Stochasticity in Colonial Growth Dynamics of Individual Bacterial Cells

Monitoring cell growth and measuring physical features of food-borne pathogenic bacteria are important for better understanding the conditions under which these organisms survive and proliferate. To address this challenge, buoyant masses of live and dead Escherichia coli O157:H7 and Listeria innocua were measured using Archimedes, a commercially available suspended microchannel resonator (SMR). Cell growth was monitored with Archimedes by observing increased cell concentration and buoyant mass values of live growing bacteria. These growth data were compared to optical density measurements obtained with a Bioscreen system. We observed buoyant mass measurements with Archimedes at cell concentrations between 10 5 and 10 8 cells/ ml, while growth was not observed with optical density measurements until the concentration was 10 7 cells/ml. Buoyant mass measurements of live and dead cells with and without exposure to hydrogen peroxide stress were also compared; live cells generally had a larger buoyant mass than dead cells. Additionally, buoyant mass measurements were used to determine cell density and total mass for both live and dead cells. Dead E. coli cells were found to have a larger density and smaller total mass than live E. coli cells. In contrast, density was the same for both live and dead L. innocua cells, while the total mass was greater for live than for dead cells. These results contribute to the ongoing challenge to further develop existing technologies used to observe cell populations at low concentrations and to measure unique physical features of cells that may be useful for developing future diagnostics.

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confidence: 99%

Mass and Density Measurements of Live and Dead Gram-Negative and Gram-Positive Bacterial Populations

Lewis

Craig

Senecal

2014

“…More recently, studies also emphasized the need to take into account the single-cell growth probability when assessing the behavior of these food-borne bacteria (14)(15)(16)(17). These observations were used to implement individual-based probabilistic approaches to assess the bacterial growth in food (18)(19)(20).…”

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confidence: 99%

Combining Individual-Based Modeling and Food Microenvironment Descriptions To Predict the Growth of Listeria monocytogenes on Smear Soft Cheese

Ferrier

Hezard²,

Lintz³

et al. 2013

An individual-based modeling (IBM) approach was developed to describe the behavior of a few Listeria monocytogenes cells contaminating smear soft cheese surface. The IBM approach consisted of assessing the stochastic individual behaviors of cells on cheese surfaces and knowing the characteristics of their surrounding microenvironments. We used a microelectrode for pH measurements and micro-osmolality to assess the water activity of cheese microsamples. These measurements revealed a high variability of microscale pH compared to that of macroscale pH. A model describing the increase in pH from approximately 5.0 to more than 7.0 during ripening was developed. The spatial variability of the cheese surface characterized by an increasing pH with radius and higher pH on crests compared to that of hollows on cheese rind was also modeled. The microscale water activity ranged from approximately 0.96 to 0.98 and was stable during ripening. The spatial variability on cheese surfaces was low compared to between-cheese variability. Models describing the microscale variability of cheese characteristics were combined with the IBM approach to simulate the stochastic growth of L. monocytogenes on cheese, and these simulations were compared to bacterial counts obtained from irradiated cheeses artificially contaminated at different ripening stages. The simulated variability of L. monocytogenes counts with the IBM/microenvironmental approach was consistent with the observed one. Contrasting situations corresponding to no growth or highly contaminated foods could be deduced from these models. Moreover, the IBM approach was more effective than the traditional population/macroenvironmental approach to describe the actual bacterial behavior variability. Since risk analysis emerged as the internationally recognized framework to improve food control systems, many risk assessments were published that evaluated the probabilities and severities of adverse health effects resulting from the exposure of consumers to pathogenic microorganisms present in foods. This is especially the case for the food-borne pathogen bacterium Listeria monocytogenes. Its ubiquitous nature and its ability to multiply in many foods during chilled storage fostered the development of quantitative microbial risk assessment aimed at ranking foods according to risk or predicting the impact of management options (1-6).The assessment of the microbial behavior of the pathogen is of the highest importance when performing these quantitative assessments, since listeriosis cases are predominantly linked to the consumption of highly contaminated foods (2), and the variability of this behavior is of paramount importance in the context of exposure assessment (7, 8). The major sources of variability affecting microbial responses in foods are the initial contamination level, the variability in processing factors, the variability in food characteristics and in the storage conditions, and the biological variability, i.e., the variability of microbial behavior.For several years, it has...

“…Experimental techniques for single-cell studies include turbidimetry (see for example, references 7 and 8), lithographic techniques (9), and flow cytometry (10). Recently, time-lapse microscopy has been successfully applied to obtain quantitative information on colonial growth dynamics originated from single cells (2). Based on the flow chamber microscopy technique proposed in reference 10, models for individual cell growth have been developed as described in references 4 and 11.…”

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confidence: 99%

“…Recently, attention has been drawn to the need for modeling and simulation methods to observe and describe the variability of singlecell behavior and small populations (1,2) in order to produce realistic estimations of safety risks along the food chain, for instance, during storage and distribution or during food processing.…”

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confidence: 99%

Modeling Bacterial Population Growth from Stochastic Single-Cell Dynamics

Alonso¹,

Molina²,

Theodoropoulos

2014

bA few bacterial cells may be sufficient to produce a food-borne illness outbreak, provided that they are capable of adapting and proliferating on a food matrix. This is why any quantitative health risk assessment policy must incorporate methods to accurately predict the growth of bacterial populations from a small number of pathogens. In this aim, mathematical models have become a powerful tool. Unfortunately, at low cell concentrations, standard deterministic models fail to predict the fate of the population, essentially because the heterogeneity between individuals becomes relevant. In this work, a stochastic differential equation (SDE) model is proposed to describe variability within single-cell growth and division and to simulate population growth from a given initial number of individuals. We provide evidence of the model ability to explain the observed distributions of times to division, including the lag time produced by the adaptation to the environment, by comparing model predictions with experiments from the literature for Escherichia coli, Listeria innocua, and Salmonella enterica. The model is shown to accurately predict experimental growth population dynamics for both small and large microbial populations. The use of stochastic models for the estimation of parameters to successfully fit experimental data is a particularly challenging problem. For instance, if Monte Carlo methods are employed to model the required distributions of times to division, the parameter estimation problem can become numerically intractable. We overcame this limitation by converting the stochastic description to a partial differential equation (backward Kolmogorov) instead, which relates to the distribution of division times. Contrary to previous stochastic formulations based on random parameters, the present model is capable of explaining the variability observed in populations that result from the growth of a small number of initial cells as well as the lack of it compared to populations initiated by a larger number of individuals, where the random effects become negligible. Often bacterial contamination of foods starts with a small number of bacteria that are capable of adapting and proliferating by repeated divisions on a given food matrix. At low cell concentrations, standard deterministic models fail to predict the variability of the bacterial population. This is so because at low initial cell numbers, heterogeneity between individuals and its influence on the division times become relevant and have a net influence on the population. Consequently, the behavior of individual bacteria cannot be neglected when assessing possible health risks along the food chain, either during storage or during distribution.Recently, attention has been drawn to the need for modeling and simulation methods to observe and describe the variability of singlecell behavior and small populations (1, 2) in order to produce realistic estimations of safety risks along the food chain, for instance, during storage and distribution or during food pro...