Gary C. Barker

The formation of Staphylococcus aureus enterotoxin in food environments and advances in risk assessment

The recent finding that the formation of staphylococcal enterotoxins in food is very different from that in cultures of pure Staphylococcus aureus sheds new light on, and brings into question, traditional microbial risk assessment methods based on planktonic liquid cultures. In fact, most bacteria in food appear to be associated with surfaces or tissues in various ways, and interaction with other bacteria through molecular signaling is prevalent. Nowadays it is well established that there are significant differences in the behavior of bacteria in the planktonic state and immobilized bacteria found in multicellular communities. Thus, in order to improve the production of high-quality, microbiologically safe food for human consumption, in situ data on enterotoxin formation in food environments are required to complement existing knowledge on the growth and survivability of S. aureus. This review focuses on enterotoxigenic S. aureus and describes recent findings related to enterotoxin formation in food environments, and ways in which risk assessment can take into account virulence behavior. An improved understanding of how environmental factors affect the expression of enterotoxins in foods will enable us to formulate new strategies for improved food safety.

Strengths and weaknesses of Monte Carlo simulation models and Bayesian belief networks in microbial risk assessment

We discuss different aspects of farm-to-fork risk assessment from a modelling perspective. Stochastic simulation models as they are presented today represent a mathematical representation of nature. In food safety risk assessment, a common modelling approach consists of a logic chain beginning at the source of the hazard and ending with the unwanted consequences of interest. This farm-to-fork approach usually begins with the hazard on the farm, sometimes with different compartments presenting different parts of the production chain, and ends with the dose received by the consumer or in case a dose response model is available the number of cases of illness. These models are typically implemented as Monte Carlo simulations, which are unidirectional in nature, and the link between statistics and simulation model is not interactive. A possible solution could be the use of Bayesian belief networks (BBNs) and this paper tries to discuss in an intuitive way the possibilities of using BBNs as an alternative for Monte Carlo modelling. An inventory is made of the strengths and weaknesses of both approaches, and an example is given showing an additional use of BBNs in biotracing problems.

Modelling Salmonella concentration throughout the pork supply chain by considering growth and survival in fluctuating conditions of temperature, pH and aw

We aim to predict the population density of Salmonella spp. through the pork supply chain under dynamic environmental conditions (pH, a(w) and temperature) that fluctuate from growth to survival/slow inactivation. To do this, the dependence of the probability of growth, and of the growth and inactivation rate on the temperature, pH and a(w) were modelled. Probabilistic and kinetic measurements, i.e. growth and survival curves, were collected from the ComBase database (www.combase.cc). Conditions at which selected data used to fit the models were generated covered wide ranges that are relevant to the pork supply chain. Probabilistic and kinetic models were combined to give predictions on the concentration of Salmonella spp. at any stage of the pork supply chain under fluctuating pH, a(w) and/or temperature. Models were implemented in a user-friendly computing tool freely available from http://www.ifr.ac.uk/safety/SalmonellaPredictions/. This program provides estimates on the population dynamics of Salmonella spp. at any stage of the pork supply chain and its predictive performance has been validated in several pork products.

Random sequential adsorption of lattice shapes onto a square lattice

We present computer simulation results for Random Sequential Adsorption. Symmetric and asymmetric lattice shapes, and their mixtures, have been irreversibly adsorbed onto a square lattice until no further adsorption events can take place. The adsorption statistics and kinetics are dependent on the shapes of the adsorbing particles. Two regimes of adsorption kinetics have been identified and the configuration of unoccupied lattice sites has been investigated using two stage Random Sequential Adsorption.

A solitary wave model for sedimentation in colloidal suspensions

The Burgers equation is used to model sedimentation of colloidal suspensions in terms of solitary waves. The importance of diffusive terms is highlighted. Analytic expressions are given for the distribution of dispersed material and the wavefront velocities are calculated as functions of time. An explanation of observed sedimentation behaviour is provided along with a discussion of the important features of real sedimenting systems.

Can a participatory approach contribute to food chain risk analysis

We consider food chain risks and specifically address stakeholder participation in the risk analysis process. We combine social and natural science perspectives to explore the participation process in relation to food risks and, in particular, to consider how some specific participation processes might be scientifically evaluated and how stakeholder participation in general might be incorporated into food risk decision making. We have built considerations based on three large integrative case studies that examine aspects of participatory processes. Here we use the case studies collectively to illustrate observations and beliefs concerning the nature of the interaction of stakeholders with established quantitative risk methodologies. This account is not supported by any large volume of analysis. The views in the report are expressed in relation to an accepted risk analysis framework and also with respect to probabilistic modeling of risks and are illustrated where possible with anecdotal reports of actual case study events.

Model suspensions of deformable particles

A collection of interacting transmutable shapes on a two dimensional square lattice are used to model a suspension of deformable droplets. All the shapes are members of a predetermined set of shapes and configurations of many particles are obtained from a Monte Carlo scheme which combines position and shape displacements. The structure and shape composition of the suspension are measured as a function of the dispersed phase volume fraction. The results for a suspension with hard core exclusion interactions between droplets show the general characteristic features associated with a fluid of macroparticles with ‘soft’ interactions. But, anomalous behaviour is observed for dense suspensions of deformable particles with second nearest neighbour interactions.

A continuum model for the growth of bacterial colonies on a surface

A model for the spatio-temporal growth of a bacterial colony on the flat surface of a solid medium is introduced based on a reaction-diffusion equation with vertical and lateral growth components. If the colony height is restricted to some maximum value, the colony morphology corresponds to solitary wave propagation in the radial direction. However, if colony growth is flux limited by the diffusion of initially separated components into the colony, vertical colony growth results from a steady state, finite sized reaction zone within the colony. In the flux-limited growth regime a more general colony morphology is obtained with constant velocity propagation of the colony radius and central height in qualitative agreement with experiment.

Food colloid science and the art of computer simulation

Abstract This article contains an elementary review of modern computer simulation methods and an introduction to their applications to problems in the structure and dynamics of food colloids. The review includes sections on Monte Carlo methods, dynamic algorithms and packing simulations. In each case the object is to explain the basic principles of the method and its relation to the underlying physics, and to clarify the computational process without presenting any technical details of numerical methods or of particular implementations which can be found elsewhere. The computational schemes are illustrated by recent examples of their applications to systems which include models of emulsions, foams, aggregates and sediments. Within the area of colloid science computer simulations provide an invaluable complement to analytical theories and experiments. Possible advances and extensions of the computational approach are indicated in the discussion and the review is completed by a small glossary of computational terms which may prove useful to the non-specialist.

Time Series Analysis of the Bacillus subtilis Sporulation Network Reveals Low Dimensional Chaotic Dynamics

Chaotic behavior refers to a behavior which, albeit irregular, is generated by an underlying deterministic process. Therefore, a chaotic behavior is potentially controllable. This possibility becomes practically amenable especially when chaos is shown to be low-dimensional, i.e., to be attributable to a small fraction of the total systems components. In this case, indeed, including the major drivers of chaos in a system into the modeling approach allows us to improve predictability of the systems dynamics. Here, we analyzed the numerical simulations of an accurate ordinary differential equation model of the gene network regulating sporulation initiation in Bacillus subtilis to explore whether the non-linearity underlying time series data is due to low-dimensional chaos. Low-dimensional chaos is expectedly common in systems with few degrees of freedom, but rare in systems with many degrees of freedom such as the B. subtilis sporulation network. The estimation of a number of indices, which reflect the chaotic nature of a system, indicates that the dynamics of this network is affected by deterministic chaos. The neat separation between the indices obtained from the time series simulated from the model and those obtained from time series generated by Gaussian white and colored noise confirmed that the B. subtilis sporulation network dynamics is affected by low dimensional chaos rather than by noise. Furthermore, our analysis identifies the principal driver of the networks chaotic dynamics to be sporulation initiation phosphotransferase B (Spo0B). We then analyzed the parameters and the phase space of the system to characterize the instability points of the network dynamics, and, in turn, to identify the ranges of values of Spo0B and of the other drivers of the chaotic dynamics, for which the whole system is highly sensitive to minimal perturbation. In summary, we described an unappreciated source of complexity in the B. subtilis sporulation network by gathering evidence for the chaotic behavior of the system, and by suggesting candidate molecules driving chaos in the system. The results of our chaos analysis can increase our understanding of the intricacies of the regulatory network under analysis, and suggest experimental work to refine our behavior of the mechanisms underlying B. subtilis sporulation initiation control.