P systems" [poster] presented at Genomes to Systems 2008 Manchester, UK.
Smaldon J, Krasnogor N. "A New Method for Prototyping Systems Biology Designs with P systems" [poster] presented at Genomes to Systems 2008 Manchester, UK.
Smaldon J, Blakes J, Lancet D, Krasnogor N. "A Multi-scaled Approach to Artificial Life Simulation With P Systems and Dissipative Particle Dynamics" [paper] accepted for GECCO 2008 Atlanta, USA.
Romero-Campero FJ, Cao H, Camara M, Krasnogor N. "Structure and Parameter Estimation for Cell Systems Biology Models" [paper] accepted for GECCO 2008 Atlanta, USA.
Romero-Campero FJ, Blakes J, Camara M, Willams P, Perez-Jimenez MJ, Krasnogor N. "Formal informatics and machine learning for more principled systems and synthetic biology" [poster] presented at European Conference on Synthetic Biology 2007 Sant Feliu de Guixols, Spain.
Protein Structure Comparison:
Protein Structure Prediction:
Stout M, Bacardit J, Hirst J D, Krasnogor N. "Prediction of Recursive Convex Hull Class Assignments for Protein Residues" [paper] in Bioinformatics 2008 24(7): 916-923.
Bacardit J, Stout M, Hirst J D, Krasnogor N. "Data Mining in Proteomics with Learning Classifier Systems" paper in Bull L, Bernado Mansilla E, Holmes J. (eds) Learning Classifier Systems in Data Mining 2008 Springer in press.
Stout M, Bacardit J, Hirst J D, Smith R E, Krasnogor, N. "Prediction of Topological Contacts in Proteins Using Learning Classifier Systems" paper in Special Issue on Evolutionary and Metaheuristic-based Data Mining Soft Computing Journal in press.
Bacardit J, Stout M, Hirst J D, Sastry K, Llor X, Krasnogor N. "Automated Alphabet Reduction Method with Evolutionary Algorithms for Protein Structure Prediction" [paper] in Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation (GECCO2007) 2007 346-353 ACM Press.
Bacardit J, Stout M, Hirst J D, Krasnogor N, Blazewicz J. "Coordination number prediction using Learning Classifier Systems: Performance and interpretability" [paper] in Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation (GECCO2006) 2006 247-254 ACM Press.
