Biomedical subjects

A R Standaert

Publications and source records attributed to A R Standaert.

8 recordsLinked to original sources

PubMed2006

Effect of environmental parameters (temperature, pH and a(w)) on the individual cell lag phase and generation time of Listeria monocytogenes.

The effect of the individual environmental factors temperature (2-30 degrees C), pH (4.4-7.4) and a(w) (0.947-0.995) as well as the combinations of these factors on the individual cell lag phase and the generation time of Listeria monocytogenes was investigated. Individual cells were isolated using a serial dilution protocol in microtiter plates, and subsequent growth was investigated by optical density (OD) measurements at 600 nm. About 100 replicates were made for each set of environmental conditions. Part of the data were previously published in Francois et al. (Francois, K., Devlieghere, F., Smet, K., Standaert, A.R., Geeraerd, A.H., Van Impe, J.F., Debevere, J., 2005a. Modelling the individual cell lag phase: effect of temperature and pH on the individual cell lag distribution of Listeria monocytogenes. Int. J. Food Microbiol. 100, 41-53.), but were recalculated here using the calibration curves for transformation of optical density to colony forming units/ml from Francois et al. (Francois, K., Devlieghere, F., Standaert, A.R., Geeraerd, A.H., Cools, I., Van Impe, J.F., Debevere, J., 2005b. Environmental factors influencing the relationship between optical density and cell count for Listeria monocytogenes. J. Appl. Microbiol. 99, 1503-1515), as this calibration curve appeared to be dependent on the environmental parameters. The previous dataset was also extended with a factor a(w), observed individually and combinations with the above mentioned environmental factors. Individual cell lag phases and subsequent growth rates were calculated assuming an exponential growth model. The results are discussed as mean values to determine the general trends and in addition, histograms are made and statistical distributions are fitted to the different data sets. When stress levels increased, the mean values and the variability observed for the individual cell lag phases increased, resulting in broader histograms and distributions that were shifting to the right. Also the gravity point of the distributions was shifting from a skewed left type to a more symmetrical type. The best description of the data is obtained with an exponential distribution for low stress levels, a gamma distribution for intermediate stress and a Weibull distribution for severe stress levels. When only low stress levels were applied, a significant percentage of the cells showed no lag phase. In those cases, a new approach was used to obtain better fits: cells with a lag phase and those without a lag phase were separated using a binomial distribution while in a second step, a gamma or a Weibull distribution is fitted to the fraction of cells showing a lag phase. A normal distribution is used to describe the variability of the generation times. These distributions can be applied to refine the exposure assessment part of the risk assessment concerning L. monocytogenes by incorporating intercellular variability.

Colony Count, Microbial↗

PubMed2006

Single cell variability of L. monocytogenes grown on liver pâté and cooked ham at 7 degrees C: comparing challenge test data to predictive simulations.

AIMS: The variability in growth between individual Listeria monocytogenes cells was investigated on liver pâté and cooked ham. These results were compared to Monte Carlo simulations based on data collected previously in broths (Francois et al., submitted for publication). METHODS AND RESULTS: Single cells were isolated by a dilution protocol and inoculated on 15 g samples of liver pâté and cooked ham, pasteurized in the packaging. Of each product, 250 samples were inoculated, of which 50 samples were analysed for L. monocytogenes on each analysis day. Results were compared to simulations, based on distributions that describe the variability of the individual cell lag phases and generation times of L. monocytogenes cultivated in broths. Based on the same simulation techniques, the variability effect was investigated for different inoculum levels (10, 100, 10,00 and 10,000 cells). It was demonstrated that the expected variability of the outgrowth of L. monocytogenes in a challenge test is very high for low inoculum levels. CONCLUSIONS: The variability in growth characteristics observed between different single L. monocytogenes cells on foods is very large. The simulations based on the previously collected optical density data in broths, could be confirmed by foods inoculated with single L. monocytogenes cells. SIGNIFICANCE AND IMPACT OF THE STUDY: The large variability between different individual L. monocytogenes cells has serious consequences for the experimental design of a challenge test. One thousand cells per portion are necessary in order to reduce the variability to acceptable levels and quantify the behaviour of the pathogen consistently with a reasonable number of challenge tests.

Animals↗

PubMed2005

Cell division theory and individual-based modeling of microbial lag: part I. The theory of cell division.

This series of two papers deals with the theory of cell division and its implementation in an individual-based modeling framework. In this first part, the theory of cell division is studied on an individual-based level in order to learn more about the mechanistic principles behind microbial lag phenomena. While some important literature on cell division theory dates from 30 to 40 years ago, until now it has hardly been introduced in the field of predictive microbiology. Yet, it provides a large amount of information on how cells likely respond to changing environmental conditions. On the basis of this theory, a general theory on microbial lag behavior caused by a combination of medium and/or temperature changes has been developed in this paper. The proposed theory then forms the basis for a critical evaluation of existing modeling concepts for microbial lag in predictive microbiology. First of all, a more thorough definition can be formulated to define the lag time lambda and the previously only vaguely defined physiological state of the cells in terms of mechanistically defined parameters like cell mass, RNA or protein content, specific growth rate and time to perform DNA replication and cell division. On the other hand, existing predictive models are evaluated with respect to the newly developed theory. For the model of , a certain fitting parameter can also be related to physically meaningful parameters while for the model of [Augustin, J.-C., Rosso, L., Carlier, V.A. 2000. A model describing the effect of temperature history on lag time for Listeria monocytogenes. Int. J. Food Microbiol. 57, 169-181] a new, mechanistically based, model structure is proposed. A restriction of the proposed theory is that it is only valid for situations where biomass growth responds instantly to an environment change. The authors are aware of the fact that this assumption is not generally acceptable. Lag in biomass can be caused, for example, by a delayed synthesis of some essential growth factor (e.g., enzymes). In the second part of this series of papers [Dens, E.J., Bernaerts, K., Standaert, A.R., Kreft, J.-U., Van Impe, J.F., this issue. Cell division theory and individual-based modeling of microbial lag: part II. Modeling lag phenomena induced by temperature shifts. Int. J. Food Microbiol], the theory of cell division is implemented in an individual-based simulation program and extended to account for lags in biomass growth. In conclusion, the cell division theory applied to microbial populations in dynamic medium and/or temperature conditions provides a useful framework to analyze microbial lag behavior.

Algorithms↗

PubMed2005

Cell division theory and individual-based modeling of microbial lag: part II. Modeling lag phenomena induced by temperature shifts.

This paper is the second in a series of two, and studies microbial lag in cell number and/or biomass measurements caused by temperature changes with an individual-based modeling approach. For this purpose, the theory of cell division, as discussed in the first part of this series of research papers, was implemented in the individual-based modeling framework BacSim. Simulations of this model are compared with experimental data of Escherichia coli, growing in an aerated, glucose-rich medium and subjected to sudden temperature shifts. The premise of a constant cell volume under changing temperature conditions predicts no lag in cell numbers after the shift, in contrast to the experimental observations. Based on literature research, two biological mechanisms that could be responsible for the observed lag phenomena are proposed. The first assumes that the average cell volume depends on temperature while the second assumes that a lag in biomass growth occurs after the temperature shift. For a lag in cell number caused by an increased average cell volume, the cell biomass always increases at the maximal rate. Therefore, cells are evidently not stressed and do not have to adapt to the new conditions, as opposed to a lag in biomass growth. Implementation and simulation of both mechanisms are found to describe the experimental observations equally well. Therefore, further research is needed to distinguish between the two mechanisms. This can be done by observing, in addition to cell numbers, a measure for the average cell volumes. In conclusion, the individual-based modeling approach is a good methodology to investigate and test biological theories and assumptions. Also, based on the simulations, suggestions for further experimental observations can be made.

Algorithms↗

PubMed2005

Environmental factors influencing the relationship between optical density and cell count for Listeria monocytogenes.

AIMS: The effect of temperature (2-30 degrees C), pH (4.8-7.4) and water activity (0.946-0.995) on the relationship between optical density (OD) at 600 nm and the plate count (CFU ml(-1)) was investigated for Listeria monocytogenes. METHODS AND RESULTS: Calibration curves, relating OD with plate counts, were collected by measuring the OD of consecutive one-half dilution series, before determining the cell density by classic plate count methods. The calibration curves were observed to be shifting in a parallel way, with increasing stress levels. Especially pH influenced the curve in a great extent, while the other variables were showing more synergetic effects. The reason for the shift was investigated by a microscopic viability test, showing a viability decrease with increasing stress levels, causing the shift of the calibration curve. In a last step a model was made describing the effect of environmental factors on the calibration curve, with different data transformations being tested. A polynomial equation was fitted to the data, taking into account a set of constraints to incorporate microbiological knowledge in the black box model. Hence, illogical interpolation results and overfitting of the data could be avoided. CONCLUSIONS: Different stress factors are affecting the relationship between the OD and the cell count of L. monocytogenes by lowering the cell viability. These effects could be modelled using a constrained polynomial model. SIGNIFICANCE AND IMPACT OF THE STUDY: The observed phenomena are important when calculating growth parameters, like growth rate and lag phase, based on OD data.

Calibration↗

PubMed2004

Obtaining single cells: analysis and evaluation of an experimental protocol by means of a simulation model.

The research presented in this paper analyses a newly developed experimental protocol for isolating single cells by constructing a simulation model of the process. The protocol involves sequential 50% dilutions of a cell suspension in a microtiter plate, so that eventually, wells are obtained containing exactly one cell. The aim of this modelling study is (i) to gain insight in the governing mechanisms of the dilution process, (ii) to confirm experimental findings and (iii) to enable the prediction of an average outcome for future experiments. The model construction process is presented chronologically. The initial basic model simulates the experiment as a sequence of binomial processes, using Monte Carlo techniques. Statistical analysis of the results shows that aggregational factors need to be taken into account in the form of a lognormal distribution. Several issues involved in this adaptation are discussed. To fully account for cell aggregation in the dilution process, a cell clumping algorithm is built into the simulation model. Simulation data from the resulting model show similar statistical characteristics as the experimental data and yield reliable prediction intervals for the available experimental data. The simulation model is a useful tool to support experimental findings and predict the outcome of future experiments. Even more importantly, this study emphasises the importance of careful statistical analysis in single cell research. The impact of stochastic effects is considerably amplified at the low cell concentrations involved and needs to be taken into account in any modelling effort.

Bacteria↗

PubMed2004

Modelling the individual cell lag phase: effect of temperature and pH on the individual cell lag distribution of Listeria monocytogenes.

The individual-based approach of the lag phase is gaining interest, especially for pathogens that initially contaminate food products in low amounts. In this paper, the effect of temperature (30, 10, 7, 4 and 2 degrees C) and pH (7.4, 6.1, 5.5, 5.0, 4.7 and 4.4) on the individual cell lag phase of Listeria monocytogenes was examined in a factorial design, using OD measurements. Individual lag phases of about 100 individual cells per condition were examined and calculated using a linear extrapolation method. Generation times were calculated out of the slope. The obtained data were analyzed at three different levels: in a first approach, the mean values were calculated for each set of environmental conditions and compared to predictions made by the USDA's Pathogen Modeling Program (PMP) for analogous growth conditions. The PMP predictions of the generation times were in the same order of magnitude as the obtained data, although a persistent underestimation could be observed. The observed individual cell lag data differed from lag phase predictions by PMP. Possible reasons for this discrepancy are discussed. Secondly, histograms of individual lag phase measurements were constructed for the different temperature-pH combinations. In this way, the influence of both factors on the variability of individual lag phases could be estimated. At low stress levels, most individual cells showed a short lag phase resulting in a compression of the histograms at the zero-lag level, while, at high stress levels, the histograms shifted to longer lag phases with a significant increase in variability. Thirdly, 37 different distribution types were fitted to the datasets to reveal the distributions that fitted best the obtained data. The gamma distribution was preferred at moderate stress levels, while the Weibull distribution was chosen for harsher growth conditions.

Colony Count, Microbial↗

PubMed2003

Modelling the individual cell lag phase. Isolating single cells: protocol development.

AIMS: To develop a protocol to isolate single cells in wells of a microtitre plate, having a high certainty of individual cells, combined with a sufficient yield. METHODS AND RESULTS: Single cells were obtained using 1/2 dilution series in microtitre plates. Seventy-two Lactococcus lactis dilution series were checked by plate counting. When the last five columns of the plates were observed, the chance of having one single cell was 80%, while the yield was 75 wells containing cells. A simulation model confirmed these results. This method was compared with the commonly applied method. CONCLUSIONS: This method makes it possible to combine a higher chance of having one cell in a microtitre well with a slightly higher yield. SIGNIFICANCE AND IMPACT OF THE STUDY: A tool is developed to isolate single cells to provide a suitable base for investigating and modelling the individual cell lag phase.

Cell Division↗