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

Abstract

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...

Modeling microbial growth and dynamics

“…However, Monte Carlo simulation approaches do usually not operate at the level of individual cells, but at more aggregated levels. In the example of Listeria monocytogenes on soft cheese, an individual-based approach was chosen, because this pathogen contaminates the cheese already at very low cell numbers (Ferrier et al 2013). This individual-based model was able to account for individual cell lag times and single-cell growth probability (Ferrier et al 2013).…”

“…In the example of Listeria monocytogenes on soft cheese, an individual-based approach was chosen, because this pathogen contaminates the cheese already at very low cell numbers (Ferrier et al 2013). This individual-based model was able to account for individual cell lag times and single-cell growth probability (Ferrier et al 2013). Individualbased models can also be used to study the underlying mechanism of the lag phase of microbial growth (Prats et al 2006).…”

Safety and quality assessment of ready-to-eat pork products in the cold chain

“…For lag time, the prediction is larger than the estimated one and therefore unsafe. The simulation of the lag time is complex in predictive microbiology as it depends on many factors (Swinnen et al, 2004;Ferrier et al, 2013). The estimations of l opt and lag min with at temperature of 8°C allowed the estimation of the growth parameters in other temperature conditions.…”

Challenges of biofilm control and utilization: lessons from mathematical modelling

“…The biofilm models are either stochastic [49,59,68,69], taking into account a certain degree of randomness of biological processes, or deterministic [41,43,54], if the stochasticity analysis is not needed to answer a particular question. They can be individual-based [49,[60][61][62]68,77], where each bacterial cell is considered as an entity, or mesoscopic [42,53,70], where an entity of interest is a whole colony or a microcolony of cells, and a single event may be for example population doubling. The models developed can focus on describing the biofilm at the scale of the whole population, or at the level of the individual cells, taking into account the details of cell structure and how it affects its behaviour [75].…”

“…These include QS [37][38][39][40][41][42][43], effects of multi-species interactions [44][45][46], antimicrobial resistance [47] or the mechanical properties of the extracellular matrix (ECM) [48]. Second, mathematical models are routinely used to inform strategies to prevent or promote biofilm formation in specific situations relevant to, e.g., food and water security [27,49] or biofuel production [30,50].…”