A motif finding algorithm using an iterative function system and cluster-space analysis.

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The role of discrete detachment events (sloughing) in the reproducibility of biofilm dynamics.

The presentation focuses on the work in progress for a method to detect the occurrence sloughing events from biofilm growth curves. The goal is to extract growth information in order to further characterize the dynamics of growth of phototrophic biofilms but also the phenomenon of sloughing. The method should work by uncoupling biomass net growth from discrete sloughing and is first used on simulated growth curves obtained from a 2-D model that is capable of simulating the occurrence of sloughing events in an implicit way. The performance of the sloughing detection method can be assessed from this, since the exact time and size of the simulated sloughing events is known. Following, a large dataset of growth curves for phototrophic biofilms cultivated in incubators in the scope of the PHOBIA project will be analyzed.

PHOBIA - of microorganisms and models, data and integration.

In the recent years, surface-attached aggregations of microorganism, so called biofilms, have become a hot topic in life sciences: on the applied side, they flare novel drug-development and biotech approaches, whereas theoreticians take interest in the evolutionary processes they reveal. While mainstream research is focussed on pathogenic biofilms (e.g., caries), the EC-project PHOBIA deals with aquatic phototrophic biofilms: mixed photo-/heterotrophic microbial communities growing on submerged surfaces exposed to sunlight.

In this talk, I will report on the computational approaches taken to integrate, analyze and model the entirety of heterogeneous data, collected by 5 laboratories all over Europe, all using different tools, but identical incubators, protocols and biofilm-specimens. In the center of information processing is a novel type of database, tagged S3DB, which enables simple and efficient data storage, independent on the specific data structure and the assistance of a particular domain expert. Based on the diverse repositories in S3DB4PHOBIA, data-driven (black-box) modelling is performed, using Artificial Neural Networks for variable selection and predictive purposes. In addition, analyzing biofilm growthcurves also triggers top-down approaches, attempting to model biofilm growth and heterogeneity by stochastic processes with light-dependent birth, death and immigration rates.

Analysis of antigen receptor diversity estimation based on microarray technology.

Recently, a promising technique to measure lymphocyte diversity in vivo was developed by a group at the Mayo Clinic, USA. However this technique lacks a statistical analysis of the error associated with the estimates of diversity. Together with this group, we have been developing a mathematical/statistical description of the processes involved in the determination of diversity. So far, we have not been able to fully describe how diversity influences the hybridization behaviour, which is essential to fully understand the errors associated with the estimates. I would like to discuss with you our current models and probe the cunning minds of the EAO members in search for mathematical solutions to this riddle.