Modelling within-host spatiotemporal dynamics of invasive bacterial disease

Mechanistic determinants of bacterial growth, death, and spread within mammalian hosts cannot be fully resolved studying a single bacterial population. They are also currently poorly understood. Here, we report on the application of sophisticated experimental approaches to map spatiotemporal population dynamics of bacteria during an infection. We analyzed heterogeneous traits of simultaneous infections with tagged Salmonella enterica populations (wild-type isogenic tagged strains [WITS]) in wild-type and gene-targeted mice. WITS are phenotypically identical but can be distinguished and enumerated by quantitative PCR, making it possible, using probabilistic models, to estimate bacterial death rate based on the disappearance of strains through time. This multidisciplinary approach allowed us to establish the timing, relative occurrence, and immune control of key infection parameters in a true host-pathogen combination. Our analyses support a model in which shortly after infection, concomitant death and rapid bacterial replication lead to the establishment of independent bacterial subpopulations in different organs, a process controlled by host antimicrobial mechanisms. Later, decreased microbial mortality leads to an exponential increase in the number of bacteria that spread locally, with subsequent mixing of bacteria between organs via bacteraemia and further stochastic selection. This approach provides us with an unprecedented outlook on the pathogenesis of S. enterica infections, illustrating the complex spatial and stochastic effects that drive an infectious disease. The application of the novel method that we present in appropriate and diverse host-pathogen combinations, together with modelling of the data that result, will facilitate a comprehensive view of the spatial and stochastic nature of within-host dynamics. © 2008 Grant et al.

Microbead dynamics on quartz crystal microbalance at elevated amplitudes

The application of the Quartz Crystal Microbalance (QCM) for biochemical sensing is well known. However, utilizing the nonlinear response of the QCM at elevated amplitudes has received sporadic attention. This study presents results for QCM-analyte interaction that provide insight into the nonlinear dynamics of the QCM with attached analyte. In particular, interactions of the QCM with polystyrene microbeads physisorbed via self-assembled monolayer (SAM) were studied through experiments and modelling. It was found that the response of the QCM coupled to these surface adsorbents is anharmonic even at low oscillation amplitudes and that the nonlinear signals from such interactions are much higher than those for bare quartz. Therefore, these signals can potentially be used as sensitive signatures of adsorbents and their kinetics on the surface. ©2009 IEEE.

Transient Ventilation Dynamics Following a Change in Strength of a Point Source of Heat

We investigate the transient ventilation flow within a confined ventilated space, with high- and low-level openings, when the strength of a low-level point source of heat is changed instantaneously. The steady-flow regime in the space involves a turbulent buoyant plume, which rises from the point source to a well-mixed warm upper layer. The steady-state height of the interface between this layer and the lower layer of exterior fluid is independent of the heat flux, but the upper layer becomes progressively warmer with heat flux. New analogue laboratory experiments of the transient adjustment between steady states identify that if the heat flux is increased, the continuing plume propagates to the top of the room forming a new, warmer layer. This layer gradually deepens, and as the turbulent plume entrains fluid from the original warm layer, the original layer is gradually depleted and disappears, and a new steady state is established. In contrast, if the source buoyancy flux is decreased, the continuing plume is cooler than the original plume, so that on reaching the interface it is of intermediate density between the original warm layer and the external fluid. The plume supplies a new intermediate layer, which gradually deepens with the continuing flow. In turn, the original upper layer becomes depleted, both as a result of being vented through the upper opening of the space, but also due to some penetrative entrainment of this layer by the plume, as the plume overshoots the interface before falling back to supply the new intermediate layer. We develop quantitative models which are in good accord with our experimental data, by combining classical plume theory with models of the penetrative entrainment for the case of a decrease in heating. Typically, we find that the effect of penetrative entrainment on the density of the intruding layer is relatively weak, provided the change in source strength is sufficiently large. However, penetrative entrainment measurably increases the rate at which the depth of the draining layer decreases. We conclude with a discussion of the importance of these results for the control of naturally ventilated spaces.

Modelling nonlinear vehicle dynamics with neural networks

The nonlinear modelling ability of neural networks has been widely recognised as an effective tool to identify and control dynamic systems, with applications including nonlinear vehicle dynamics which this paper focuses on using multi-layer perceptron networks. Existing neural network literature does not detail some of the factors which effect neural network nonlinear modelling ability. This paper investigates into and concludes on required network size, structure and initial weights, considering results for networks of converged weights. The paper also presents an online training method and an error measure representing the network's parallel modelling ability over a range of operating conditions. Copyright © 2010 Inderscience Enterprises Ltd.

The design of an agent for modelling supply chain network dynamics

A useful insight into managerial decision making can be found from simulation of business systems, but existing work on simulation of supply chain behaviour has largely considered non-competitive chains. Where competitive agents have been examined, they have generally had a simple structure and been used for fundamental examination of stability and equilibria rather than providing practical guidance to managers. In this paper, a new agent for the study of competitive supply chain network dynamics is proposed. The novel features of the agent include the ability to select between competing vendors, distribute orders preferentially among many customers, manage production and inventory, and determine price based on competitive behaviour. The structure of the agent is related to existing business models and sufficient details are provided to allow implementation. The agent is tested to demonstrate that it recreates the main results of the existing modelling and management literature on supply chain dynamics. A brief exploration of competitive dynamics is given to confirm that the proposed agent can respond to competition. The results demonstrate that overall profitability for a supply chain network is maximised when businesses operate collectively. It is possible for an individual business to achieve higher profits by adopting a more competitive stance, but the consequence of this is that the overall profitability of the network is reduced. The agent will be of use for a broad range of studies on the long-run effect of management decisions on their network of suppliers and customers.