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Mathematical Modelling of Microbial Adaptation and Interaction

Start date
2005
End date
2007
Objective
This project is part of the programme "Physiology and predictive ecology of foodborne pathogens". The overall aim of the project is to provide a platform for fully quantitative microbiology. We focus on the stochastic and dynamic modelling of bacterial kinetics, extending it to yet unexplored areas, and applying novel techniques.

Work on the modelling of lag time will now include molecular microbiology techniques and results, and will study microbial interactions in mixed cultures both at population and single cell level. Key objectives include:

  • Extension and development of ComBase and related software tools. This activity follows the well-established predictive microbiology approach having been successfully applied in the past.
  • Modelling microbial interactions at single cell level, using stochastic modelling techniques. Research with pure cultures will be extended to include interactions between single cells in mixed populations.
  • Using biostatistics tools to evaluate Microarray data and model gene expression during periods of microbial adaptation.
  • Developing a new approach to characterise microbial populations as complex networks.
This research has new potential when applied to describe complex microbial ecology (in food, in GI tract). It will be developed in strong collaboration with partners in the US. Our research therefore integrate modelling approaches at different levels of microbiology:
  1. The microbial population level (see the ComBase database on microbial responses to the food environment and related computational tools and models at www.combase.cc);
  2. The single cell level (see the Bacanova project outcome and Varibase database on http://www.ifr.ac.uk/bacanova/);
  3. The molecular levels (see the Gencom tool and related research to analyse Microarray data at http://www.ifr.ac.uk/safety/gencom/).
Funding Source
Biotechnology and Biological Sciences Research Council
Project number
BBS/E/F/00042266
Categories
Predictive Microbiology