Hypersensitivity and growth adaptation of oestrogen-deprived MCF-7 human breast cancer cells

Background: Efficacy of endocrine therapy is compromised when human breast cancer cells circumvent imposed growth inhibition. The model of long-term oestrogen-deprived MCF-7 human breast cancer cells has suggested the mechanism results from hypersensitivity to low levels of residual oestrogen. Materials and methods: MCF-7 cells were maintained for up to 30 weeks in phenol-red-free medium and charcoal-stripped serum with 10-8 M 17-oestradiol and 10 g/ml insulin (stock 1), 10-8 M 17-oestradiol (stock 2), 10 g/ml insulin (stock 3) or no addition (stock 4). Results: Loss of growth response to oestrogen was observed only in stock 4 cells. Long-term maintenance with insulin in the absence of oestradiol (stock 3) resulted in raised oestrogen receptor alpha (ERlevels (measured by western immunoblotting) and development of hypersensitivity (assayed by oestrogen-responsive reporter gene induction and dose response to oestradiol for proliferation under serum-free conditions), but with no loss of growth response to oestrogen. Conclusion: Hypersensitivity can develop without any growth adaptation and therefore is not a prerequisite for loss of growth response in MCF-7 cells.

Assimilation of sea-ice concentration in a global climate model — physical and statistical aspects

We investigate the initialization of Northern-hemisphere sea ice in the global climate model ECHAM5/MPI-OM by assimilating sea-ice concentration data. The analysis updates for concentration are given by Newtonian relaxation, and we discuss different ways of specifying the analysis updates for mean thickness. Because the conservation of mean ice thickness or actual ice thickness in the analysis updates leads to poor assimilation performance, we introduce a proportional dependence between concentration and mean thickness analysis updates. Assimilation with these proportional mean-thickness analysis updates significantly reduces assimilation error both in identical-twin experiments and when assimilating sea-ice observations, reducing the concentration error by a factor of four to six, and the thickness error by a factor of two. To understand the physical aspects of assimilation errors, we construct a simple prognostic model of the sea-ice thermodynamics, and analyse its response to the assimilation. We find that the strong dependence of thermodynamic ice growth on ice concentration necessitates an adjustment of mean ice thickness in the analysis update. To understand the statistical aspects of assimilation errors, we study the model background error covariance between ice concentration and ice thickness. We find that the spatial structure of covariances is best represented by the proportional mean-thickness analysis updates. Both physical and statistical evidence supports the experimental finding that proportional mean-thickness updates are superior to the other two methods considered and enable us to assimilate sea ice in a global climate model using simple Newtonian relaxation.

A moving mesh approach for modelling avascular tumour growth

A key step in many numerical schemes for time-dependent partial differential equations with moving boundaries is to rescale the problem to a fixed numerical mesh. An alternative approach is to use a moving mesh that can be adapted to focus on specific features of the model. In this paper we present and discuss two different velocity-based moving mesh methods applied to a two-phase model of avascular tumour growth formulated by Breward et al. (2002) J. Math. Biol. 45(2), 125-152. Each method has one moving node which tracks the moving boundary. The first moving mesh method uses a mesh velocity proportional to the boundary velocity. The second moving mesh method uses local conservation of volume fraction of cells (masses). Our results demonstrate that these moving mesh methods produce accurate results, offering higher resolution where desired whilst preserving the balance of fluxes and sources in the governing equations.

An energy budget agent-based model of earthworm populations and its application to study the effects of pesticides

Earthworms are important organisms in soil communities and so are used as model organisms in environmental risk assessments of chemicals. However current risk assessments of soil invertebrates are based on short-term laboratory studies, of limited ecological relevance, supplemented if necessary by site-specific field trials, which sometimes are challenging to apply across the whole agricultural landscape. Here, we investigate whether population responses to environmental stressors and pesticide exposure can be accurately predicted by combining energy budget and agent-based models (ABMs), based on knowledge of how individuals respond to their local circumstances. A simple energy budget model was implemented within each earthworm Eisenia fetida in the ABM, based on a priori parameter estimates. From broadly accepted physiological principles, simple algorithms specify how energy acquisition and expenditure drive life cycle processes. Each individual allocates energy between maintenance, growth and/or reproduction under varying conditions of food density, soil temperature and soil moisture. When simulating published experiments, good model fits were obtained to experimental data on individual growth, reproduction and starvation. Using the energy budget model as a platform we developed methods to identify which of the physiological parameters in the energy budget model (rates of ingestion, maintenance, growth or reproduction) are primarily affected by pesticide applications, producing four hypotheses about how toxicity acts. We tested these hypotheses by comparing model outputs with published toxicity data on the effects of copper oxychloride and chlorpyrifos on E. fetida. Both growth and reproduction were directly affected in experiments in which sufficient food was provided, whilst maintenance was targeted under food limitation. Although we only incorporate toxic effects at the individual level we show how ABMs can readily extrapolate to larger scales by providing good model fits to field population data. The ability of the presented model to fit the available field and laboratory data for E. fetida demonstrates the promise of the agent-based approach in ecology, by showing how biological knowledge can be used to make ecological inferences. Further work is required to extend the approach to populations of more ecologically relevant species studied at the field scale. Such a model could help extrapolate from laboratory to field conditions and from one set of field conditions to another or from species to species.

Earthworm distribution and abundance predicted by a process-based model

Earthworms are significant ecosystem engineers and are an important component of the diet of many vertebrates and invertebrates, so the ability to predict their distribution and abundance would have wide application in ecology, conservation and land management. Earthworm viability is known to be affected by the availability and quality of food resources, soil water conditions and temperature, but has not yet been modelled mechanistically to link effects on individuals to field population responses. Here we present a novel model capable of predicting the effects of land management and environmental conditions on the distribution and abundance of Aporrectodea caliginosa, the dominant earthworm species in agroecosystems. Our process-based approach uses individual based modelling (IBM), in which each individual has its own energy budget. Individual earthworm energy budgets follow established principles of physiological ecology and are parameterised for A. caliginosa from experimental measurements under optimal conditions. Under suboptimal conditions (e.g. food limitation, low soil temperatures and water contents) reproduction is prioritised over growth. Good model agreement to independent laboratory data on individual cocoon production and growth of body mass, under variable feeding and temperature conditions support our representation of A. caliginosa physiology through energy budgets. Our mechanistic model is able to accurately predict A. caliginosa distribution and abundance in spatially heterogeneous soil profiles representative of field study conditions. Essential here is the explicit modelling of earthworm behaviour in the soil profile. Local earthworm movement responds to a trade-off between food availability and soil water conditions, and this determines the spatiotemporal distribution of the population in the soil profile. Importantly, multiple environmental variables can be manipulated simultaneously in the model to explore earthworm population exposure and effects to combinations of stressors. Potential applications include prediction of the population-level effects of pesticides and changes in soil management e.g. conservation tillage and climate change.

Resilience of rice (Oryzaspp.) pollen germination and tube growth to temperature stress

Resilience of rice cropping systems to potential global climate change will partly depend on temperature tolerance of pollen germination (PG) and tube growth (PTG). Germination of pollen of high temperature susceptible Oryza glaberrima Steud. (cv. CG14) and O. sativa L. ssp. indica (cv. IR64) and high temperature tolerant O. sativa ssp. aus (cv. N22), was assessed on a 5.6-45.4°C temperature gradient system. Mean maximum PG was 85% at 27°C with 1488 μm PTG at 25°C. The hypothesis that in each pollen grain, minimum temperature requirements (Tn) and maximum temperature limits (Tx) for germination operate independently was accepted by comparing multiplicative and subtractive probability models. The maximum temperature limit for PG in 50% of grains (Tx(50)) was lowest (29.8°C) in IR64 compared with CG14 (34.3°C) and N22 (35.6°C). Standard deviation (sx) of Tx was also low in IR64 (2.3°C) suggesting that the mechanism of IR64's susceptibility to high temperatures may relate to PG. Optimum germination temperatures and thermal times for 1mm PTG were not linked to tolerating high temperatures at anthesis. However, the parameters Tx(50) and sx in the germination model define new pragmatic criteria for successful and resilient PG, preferable to the more traditional cardinal (maximum and minimum) temperatures.

Modeling growth strategy in a biotechnology startup firm

The top managers of a biotechnology startup firm agreed to participate in a system dynamics modeling project to help them think about the firm's growth strategy. The article describes how the model was created and used to stimulate debate and discussion about growth management. The paper highlights several novel features about the process used for capturing management team knowledge. A heavy emphasis was placed on mapping the operating structure of the factory and distribution channels. Qualitative modeling methods (structural diagrams, descriptive variable names, and friendly algebra) were used to capture the management team's descriptions of the business. Simulation scenarios were crafted to stimulate debate about strategic issues such as capacity allocation, capacity expansion, customer recruitment, customer retention, and market growth, and to engage the management team in using the computer to design strategic scenarios. The article concludes with comments on the impact of the project.

In vitro colonic metabolism of coffee and chlorogenic acid results in selective changes in human faecal microbiota growth

Coffee is a relatively rich source of chlorogenic acids (CGA), which, like other polyphenols are postulated to exert preventative effects against cardiovascular disease and type-2 diabetes. As a considerable proportion of ingested CGA reaches the large intestine, CGA may be capable of exerting beneficial effects in the large gut. Here we utilise a stirred, anaerobic, pH controlled, batch culture fermentation model of the distal region of the colon in order to investigate the impact of coffee and CGA on the growth of the human faecal microbiota. Incubation of the coffee with the human faecal microbiota led to the rapid metabolism of CGA (4h) and the production of dihydrocaffeic acid and dihydroferulic acid, whilst caffeine remained un-metabolised. The coffee with the highest levels of CGA (p<0.05, relative to the other coffees) induced a significant increase in Bifidobacterium spp. relative to the control at 10 hours post exposure (p<0.05). Similarly, an equivalent quantity of CGA (80.8mg; matched with that in high CGA coffee) induced a significant increase in Bifidobacterium spp. (p<0.05). CGA alone also induced a significant increase in the Clostridium coccoides-Eubacterium rectale group (p<0.05). This selective metabolism and subsequent amplification of specific bacterial populations could be beneficial to host health.

A multi-model evaluation of aerosols over South Asia: common problems and possible causes

Atmospheric pollution over South Asia attracts special attention due to its effects on regional climate, water cycle and human health. These effects are potentially growing owing to rising trends of anthropogenic aerosol emissions. In this study, the spatio-temporal aerosol distributions over South Asia from seven global aerosol models are evaluated against aerosol retrievals from NASA satellite sensors and ground-based measurements for the period of 2000–2007. Overall, substantial underestimations of aerosol loading over South Asia are found systematically in most model simulations. Averaged over the entire South Asia, the annual mean aerosol optical depth (AOD) is underestimated by a range 15 to 44% across models compared to MISR (Multi-angle Imaging SpectroRadiometer), which is the lowest bound among various satellite AOD retrievals (from MISR, SeaWiFS (Sea-Viewing Wide Field-of-View Sensor), MODIS (Moderate Resolution Imaging Spectroradiometer) Aqua and Terra). In particular during the post-monsoon and wintertime periods (i.e., October–January), when agricultural waste burning and anthropogenic emissions dominate, models fail to capture AOD and aerosol absorption optical depth (AAOD) over the Indo–Gangetic Plain (IGP) compared to ground-based Aerosol Robotic Network (AERONET) sunphotometer measurements. The underestimations of aerosol loading in models generally occur in the lower troposphere (below 2 km) based on the comparisons of aerosol extinction profiles calculated by the models with those from Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) data. Furthermore, surface concentrations of all aerosol components (sulfate, nitrate, organic aerosol (OA) and black carbon (BC)) from the models are found much lower than in situ measurements in winter. Several possible causes for these common problems of underestimating aerosols in models during the post-monsoon and wintertime periods are identified: the aerosol hygroscopic growth and formation of secondary inorganic aerosol are suppressed in the models because relative humidity (RH) is biased far too low in the boundary layer and thus foggy conditions are poorly represented in current models, the nitrate aerosol is either missing or inadequately accounted for, and emissions from agricultural waste burning and biofuel usage are too low in the emission inventories. These common problems and possible causes found in multiple models point out directions for future model improvements in this important region.

Interactions between wind-blown snow redistribution and melt ponds in a coupled ocean–sea ice model

Introducing a parameterization of the interactions between wind-driven snow depth changes and melt pond evolution allows us to improve large scale models. In this paper we have implemented an explicit melt pond scheme and, for the first time, a wind dependant snow redistribution model and new snow thermophysics into a coupled ocean–sea ice model. The comparison of long-term mean statistics of melt pond fractions against observations demonstrates realistic melt pond cover on average over Arctic sea ice, but a clear underestimation of the pond coverage on the multi-year ice (MYI) of the western Arctic Ocean. The latter shortcoming originates from the concealing effect of persistent snow on forming ponds, impeding their growth. Analyzing a second simulation with intensified snow drift enables the identification of two distinct modes of sensitivity in the melt pond formation process. First, the larger proportion of wind-transported snow that is lost in leads directly curtails the late spring snow volume on sea ice and facilitates the early development of melt ponds on MYI. In contrast, a combination of higher air temperatures and thinner snow prior to the onset of melting sometimes make the snow cover switch to a regime where it melts entirely and rapidly. In the latter situation, seemingly more frequent on first-year ice (FYI), a smaller snow volume directly relates to a reduced melt pond cover. Notwithstanding, changes in snow and water accumulation on seasonal sea ice is naturally limited, which lessens the impacts of wind-blown snow redistribution on FYI, as compared to those on MYI. At the basin scale, the overall increased melt pond cover results in decreased ice volume via the ice-albedo feedback in summer, which is experienced almost exclusively by MYI.

SHIMMER (1.0): a novel mathematical model for microbial and biogeochemical dynamics in glacier forefield ecosystems

SHIMMER (Soil biogeocHemIcal Model for Microbial Ecosystem Response) is a new numerical modelling framework designed to simulate microbial dynamics and biogeochemical cycling during initial ecosystem development in glacier forefield soils. However, it is also transferable to other extreme ecosystem types (such as desert soils or the surface of glaciers). The rationale for model development arises from decades of empirical observations in glacier forefields, and enables a quantitative and process focussed approach. Here, we provide a detailed description of SHIMMER, test its performance in two case study forefields: the Damma Glacier (Switzerland) and the Athabasca Glacier (Canada) and analyse sensitivity to identify the most sensitive and unconstrained model parameters. Results show that the accumulation of microbial biomass is highly dependent on variation in microbial growth and death rate constants, Q10 values, the active fraction of microbial biomass and the reactivity of organic matter. The model correctly predicts the rapid accumulation of microbial biomass observed during the initial stages of succession in the forefields of both the case study systems. Primary production is responsible for the initial build-up of labile substrate that subsequently supports heterotrophic growth. However, allochthonous contributions of organic matter, and nitrogen fixation, are important in sustaining this productivity. The development and application of SHIMMER also highlights aspects of these systems that require further empirical research: quantifying nutrient budgets and biogeochemical rates, exploring seasonality and microbial growth and cell death. This will lead to increased understanding of how glacier forefields contribute to global biogeochemical cycling and climate under future ice retreat.

Kazakhstan: long-term economic growth and the role of the oil sector

This study combines a narrative and modelling framework to analyse the development of Kazakhstan’s oil sector since its takeoff following separation from the USSR. As in the case of other emerging or transitional countries with large natural resource endowments, a key question is whether the exploitation of the natural resource is a benefit to longer term economic development: is it a curse, a blessing – or neither? Narrative evidence suggests that the establishment of good governance, in terms of institutions and policies, provides a background to sound long-term development, especially if combined with the development of sectors outside the natural resource sector, for example diversification into manufacturing and services, often through attracting FDI. The narrative is supported by econometric modelling of the relationship between domestic output, overseas output and exports of oil, which finds in favour of a sustained positive effect of oil exports on GDP. The model then provides a basis for projection of the growth in GDP given a consensus view of likely developments in the oil price.

Interaction of plant growth regulators and reactive oxygen species to regulate petal senescence in wallflowers (Erysimum linifolium)

Background In many species floral senescence is coordinated by ethylene. Endogenous levels rise, and exogenous application accelerates senescence. Furthermore, floral senescence is often associated with increased reactive oxygen species, and is delayed by exogenously applied cytokinin. However, how these processes are linked remains largely unresolved. Erysimum linifolium (wallflower) provides an excellent model for understanding these interactions due to its easily staged flowers and close taxonomic relationship to Arabidopsis. This has facilitated microarray analysis of gene expression during petal senescence and provided gene markers for following the effects of treatments on different regulatory pathways. Results In detached Erysimum linifolium (wallflower) flowers ethylene production peaks in open flowers. Furthermore senescence is delayed by treatments with the ethylene signalling inhibitor silver thiosulphate, and accelerated with ethylene released by 2-chloroethylphosphonic acid. Both treatments with exogenous cytokinin, or 6-methyl purine (which is an inhibitor of cytokinin oxidase), delay petal senescence. However, treatment with cytokinin also increases ethylene biosynthesis. Despite the similar effects on senescence, transcript abundance of gene markers is affected differentially by the treatments. A significant rise in transcript abundance of WLS73 (a putative aminocyclopropanecarboxylate oxidase) was abolished by cytokinin or 6-methyl purine treatments. In contrast, WFSAG12 transcript (a senescence marker) continued to accumulate significantly, albeit at a reduced rate. Silver thiosulphate suppressed the increase in transcript abundance both of WFSAG12 and WLS73. Activity of reactive oxygen species scavenging enzymes changed during senescence. Treatments that increased cytokinin levels, or inhibited ethylene action, reduced accumulation of hydrogen peroxide. Furthermore, although auxin levels rose with senescence, treatments that delayed early senescence did not affect transcript abundance of WPS46, an auxin-induced gene. Conclusions A model for the interaction between cytokinins, ethylene, reactive oxygen species and auxin in the regulation of floral senescence in wallflowers is proposed. The combined increase in ethylene and reduction in cytokinin triggers the initiation of senescence and these two plant growth regulators directly or indirectly result in increased reactive oxygen species levels. A fall in conjugated auxin and/or the total auxin pool eventually triggers abscission.

What controls the vertical distribution of aerosol? Relationships between process sensitivity in HadGEM3–UKCA and inter-model variation from AeroCom Phase II

The vertical profile of aerosol is important for its radiative effects, but weakly constrained by observations on the global scale, and highly variable among different models. To investigate the controlling factors in one particular model, we investigate the effects of individual processes in HadGEM3–UKCA and compare the resulting diversity of aerosol vertical profiles with the inter-model diversity from the AeroCom Phase II control experiment. In this way we show that (in this model at least) the vertical profile is controlled by a relatively small number of processes, although these vary among aerosol components and particle sizes. We also show that sufficiently coarse variations in these processes can produce a similar diversity to that among different models in terms of the global-mean profile and, to a lesser extent, the zonal-mean vertical position. However, there are features of certain models' profiles that cannot be reproduced, suggesting the influence of further structural differences between models. In HadGEM3–UKCA, convective transport is found to be very important in controlling the vertical profile of all aerosol components by mass. In-cloud scavenging is very important for all except mineral dust. Growth by condensation is important for sulfate and carbonaceous aerosol (along with aqueous oxidation for the former and ageing by soluble material for the latter). The vertical extent of biomass-burning emissions into the free troposphere is also important for the profile of carbonaceous aerosol. Boundary-layer mixing plays a dominant role for sea salt and mineral dust, which are emitted only from the surface. Dry deposition and below-cloud scavenging are important for the profile of mineral dust only. In this model, the microphysical processes of nucleation, condensation and coagulation dominate the vertical profile of the smallest particles by number (e.g. total CN  >  3 nm), while the profiles of larger particles (e.g. CN  >  100 nm) are controlled by the same processes as the component mass profiles, plus the size distribution of primary emissions. We also show that the processes that affect the AOD-normalised radiative forcing in the model are predominantly those that affect the vertical mass distribution, in particular convective transport, in-cloud scavenging, aqueous oxidation, ageing and the vertical extent of biomass-burning emissions.

Coupling an aerosol box model with one-dimensional flow: a tool for understanding observations of new particle formation events

Field observations of new particle formation and the subsequent particle growth are typically only possible at a fixed measurement location, and hence do not follow the temporal evolution of an air parcel in a Lagrangian sense. Standard analysis for determining formation and growth rates requires that the time-dependent formation rate and growth rate of the particles are spatially invariant; air parcel advection means that the observed temporal evolution of the particle size distribution at a fixed measurement location may not represent the true evolution if there are spatial variations in the formation and growth rates. Here we present a zero-dimensional aerosol box model coupled with one-dimensional atmospheric flow to describe the impact of advection on the evolution of simulated new particle formation events. Wind speed, particle formation rates and growth rates are input parameters that can vary as a function of time and location, using wind speed to connect location to time. The output simulates measurements at a fixed location; formation and growth rates of the particle mode can then be calculated from the simulated observations at a stationary point for different scenarios and be compared with the ‘true’ input parameters. Hence, we can investigate how spatial variations in the formation and growth rates of new particles would appear in observations of particle number size distributions at a fixed measurement site. We show that the particle size distribution and growth rate at a fixed location is dependent on the formation and growth parameters upwind, even if local conditions do not vary. We also show that different input parameters used may result in very similar simulated measurements. Erroneous interpretation of observations in terms of particle formation and growth rates, and the time span and areal extent of new particle formation, is possible if the spatial effects are not accounted for.