A process-based, stage-structured model of potato cyst nematode population dynamics: Effects of temperature and resistance.

Potato cyst nematodes (PCN) are responsible for large losses in potato yields in many of the world's potato-growing regions. As soil temperatures increase due to climate change, there is potential for faster growth rates of PCN, allowing development of multiple generations in a growing season. We develop a process-based temperature-dependent model representing the life cycle of Globodera pallida, comprising juvenile, adult and cyst/diapause stages. To incorporate variability in the amount of time spent in each stage caused by genetic/environmental variation, the model is based on a mix of ordinary differential equations (ODEs) with sub-stages, and delay differential equations (DDEs). The effect of climate change is incorporated through the influence of soil temperature on the rate of development and survival in the hatching and juvenile stages. The level of the plant resistance to PCN is incorporated via the proportion of juveniles which become adults. After comparing the model with field data we run simulations to explore the effects of temperature and resistance on PCN populations. We find that with higher temperatures and longer growing seasons multiple generations of PCN can develop within a season, provided any required diapause period is short. Despite this, we show that growing resistant potatoes is a very effective control strategy and planting potatoes with even moderate levels of resistance can counter the effects of climate change.

Modelling the emergent dynamics and major metabolites of the human colonic microbiota.

We present here a first attempt at modelling microbial dynamics in the human colon incorporating both uncertainty and adaptation. This is based on the development of a Monod-equation based, differential equation model, which produces computer simulations of the population dynamics and major metabolites of microbial communities from the human colon. To reduce the complexity of the system, we divide the bacterial community into 10 bacterial functional groups (BFGs) each distinguished by its substrate preferences, metabolic pathways and its preferred pH range. The model simulates the growth of a large number of bacterial strains and incorporates variation in microbiota composition between people, while also allowing succession and enabling adaptation to environmental changes. The model is shown to reproduce many of the observed changes in major phylogenetic groups and key metabolites such as butyrate, acetate and propionate in response to a one unit pH shift in experimental continuous flow fermentors inoculated with human faecal microbiota. Nevertheless, it should be regarded as a learning tool to be updated as our knowledge of bacterial groups and their interactions expands. Given the difficulty of accessing the colon, modelling can play an extremely important role in interpreting experimental data and predicting the consequences of dietary modulation.

pH feedback and phenotypic diversity within bacterial functional groups of the human gut.

Microbial diversity in the human colon is very high with apparently large functional redundancy such that within each bacterial functional group there are many coexisting strains. Modelling this mathematically is problematic since strains within a functional group are often competing for the same limited number of resources and therefore competitive exclusion theory predicts a loss of diversity over time. Here we investigate, through computer simulation, a fluctuation dependent mechanism for the promotion of diversity. A variable pH environment caused by acidic by-products of bacterial growth on a fluctuating substrate coupled with small differences in acid tolerance between strains promotes diversity under both equilibrium and far-from-equilibrium conditions. Under equilibrium conditions pH fluctuations and relative nonlinearity in pH limitation among strains combine to prevent complete competitive exclusion. Under far-from-equilibrium conditions, loss of diversity through extinctions is made more difficult because pH cycling leads to fluctuations in the competitive ranking of strains, thereby helping to equalise fitness. We assume a trade-off between acid tolerance and maximum growth rate so that our microbial system consists of strains ranging from specialists to generalists. By altering the magnitude of the effect of the system on its pH environment (e.g. the buffering capacity of the colon) and the pattern of incoming resource we explore the conditions that promote diversity.

Estimating the contribution of the porcine fecal core microbiota to metabolite production via mathematical modeling and in vitro fermentation.

The swine gut microbiota is a complex ecosystem found throughout the gastrointestinal tract, with multiple exchanges with the host and whose composition is linked to both external and internal factors, such as diet or breed. Diet, probiotic, or prebiotic interventions have been designed to boost beneficial host-microbiota interactions, such as the production of anti-inflammatory molecules, or the fermentation of otherwise undigested resources. In parallel, a smaller microbial population, shared among the same host species, independent of external or internal factors, has been described and defined as the "core microbiota." Therapies targeting the core microbiota could possibly lead to more precise and long-lasting effects. However, the metabolic role of the porcine core microbiota, especially in relation to the rest of the microbial community, is currently missing. We present here the first dynamic model of the porcine core microbiota, which we used to estimate the core-microbiota metabolite production and to forecast the effect of a synbiotic intervention targeting the core genera of the core microbiota. We developed a community model in which a total of 17 microbial groups were established based on culture-based information of representative species. First, the model parameters were estimated, and the resulting model simulations were compared favorably with in vitro experimentation. The model was then used to predict the microbial dynamics of the core and non-core members under different experimental conditions. Therefore, it was able to theorize the main-metabolite core microbiota contribution, hypothesizing that it could be mainly responsible for acetate and propionate, but not for butyrate production.IMPORTANCECurrently, little information is present in the literature to describe the generic metabolic role of the porcine core microbiota or to inform on the effect of interventions targeting the core genera. Moreover, both in vitro and in vivo experimentations aiming to explore the core microbiota dynamics are technically demanding, expensive, or restricted by ethical considerations. Modeling approaches can be used as an initial exploratory tool to develop hypotheses for targeted experimentation. Our mathematical model provides initial information on the microbial and metabolite dynamics of the core microbiota in relation to diet and therapeutic intervention.

Challenges in microbial ecology: building predictive understanding of community function and dynamics.

The importance of microbial communities (MCs) cannot be overstated. MCs underpin the biogeochemical cycles of the earth's soil, oceans and the atmosphere, and perform ecosystem functions that impact plants, animals and humans. Yet our ability to predict and manage the function of these highly complex, dynamically changing communities is limited. Building predictive models that link MC composition to function is a key emerging challenge in microbial ecology. Here, we argue that addressing this challenge requires close coordination of experimental data collection and method development with mathematical model building. We discuss specific examples where model-experiment integration has already resulted in important insights into MC function and structure. We also highlight key research questions that still demand better integration of experiments and models. We argue that such integration is needed to achieve significant progress in our understanding of MC dynamics and function, and we make specific practical suggestions as to how this could be achieved.

Screening for Chlamydia trachomatis infection is indicated for women under 30 using emergency contraception.

A total of 838 women attending a large family planning clinic in Scotland for emergency contraception were offered screening for Chlamydia trachomatis infection. 569 were screened using ligase chain reaction test in first void urine at the time of presenting for emergency contraception and were retested 1 or 2 weeks later. Women aged under 20 and over 30 years were significantly more likely to decline to be tested than women aged 20 to 30. The prevalence of chlamydia was 7.6% in woman aged 24 or less, 5.3% in women aged 25 to 29, and 1.2% in women aged 30 or more. Only two women (< 1%) who tested negative at the time of using EC were positive 1 or 2 weeks later. Women under age 30 who use EC should be offered screening for chlamydia infection and testing at the time they attend for EC is adequate to detect the great majority of infected women.