Stochasticity in colonial growth dynamics of individual bacterial cells

Individual cell heterogeneity in Predictive Food Microbiology: Challenges in predicting a "noisy" world

Gene expression is a fundamentally noisy process giving rise to a significant cell to cell variability at the phenotype level. The phenotypic noise is manifested in a wide range of microbial traits. Heterogeneous behavior of individual cells is observed at the growth, survival and inactivation responses and should be taken into account in the context of Predictive Food Microbiology (PMF). Recent methodological advances can be employed for the study and modeling of single cell dynamics leading to a new generation of mechanistic models which can provide insight into the link between phenotype, gene-expression, protein and metabolic functional units at the single cell level. Such models however, need to deal with an enormous amount of interactions and processes that influence each other, forming an extremely complex system. In this review paper, we discuss the importance of noise and present the future challenges in predicting the "noisy" microbial responses in foods.

Antibacterial Effect of Copper on Microorganisms Isolated from Bovine Mastitis

The antimicrobial properties of copper have been recognized for several years; applying these properties to the prevention of diseases such as bovine mastitis is a new area of research. The aim of the present study was to evaluate in vitro the antimicrobial activity of copper on bacteria isolated from subclinical and clinical mastitis milk samples from two regions in Chile. A total of 327 microorganisms were recovered between March and September 2013, with different prevalence by sample origin (25 and 75% from the central and southern regions of Chile, respectively). In the central region, Escherichia coli and coagulase negative Staphylococci (CNS) were the most frequently detected in clinical mastitis cases (33%), while in the southern region S. uberis, S. aureus, and CNS were detected with frequencies of 22, 21, and 18%, respectively. Antibiotic susceptibility studies revealed that 34% of isolates were resistant to one or more antibiotics and the resistance profile was different between bacterial species and origins of isolation of the bacteria. The minimum inhibitory concentration of copper (MIC-Cu) was evaluated in all the isolates; results revealed that a concentration as low as 250 ppm copper was able to inhibit the great majority of microorganisms analyzed (65% of isolates). The remaining isolates showed a MIC-Cu between 375 and 700 ppm copper, and no growth was observed at 1000 ppm. A linear relationship was found between the logarithm of viable bacteria number and time of contact with copper. With the application of the same concentration of copper (250 ppm), CNS showed the highest tolerance to copper, followed by S. uberis and S. aureus; the least resistant was E. coli. Based on these in vitro results, copper preparations could represent a good alternative to dipping solutions, aimed at preventing the presence and multiplication of potentially pathogenic microorganisms involved in bovine mastitis disease.

A second look at the final number of cells in a fluctuation experiment

In a fluctuation experiment, the number of cells existing in a culture immediately before plating (commonly known as Nt) varies across the parallel cultures. However, most existing mathematical models for fluctuation assay data do not recognize the variation in Nt. Despite repeated attempts in the past to integrate this source of variability in the estimation of microbial mutation rates, several questions of practical importance remain unanswered. The present investigation finds that the variation needs accounting for only when the coefficient of variation for Nt is large, and experimental data suggest that the coefficient of variation is often moderate or small. Moreover, an increase in the inoculum size can reduce the coefficient of variation. Through extensive simulation, several existing methods that accommodate the variation in Nt are compared. It was found that a newly devised likelihood method based on the existing gamma mixture model outperforms other existing methods. The investigation focuses on the estimation of mutation rates using the Lea-Coulson model, under which mutation is selectively neutral; however, the paper also explores the major findings' implications for the comparison of mutation rates using the likelihood ratio test, and for the estimation of mutation rates using the Mandelbrot-Koch model that allows for non-neutral mutations.

Variability in Cell Response of Cronobacter sakazakii after Mild-Heat Treatments and Its Impact on Food Safety

Cronobacter spp. have been responsible for severe infections in infants associated with consumption of powdered infant formula and follow-up formulae. Despite several risk assessments described in published studies, few approaches have considered the tremendous variability in cell response that small micropopulations or single cells can have in infant formula during storage, preparation or post process/preparation before the feeding of infants. Stochastic approaches can better describe microbial single cell response than deterministic models as we prove in this study. A large variability of lag phase was observed in single cell and micropopulations of ≤50 cells. This variability increased as the heat shock increased and growth temperature decreased. Obviously, variability of growth of individual Cronobacter sakazakii cell is affected by inoculum size, growth temperature and the probability of cells able to grow at the conditions imposed by the experimental conditions should be taken into account, especially when errors in bottle-preparation practices, such as improper holding temperatures, or manipulation, may lead to growth of the pathogen to a critical cell level. The mean probability of illness from initial inoculum size of 1 cell was below 0.2 in all the cases and for inoculum size of 50 cells the mean probability of illness, in most of the cases, was above 0.7.

Towards lag phase of microbial populations at growth-limiting conditions: The role of the variability in the growth limits of individual cells

The water activity (aw) growth limits of unheated and heat stressed Listeria monocytogenes individual cells were studied. The aw limits varied from 0.940 to 0.997 and 0.951 to 0.997 for unheated and heat stressed cells, respectively. Due to the above variability a decrease in aw results in the presence of a non-growing fraction in the population leading to an additional pseudo-lag in population growth. In this case the total apparent lag of the population is the sum of the physiological lag of the growing cells (time required to adjust to the new environment) and the pseudo-lag. To investigate the effect of aw on the above lag components, the growth kinetics of L. monocytogenes on tryptone soy agar with aw adjusted to values ranging from 0.997 to 0.940 was monitored. The model of B&R was fitted to the data for the estimation of the apparent lag. In order to estimate the physiological lag of the growing fraction of the inoculum, the model was refitted to the growth data using as initial population level the number of cells that were able to grow (estimated from the number of colonies formed on the agar at the end of storage) and excluding the rest data during the lag. The results showed that for the unheated cells the apparent lag was almost identical to the physiological lag for aw values ranging from 0.997 to 0.970, as the majority of the cells in the initial population was able to grow in these conditions. As the aw decreased from 0.970 to 0.940 however, the number of cells in the population which were able to grow, decreased resulting to an increase in the pseudo-lag. The maximum value of pseudo-lag was 13.1h and it was observed at aw=0.940 where 10% of the total inoculated cells were able to grow. For heat stressed populations a pseudo-lag started to increase at higher aw conditions (0.982) compared to unheated cells. In contrast to the apparent lag, a linear relation between physiological lag and aw was observed for both unheated and heat stressed cells.

Bacterial Colonies in Solid Media and Foods: A Review on Their Growth and Interactions with the Micro-Environment

Bacteria, either indigenous or added, are immobilized in solid foods where they grow as colonies. Since the 80's, relatively few research groups have explored the implications of bacteria growing as colonies and mostly focused on pathogens in large colonies on agar/gelatine media. It is only recently that high resolution imaging techniques and biophysical characterization techniques increased the understanding of the growth of bacterial colonies, for different sizes of colonies, at the microscopic level and even down to the molecular level. This review covers the studies on bacterial colony growth in agar or gelatine media mimicking the food environment and in model cheese. The following conclusions have been brought to light. Firstly, under unfavorable conditions, mimicking food conditions, the immobilization of bacteria always constrains their growth in comparison with planktonic growth and increases the sensibility of bacteria to environmental stresses. Secondly, the spatial distribution describes both the distance between colonies and the size of the colonies as a function of the initial level of population. By studying the literature, we concluded that there systematically exists a threshold that distinguishes micro-colonies (radius < 100-200 μm) from macro-colonies (radius >200 μm). Micro-colonies growth resembles planktonic growth and no pH microgradients could be observed. Macro-colonies growth is slower than planktonic growth and pH microgradients could be observed in and around them due to diffusion limitations which occur around, but also inside the macro-colonies. Diffusion limitations of milk proteins have been demonstrated in a model cheese around and in the bacterial colonies. In conclusion, the impact of immobilization is predominant for macro-colonies in comparison with micro-colonies. However, the interaction between the colonies and the food matrix itself remains to be further investigated at the microscopic scale.

Colonial vs. planktonic type of growth: mathematical modeling of microbial dynamics on surfaces and in liquid, semi-liquid and solid foods

Predictive models are mathematical expressions that describe the growth, survival, inactivation, or biochemical processes of foodborne bacteria. During processing of contaminated raw materials and food preparation, bacteria are entrapped into the food residues, potentially transferred to the equipment surfaces (abiotic or inert surfaces) or cross-contaminate other foods (biotic surfaces). Growth of bacterial cells can either occur planktonically in liquid or immobilized as colonies. Colonies are on the surface or confined in the interior (submerged colonies) of structured foods. For low initial levels of bacterial population leading to large colonies, the immobilized growth differs from planktonic growth due to physical constrains and to diffusion limitations within the structured foods. Indeed, cells in colonies experience substrate starvation and/or stresses from the accumulation of toxic metabolites such as lactic acid. Furthermore, the micro-architecture of foods also influences the rate and extent of growth. The micro-architecture is determined by (i) the non-aqueous phase with the distribution and size of oil particles and the pore size of the network when proteins or gelling agent are solidified, and by (ii) the available aqueous phase within which bacteria may swarm or swim. As a consequence, the micro-environment of bacterial cells when they grow in colonies might greatly differs from that when they grow planktonically. The broth-based data used for modeling (lag time and generation time, the growth rate, and population level) are poorly transferable to solid foods. It may lead to an over-estimation or under-estimation of the predicted population compared to the observed population in food. If the growth prediction concerns pathogen bacteria, it is a major importance for the safety of foods to improve the knowledge on immobilized growth. In this review, the different types of models are presented taking into account the stochastic behavior of single cells in the growth of a bacterial population. Finally, the recent advances in the rules controlling different modes of growth, as well as the methodological approaches for monitoring and modeling such growth are detailed.

Assessment of the effect of a Salmonella enterica ser. Typhimurium culture supernatant on the single-cell lag time of foodborne pathogens

The objective of this study was the in vitro evaluation of the effect of a cell-free microbial supernatant, produced by a luxS-positive Salmonella enterica ser. Typhimurium strain, on the single-cell growth kinetic behavior of two strains of S. enterica (serotypes Enteritidis and Typhimurium) and a methicillin-resistant Staphylococcus aureus strain. The single-cell lag time (λ) of the pathogens was estimated in the absence and presence (20% v/v) of microbial supernatant based on optical density measurements. As demonstrated by the obtained results, the tested microbial supernatant had a strain-specific effect on the single-cell λ and its variability. Although the mean λ values were similar in the absence and presence of microbial supernatant in the case of Salmonella Enteritidis, a significant (P ≤ 0.05) reduction and increase in the mean value of this parameter in the presence of microbial supernatant were observed for Salmonella Typhimurium and St. aureus, respectively. With regard to the effect of the tested microbial supernatant on the single-cell variability of λ, similar λ distributions were obtained in its absence and presence for S. Enteritidis, while considerable differences were noted for the other two tested organisms; the coefficient of variation of λ in the absence and presence of microbial supernatant was 41.6 and 69.8% for S. Typhimurium, respectively, with the corresponding values for St. aureus being 74.0 and 56.9%. As demonstrated by the results of bioassays, the tested microbial supernatant exhibited autoinducer-2 activity, indicating a potential association of such quorum sensing compounds with the observed effects. Although preliminary in nature, the collected data provide a good basis for future research on the role of quorum sensing in the single-cell growth behavior of foodborne pathogens.

Droplet-based microfluidics platform for ultra-high-throughput bioprospecting of cellulolytic microorganisms

Discovery of microorganisms producing enzymes that can efficiently hydrolyze cellulosic biomass is of great importance for biofuel production. To date, however, only a miniscule fraction of natural biodiversity has been tested because of the relatively low throughput of screening systems and their limitation to screening only culturable microorganisms. Here, we describe an ultra-high-throughput droplet-based microfluidic system that allowed the screening of over 100,000 cells in less than 20 min. Uncultured bacteria from a wheat stubble field were screened directly by compartmentalization of single bacteria in 20 pl droplets containing a fluorogenic cellobiohydrolase substrate. Sorting of droplets based on cellobiohydrolase activity resulted in a bacterial population with 17- and 7-fold higher cellobiohydrolase and endogluconase activity, respectively, and very different taxonomic diversity than when selected for growth on medium containing starch and carboxymethylcellulose as carbon source.

Recent trends in non-invasive in situ techniques to monitor bacterial colonies in solid (model) food

Planktonic cells typically found in liquid systems, are routinely used for building predictive models or assessing the efficacy of food preserving technologies. However, freely suspended cells often show different susceptibility to environmental hurdles than colony cells in solid matrices. Limited oxygen, water and nutrient availability, metabolite accumulation and physical constraints due to cell immobilization in the matrix, are main factors affecting cell growth. Moreover, intra- and inter-colony interactions, as a consequence of the initial microbial load in solid systems, may affect microbial physiology. Predictive food microbiology approaches are moving toward a more realistic resemblance to food products, performing studies in structured solid systems instead of liquids. Since structured systems promote microbial cells to become immobilized and grow as colonies, it is essential to study the colony behavior, not only for food safety assurance systems, but also for understanding cell physiology and optimizing food production processes in solid matrices. Traditionally, microbial dynamics in solid systems have been assessed with a macroscopic approach by applying invasive analytical techniques; for instance, viable plate counting, which yield information about overall population. In the last years, this approach is being substituted by more mechanistically inspired ones at mesoscopic (colony) and microscopic (cell) levels. Therefore, non-invasive and in situ monitoring is mandatory for a deeper insight into bacterial colony dynamics. Several methodologies that enable high-throughput data collection have been developed, such as microscopy-based techniques coupled with image analysis and OD-based measurements in microplate readers. This research paper provides an overview of non-invasive in situ techniques to monitor bacterial colonies in solid (model) food and emphasizes their advantages and inconveniences in terms of accuracy, performance and output information.

Tracking single-cells in overcrowded bacterial colonies

Cell tracking enables data extraction from timelapse "cell movies" and promotes modeling biological processes at the single-cell level. We introduce a new fully automated computational strategy to track accurately cells across frames in time-lapse movies. Our method is based on a dynamic neighborhoods formation and matching approach, inspired by motion estimation algorithms for video compression. Moreover, it exploits "divide and conquer" opportunities to solve effectively the challenging cells tracking problem in overcrowded bacterial colonies. Using cell movies generated by different labs we demonstrate that the accuracy of the proposed method remains very high (exceeds 97%) even when analyzing large overcrowded microbial colonies.

Trg-deficient Salmonella colonize quiescent tumor regions by exclusively penetrating or proliferating

Chemotherapeutics fail to effectively treat tumors because they cannot reach quiescent regions far from blood vessels. Motile Salmonella are an attractive delivery system that could break this therapeutic barrier. However, little is known about the dissemination and tissue penetration of individual bacteria in tumors after intravenous administration. We hypothesized that eliminating the Trg receptor would improve accumulation in tumor quiescence. To test this hypothesis, we deleted the trg gene from nonpathogenic Salmonella. To quantify individual bacterial behavior, we measured tissue penetration in a tumor-on-a-chip device and measured colony localization in mouse tumors using immunofluorescence. In tumors in vitro and in mice, trg(-) Salmonella penetrated farther into tissue than control bacteria. This difference in localization was caused by the inability to sense sugars in well perfused tissue. Three distinct bacterial phenotypes were observed: proliferating, penetrating, and inactive. Large proliferating colonies, containing more than 40% of individual bacteria, only formed less than 60μm from blood vessels. Small colonies, in comparison, were present both near (inactive) and far (penetrating) from vessels. The farthest was 361.2μm from a vessel, demonstrating the ability to target avascular regions. In addition, colonization was most pronounced in poorly vascularized tumor regions. We show that deletion of trg amplifies Salmonella accumulation in quiescent tumor regions, and, for the first time, identify biological processes that control bacterial distribution in tumors. Understanding how Salmonella penetrate tissue, target quiescence and specifically replicate in tumors are essential steps toward creating a tightly controlled, tunable bacterial therapy.

In situ examination of Lactobacillus brevis after exposure to an oxidizing disinfectant

Beer is a hostile environment for most microorganisms, but some lactic acid bacteria can grow in this environment. This is primarily because these organisms have developed the ability to grow in the presence of hops. It has been speculated that hop resistance is inversely correlated to resistance against oxidation, and this would have great impact on the use of various disinfectants in the brewing industry. In this study, we cultivated bacteria under aerobic and anaerobic conditions, and then investigated the in situ outgrowth of individual cells into microcolonies on de Man Rogosa Sharpe (MRS) agar after exposure to the oxidizing agent peracetic acid (PAA). An automated microscope stage allowed us to analyse a much larger number of cells over extended periods of incubation. After PAA treatment, the lag time increased markedly, and extensive variation in morphology, μmax as well as stress resistance was observed between and within the tested Lactobacillus brevis strains. The results suggest that aerobic cultivation increased the oxidative stress tolerance in Lactobacillus brevis. The results also show that dead cells are randomly distributed in a microcolony and the majority of non-growing individual cells do not stain with a membrane impermanent dye (Propidium iodide), which indicates that PAA may not destroy the plasma membrane. In conclusion, the developed microscopic analysis of individual cells on MRS agar can provides faster results and more details of cell physiology compared to the traditional CFU method.

Effect of microstructure on population growth parameters of Escherichia coli in gelatin-dextran systems

Current literature acknowledges the effect of food structure on bacterial dynamics. Most studies introduce this "structure" factor using a single gelling agent, resulting in a homogeneous environment, whereas in practice most food products are heterogeneous. Therefore, this study focuses on heterogeneous protein-polysaccharide mixtures, based on gelatin and dextran. These mixtures show phase separation, leading to a range of heterogeneous microstructures by adjusting relative concentrations of both gelling agents. Based on confocal microscope observations, the growth of Escherichia coli in gelatin-dextran systems was observed to occur in the dextran phase. To find a relation between microscopic and population behavior, growth experiments were performed in binary and singular gelatin-dextran systems and culture broth at 23.5°C, with or without adding 2.9% (wt/vol) NaCl. The Baranyi and Roberts growth model was fitted to the experimental data and parameter estimates were statistically compared. For salted binary mixtures, a decrease in the population maximum cell density was observed with increasing gelatin concentration. In this series, for one type of microstructure, i.e., a gelatin matrix phase with a disperse dextran phase, the maximum cell density decreased with decreasing percentage of dextran phase. However, this relation no longer held when other types of microstructure were observed. Compared to singular systems, adding a second gelling agent in the presence of NaCl had an effect on population lag phases and maximum cell densities. For unsalted media, the growth parameters of singular and binary mixtures were comparable. Introducing this information into mathematical models leads to more reliable growth predictions and enhanced food safety.

Unbiased estimation of mutation rates under fluctuating final counts

Estimation methods for mutation rates (or probabilities) in Luria-Delbrück fluctuation analysis usually assume that the final number of cells remains constant from one culture to another. We show that this leads to systematically underestimate the mutation rate. Two levels of information on final numbers are considered: either the coefficient of variation has been independently estimated, or the final number of cells in each culture is known. In both cases, unbiased estimation methods are proposed. Their statistical properties are assessed both theoretically and through Monte-Carlo simulation. As an application, the data from two well known fluctuation analysis studies on Mycobacterium tuberculosis are reexamined.

Use of enrichment real-time PCR to enumerate salmonella on chicken parts

Salmonella bacteria that survive cooking or that cross-contaminate other food during meal preparation and serving represent primary routes of consumer exposure to this pathogen from chicken. In the present study, enrichment real-time PCR (qPCR) was used to enumerate Salmonella bacteria that contaminate raw chicken parts at retail or that cross-contaminate cooked chicken during simulated meal preparation and serving. Whole raw chickens obtained at retail were partitioned into wings, breasts, thighs, and drumsticks using a sterilized knife and cutting board, which were then used to partition a cooked chicken breast to assess cross-contamination. After enrichment in buffered peptone water (400 ml, 8 h, 40°C, 80 rpm), subsamples were used for qPCR and cultural isolation of Salmonella. In some experiments, chicken parts were spiked with 0 to 3.6 log of Salmonella Typhimurium var. 5- to generate a standard curve for enumeration by qPCR. Of 10 raw chickens examined, 7 (70%) had one or more parts contaminated with Salmonella. Of 80 raw parts examined, 15 (19%) were contaminated with Salmonella. Of 20 cooked chicken parts examined, 2 (10%) were cross-contaminated with Salmonella. Predominant serotypes identified were Typhimurium (71%) and its variants (var. 5-, monophasic, and nonmotile) and Kentucky (18%). The number of Salmonella bacteria on contaminated parts ranged from one to two per part. Results of this study indicated that retail chicken parts examined were contaminated with low levels of Salmonella, which resulted in low levels of cross-contamination during simulated meal preparation and serving. Thus, if consumers properly handle and prepare the chicken, it should pose no or very low risk of consumer exposure to Salmonella.

Modeling bacterial population growth from stochastic single-cell dynamics

A 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.

Mass and density measurements of live and dead Gram-negative and Gram-positive bacterial populations

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.

Modelling dynamics of plasmid-gene mediated antimicrobial resistance in enteric bacteria using stochastic differential equations

The ubiquitous commensal bacteria harbour genes of antimicrobial resistance (AMR), often on conjugative plasmids. Antimicrobial use in food animals subjects their enteric commensals to antimicrobial pressure. A fraction of enteric Escherichia coli in cattle exhibit plasmid-gene mediated AMR to a third-generation cephalosporin ceftiofur. We adapted stochastic differential equations with diffusion approximation (a compartmental stochastic mathematical model) to research the sources and roles of stochasticity in the resistance dynamics, both during parenteral antimicrobial therapy and in its absence. The results demonstrated that demographic stochasticity among enteric E. coli in the occurrence of relevant events was important for the AMR dynamics only when bacterial numbers were depressed during therapy. However, stochasticity in the parameters of enteric E. coli ecology, whether externally or intrinsically driven, contributed to a wider distribution of the resistant E. coli fraction, both during therapy and in its absence, with stochasticities in individual parameters interacting in their contribution.

Combining individual-based modeling and food microenvironment descriptions to predict the growth of Listeria monocytogenes on smear soft cheese

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.