Biomedical subjects

József Baranyi

Publications and source records attributed to József Baranyi.

11 recordsLinked to original sources

PubMed2006

The effect of reuterin on the lag time of single cells of Listeria innocua grown on a solid agar surface at different pH and NaCl concentrations.

The lag time of single cells of Listeria innocua grown on the surface of Brain Heart Infusion Agar was studied by microscopy and image analysis. An experimental set-up that enabled relocation of the cells on the agar surface was developed and used to collect data from 50 to 100 individual cells at a time. Reuterin was added at different concentrations (0-10 AU/ml) and it was observed that it increased both the lag time of the cells and its variance. Furthermore, for a large proportion of cells, reuterin completely prevented the cell division within the time of observation. Reuterin in combination with low pH inhibited the cell division even more efficiently. A similar effect was observed for the combination of reuterin and sodium chloride. Our experimental set-up provides a good model system for generating data on the lag time of single cells on solid surfaces, which can improve the predictions of microbial growth on solid food matrices.

Agar↗

PubMed2006

Comparison of different approaches for comparative genetic analysis using microarray hybridization.

A robust analysis of comparative genomic microarray data is critical for meaningful genomic comparison studies. In this paper, we compare our method (implemented in a new software tool, GENCOM, freely available at http://www.ifr.ac.uk/safety/gencom ) with three commonly used analysis methods: GACK (freely available at http://falkow.stanford.edu ), an empirical cut-off value of twofold difference between the fluorescence intensities after LOWESS normalization or after AVERAGE normalization in which the fluorescence intensity is divided by the average fluorescence intensity of the entire data set. Each method was tested using data sets from real experiments with prior knowledge of conserved and divergent genes. GENCOM and GACK were superior when a high proportion of genes were divergent. GENCOM was the most suitable method for the data set in which the relationship between the fluorescence intensities was not linear. GENCOM has proved robust in an analysis of all the data sets tested.

Algorithms↗

PubMed2006

Kinetics of single cells: observation and modeling of a stochastic process.

The successive generation times for single cells of Escherichia coli K-12 were measured as described by A. Elfwing, Y. LeMarc, J. Baranyi, and A. Ballagi (Appl. Environ. Microbiol. 70:675-678, 2004), and the histograms they generated were used as empirical distributions to simulate growth of the population as the result of the multiplication of its single cells. This way, a stochastic birth model in which the underlying distributions were measured experimentally was simulated. To validate the model, analogous bacterial growth curves were generated by the use of different inoculum levels. The agreement with the simulation was very good, proving that the growth of the population can be predicted accurately if the distribution of the first few division times for the single cells within that population is known. Two questions were investigated by the simulation. (i) To what extent can we say that the distribution of the detection time, i.e., the time by which a single-cell-generated subpopulation reaches a detectable level, can be identified with that of the lag time of the original single cell? (ii) For low inocula, how does the inoculum size affect the lag time of the population?

Cell Division↗

PubMed2005

Stochastic modelling of individual cell growth using flow chamber microscopy images.

In this paper, we analysed individual cell growth images obtained by flow chamber microscopy system. We used replicate flow chamber experiment data as reported by [Elfwing, A., Le Marc, Y., Baranyi, J., Ballagi, A., 2004. Observing the growth and division of large number of individual bacteria using image analysis. Applied and Environmental Microbiology 70, 675-678] involving both unstressed and heat shocked Listeria innocua cells. After observing the kinetics of a large number of cells, we propose a new stochastic model for their individual growth. By comparing our model with other existing models in the literature, we demonstrate that ours can accurately describe the growth of both stressed and unstressed cells. Our results indicate that the lag period, in terms of cell division, coincides dominantly with a lag period in terms of cell size. We also reveal various connections between cell length, lag time and cell division models. Finally, we present the results of our investigation on the effect of the duration of sublethal heat shock on the found growth properties.

Biomass↗

PubMed2005

Connection between stochastic and deterministic modelling of microbial growth.

We present in this paper various links between individual and population cell growth. Deterministic models of the lag and subsequent growth of a bacterial population and their connection with stochastic models for the lag and subsequent generation times of individual cells are analysed. We derived the individual lag time distribution inherent in population growth models, which shows that the Baranyi model allows a wide range of shapes for individual lag time distribution. We demonstrate that individual cell lag time distributions cannot be retrieved from population growth data. We also present the results of our investigation on the effect of the mean and variance of the individual lag time and the initial cell number on the mean and variance of the population lag time. These relationships are analysed theoretically, and their consequence for predictive microbiology research is discussed.

Bacteria↗

PubMed2004

Methods to determine the growth domain in a multidimensional environmental space.

Data from a database on microbial responses to the food environment (ComBase, see www.combase.cc) were used to study the boundary of growth several pathogens (Aeromonas hydrophila, Escherichia coli, Listeria monocytogenes, Yersinia enterocolitica). Two methods were used to evaluate the growth/no growth interface. The first one is an application of the Minimum Convex Polyhedron (MCP) introduced by Baranyi et al. [Baranyi, J., Ross, T., McMeekin, T., Roberts, T.A., 1996. The effect of parameterisation on the performance of empirical models used in Predictive Microbiology. Food Microbiol. 13, 83-91.]. The second method applies logistic regression to define the boundary of growth. The combination of these two different techniques can be a useful tool to handle the problem of extrapolation of predictive models at the growth limits.

Aeromonas hydrophila↗

PubMed2004

Analysis and validation of a predictive model for growth and death of Aeromonas hydrophila under modified atmospheres at refrigeration temperatures.

Specific growth and death rates of Aeromonas hydrophila were measured in laboratory media under various combinations of temperature, pH, and percent CO(2) and O(2) in the atmosphere. Predictive models were developed from the data and validated by means of observations obtained from (i) seafood experiments set up for this purpose and (ii) the ComBase database (http://www.combase.cc; http://wyndmoor.arserrc.gov/combase/). Two main reasons were identified for the differences between the predicted and observed growth in food: they were the variability of the growth rates in food and the bias of the model predictions when applied to food environments. A statistical method is presented to quantitatively analyze these differences. The method was also used to extend the interpolation region of the model. In this extension, the concept of generalized Z values (C. Pin, G. García de Fernando, J. A. Ordóñez, and J. Baranyi, Food Microbiol. 18:539-545, 2001) played an important role. The extension depended partly on the density of the model-generating observations and partly on the accuracy of extrapolated predictions close to the boundary of the interpolation region. The boundary of the growth region of the organism was also estimated by means of experimental results for growth and death rates.

Aeromonas hydrophila↗

PubMed2004

ComBase: a common database on microbial responses to food environments.

The advancement of predictive microbiology relies on available data that describe the behavior of microorganisms in different environmental matrices. For such information to be useful to the predictive microbiology research community, data must be organized in a manner that permits efficient access and data retrieval. Here, we describe a database protocol that encompasses observations of bacterial responses to food environments, resulting in a database (ComBase) for predictive microbiology purposes. The data included in ComBase were obtained from cooperating research institutes and from the literature and are publicly available via the Internet.

Bacteria↗

PubMed2003

Distribution of turbidity detection times produced by single cell-generated bacterial populations.

The distributions of the times to turbidity for wells inoculated with single cells of Listeria innocua were determined in different environmental conditions (pH 4.5 to 7 and with 0.5% to 8% of NaCl at 30 degrees C). It was established by statistical analysis that the main source of the variability of the detection times, T, is the variability of individual lag times. A linear relation dev(T) approximately T was observed between the detection times and their standard deviation. At slow growth, other sources of variability became increasingly significant.

Computer Simulation↗

PubMed2002

Analysing the lag-growth rate relationship of Yersinia enterocolitica.

A generalised z-value concept has been applied to analyse the relationship between the lag and the growth rate of Yersinia enterocolitica at a range of temperature, atmospheric carbon dioxide and oxygen percentages. The product of the specific growth rate and the lag (the "work to be done" during the lag phase) is found to be independent of temperature. However, it does depend on the CO2 and O2 concentrations, though the effect of oxygen was less noticeable than the effect of carbon dioxide.

Carbon Dioxide↗

PubMed2002

Stochastic modelling of bacterial lag phase.

In order to study the lag distribution of the individual cells in a bacterial population, a stochastic birth model is used in this study. An integral formula is applied to transform the assumed lag distribution into a growth function describing the transition between lag and exponential phase of the cell population. By means of this formula, it is pointed out that traditional viable count curves are not suitable to identify the distribution of individual cells' lag time.

Bacteria↗