Research Leader: Dr József Baranyi
The core of our efforts in science is the application of advanced mathematical, statistical and computational tools to food microbiology research. Our interdisciplinary approach is key to the activity of the group.
Our research includes (i) Predictive Microbiology, a quantitative approach to food microbiology); (ii) Modelling Microbial Complexity and Variability by network analysis and stochatic processes; and (iii) Biostatistical Analysis of microarray data (see further information on IFR's Microarray facility) .
Predictive Microbiology can also be called Quantitative Microbial Ecology of Food. Its main focus is the description of microbial responses to food environments using mathematical models and other numerical and statistical methods.
Predictive Microbiology results are utilised by the food industry (e.g. for the formulation of new products), regulatory bodies (e.g. to issue guidelines for storage and handling of foods) and by risk assessors working in the food trade. A main output of our efforts in the area is the internationally acclaimed ComBase database and its software tools (www.combase.cc).
Modelling Microbial Complexity and Variability
We see Network Science as a major tool to characterise Microbial Complexity. Our group played a major role in organising NetSci’08 (see www.ifr.ac.uk/netsci08). The variability of single cells kinetics was the subject of several of our recent papers (see the publication list).
Biostatistical Analysis of Microarray datasets
In this area, we have developed and published two software tools, which are freely available for download. GENCOM and ArrayLeaRNA represent new approaches to the analysis of microarray hybridisation data derived from comparative genomic and gene expression studies.
Our interests and current projects
- Dynamic modelling of bacterial growth as function of the environment
( József Baranyi and Yvan Le Marc)
- ComBase: A Combined Database of microbial
responses to the food environment.
- Stochastic modelling of the kinetics of individual cells
( József Baranyi, Carmen Pin and Aline Métris).
- Probability of growth and growth domain of pathogens
( Yvan Le Marc, József Baranyi and Carmen Pin).
- Modelling microbial adaptation and interaction: analysis of transcriptomic networks
(József Baranyi and Carmen Pin).
|The Computational Microbiology
Team is lead by
Dr József Baranyi
|Dr Aline Métris||Dr Carmen Pin||Susie George|
|Gaspar Avendaño||Dr Marina Muñoz||Daniel Marín|
PhD. Students and Visiting Scientists
Prof José M. Peinado, Visiting Professor in 2005, 2006, 2007 and 2008.. Former Head of the Dept. of Microbiology, Faculty of Biology and professor of Industrial Microbiology at the University Complutense, Madrid, Spain.
Adriana Lobacz, visiting student in 2006, while studying for her PhD. at the University of Warmia and Mazury in Olsztyn, Poland.
Dr Sylvain Leroy, visiting student in 2006, while studying for his PhD. at the University of Bologna, Italy. Currently Professor of Food Science at the University of Yaoundé, Cameroon.
Dr Zoltán Kutalik, formally PhD. student at University of East Anglia and IFR (2003-2006), co-supervised by Dr J. Baranyi. Currently Postdoctoral Research Assistant, Dept of Medical Genetics, University of Lausanne, Switzerland.
Julie Farjon, visiting student in 2005, from I.U.P. Innovation en Industries Alimentaires, France.
Dr Elena Cosciani, visiting student in 2004, from the Instituto Zooprofilattico Sperimentale della Lombardia e dell’Emilia Romagna, Brescia, Italy. Obtained her PhD. at the University of Parma, Italy; co-supervised by Dr J. Baranyi.
Dr Elena Carrasco, visiting student in 2004, while studying for her PhD. at the University of Cordoba, Spain.
Dr Roberto Sotoca, visiting student in 2004, while studying for his PhD. at the University Complutense Madrid, Spain. Currently working at the Quality Dept. of Carrefour, Spain.
Anders Elfwing, visiting scientist in 2004, from the Uppsala University, Uppsala, Sweden.
Numerous programs have been developed at IFR for the applications of Predictive Modelling. They can be browsed or downloaded free of charge.
Predictive microbiology uses mathematical models to predict the rate of growth of an organism in a given environment. Effective models can only be formed based on large quantities of data. A database, ComBase, has been created to consolidate data from scientific literature. The whole database can be browsed on the internet or standalone prototypes can be downloaded at www.combase.cc
DMFit is a software package that can be used to estimate the specific growth rates from experimental data with the Baranyi model. DMFit is available to download, free of charge at www.ifr.ac.uk/safety/DMFit
Gencom is for the analysis of microarray hybridisation data derived from comparative genomic studies. Gencom is available to download, free of charge at www.ifr.ac.uk/safety/Gencom
ArrayLeaRNA is for the analysis of microarray hybridisation data derived from gene expression profile studies. ArrayLeaRNA is available to download, free of charge at www.ifr.ac.uk/safety/ArrayLeaRNA
Salmonella predictions is an Excel add-in to predict Salmonella spp. in food environments. The Salmonella_Predictions.xls Excel add-in is avilable to download, free of charge at http://www.ifr.ac.uk/safety/SalmonellaPredictions
A. Métris, S. M. George, Mackey BM, J. Baranyi (2008). Modelling the variability of the lag times of single cells of Listeria innocua populations in response to sub-lethal and lethal heat treatments. Appl.Env.Microbiol 74. 6949–6955.
Pin C., Reuter M., Pearson B., Friis L., Overweg K., Baranyi J. and Wells J.M. (2006) Comparison of commonly used approaches for comparative genetic analysis using microarray hybridisations. Applied Microbiology and Biotechnology 72, 852-859.
Pin C. and Baranyi J. (2006) Kinetics of single cells: Observation and modelling of a stochastic process. Appl. Environ. Microbiol. 72. 2163-2169.
Le Marc Y., Pin C., Baranyi J. (2005) Methods to determine the growth domain in a multidimensional environmental space. Int. J. Food Microbiol. 100: 3-12.
Kutalik, Z., Razaz, M. and Baranyi, J. (2005). Connection between stochastic and deterministic modelling of bacterial growth. J. Theor. Biol. 232 (2), 283-297.
Elfwing A., Le Marc Y., Baranyi J. and Ballagi A. (2004). Observing the growth and division of large number of individual bacteria using image analysis. Appl. Environ. Microbiol. 70. 675-678
Baranyi J. and Roberts T.A. (1994) A dynamic approach to predicting bacterial growth in food. Int. J. Food Microbiol. 23, 277-294.