From a discrete to a continuum model of cell dynamics in one dimension

Multiscale modeling is emerging as one of the key challenges in mathematical biology. However, the recent rapid increase in the number of modeling methodologies being used to describe cell populations has raised a number of interesting questions. For example, at the cellular scale, how can the appropriate discrete cell-level model be identified in a given context? Additionally, how can the many phenomenological assumptions used in the derivation of models at the continuum scale be related to individual cell behavior? In order to begin to address such questions, we consider a discrete one-dimensional cell-based model in which cells are assumed to interact via linear springs. From the discrete equations of motion, the continuous Rouse [P. E. Rouse, J. Chem. Phys. 21, 1272 (1953)] model is obtained. This formalism readily allows the definition of a cell number density for which a nonlinear "fast" diffusion equation is derived. Excellent agreement is demonstrated between the continuum and discrete models. Subsequently, via the incorporation of cell division, we demonstrate that the derived nonlinear diffusion model is robust to the inclusion of more realistic biological detail. In the limit of stiff springs, where cells can be considered to be incompressible, we show that cell velocity can be directly related to cell production. This assumption is frequently made in the literature but our derivation places limits on its validity. Finally, the model is compared with a model of a similar form recently derived for a different discrete cell-based model and it is shown how the different diffusion coefficients can be understood in terms of the underlying assumptions about cell behavior in the respective discrete models.

Phylogenetic mixture models can reduce node-density artifacts

We investigate the performance of phylogenetic mixture models in reducing a well-known and pervasive artifact of phylogenetic inference known as the node-density effect, comparing them to partitioned analyses of the same data. The node-density effect refers to the tendency for the amount of evolutionary change in longer branches of phylogenies to be underestimated compared to that in regions of the tree where there are more nodes and thus branches are typically shorter. Mixture models allow more than one model of sequence evolution to describe the sites in an alignment without prior knowledge of the evolutionary processes that characterize the data or how they correspond to different sites. If multiple evolutionary patterns are common in sequence evolution, mixture models may be capable of reducing node-density effects by characterizing the evolutionary processes more accurately. In gene-sequence alignments simulated to have heterogeneous patterns of evolution, we find that mixture models can reduce node-density effects to negligible levels or remove them altogether, performing as well as partitioned analyses based on the known simulated patterns. The mixture models achieve this without knowledge of the patterns that generated the data and even in some cases without specifying the full or true model of sequence evolution known to underlie the data. The latter result is especially important in real applications, as the true model of evolution is seldom known. We find the same patterns of results for two real data sets with evidence of complex patterns of sequence evolution: mixture models substantially reduced node-density effects and returned better likelihoods compared to partitioning models specifically fitted to these data. We suggest that the presence of more than one pattern of evolution in the data is a common source of error in phylogenetic inference and that mixture models can often detect these patterns even without prior knowledge of their presence in the data. Routine use of mixture models alongside other approaches to phylogenetic inference may often reveal hidden or unexpected patterns of sequence evolution and can improve phylogenetic inference.

Antiviral effects of antisense morpholino oligomers in murine coronavirus infection models

The recent emergence of novel pathogenic human and animal coronaviruses has highlighted the need for antiviral therapies that are effective against a spectrum of these viruses. We have used several strains of murine hepatitis virus (MHV) in cell culture and in vivo in mouse models to investigate the antiviral characteristics of peptide-conjugated antisense phosphorodiamidate morpholino oligomers (P-PMOs). Ten P-PMOs directed against various target sites in the viral genome were tested in cell culture, and one of these (5TERM), which was complementary to the 5' terminus of the genomic RNA, was effective against six strains of MHV. Further studies were carried out with various arginine-rich peptides conjugated to the 5TERM PMO sequence in order to evaluate efficacy and toxicity and thereby select candidates for in vivo testing. In uninfected mice, prolonged P-PMO treatment did not result in weight loss or detectable histopathologic changes. 5TERM P-PMO treatment reduced viral titers in target organs and protected mice against virus-induced tissue damage. Prophylactic 5TERM P-PMO treatment decreased the amount of weight loss associated with infection under most experimental conditions. Treatment also prolonged survival in two lethal challenge models. In some cases of high-dose viral inoculation followed by delayed treatment, 5TERM P-PMO treatment was not protective and increased morbidity in the treated group, suggesting that P-PMO may cause toxic effects in diseased mice that were not apparent in the uninfected animals. However, the strong antiviral effect observed suggests that with further development, P-PMO may provide an effective therapeutic approach against a broad range of coronavirus infections.

In-service and pre-service teachers' opinion on the use of models in teaching chemistry

The presented study examined the opinion of in-service and prospective chemistry teachers about the importance of usage of molecular and crystal models in secondary-level school practice, and investigated some of the reasons for their (non-) usage. The majority of participants stated that the use of models plays an important role in chemistry education and that they would use them more often if the circumstances were more favourable. Many teachers claimed that three-dimensional (3d) models are still not available in sufficient number at their schools; they also pointed to the lack of available computer facilities during chemistry lessons. The research revealed that, besides the inadequate material circumstances, less than one third of participants are able to use simple (freeware) computer programs for drawing molecular structures and their presentation in virtual space; however both groups of teachers expressed the willingness to improve their knowledge in the subject area. The investigation points to several actions which could be undertaken to improve the current situation.

LIPGENE food-exchange model for alteration of dietary fat quantity and quality in free-living participants from eight European countries

Controlled human intervention trials are required to confirm the hypothesis that dietary fat quality may influence insulin action. The aim was to develop a food-exchange model, suitable for use in free-living volunteers, to investigate the effects of four experimental diets distinct in fat quantity and quality: high SFA (HSFA); high MUFA (HMUFA) and two low-fat (LF) diets, one supplemented with 1.24g EPA and DHA/d (LFn-3). A theoretical food-exchange model was developed. The average quantity of exchangeable fat was calculated as the sum of fat provided by added fats (spreads and oils), milk, cheese, biscuits, cakes, buns and pastries using data from the National Diet and Nutrition Survey of UK adults. Most of the exchangeable fat was replaced by specifically designed study foods. Also critical to the model was the use of carbohydrate exchanges to ensure the diets were isoenergetic. Volunteers from eight centres across Europe completed the dietary intervention. Results indicated that compositional targets were largely achieved with significant differences in fat quantity between the high-fat diets (39.9 (SEM 0.6) and 38.9 (SEM 0.51) percentage energy (%E) from fat for the HSFA and HMUFA diets respectively) and the low-fat diets (29.6 (SEM 0.6) and 29.1 (SEM 0.5) %E from fat for the LF and LFn-3 diets respectively) and fat quality (17.5 (SEM 0.3) and 10.4 (SEM 0.2) %E front SFA and 12.7 (SEM 0.3) and 18.7 (SEM 0.4) %E MUFA for the HSFA and HMUFA diets respectively). In conclusion, a robust, flexible food-exchange model was developed and implemented successfully in the LIPGENE dietary intervention trial.

Nonlinear system identification using particle swarm optimisation tuned radial basis function models

A novel particle swarm optimisation (PSO) tuned radial basis function (RBF) network model is proposed for identification of non-linear systems. At each stage of orthogonal forward regression (OFR) model construction process, PSO is adopted to tune one RBF unit's centre vector and diagonal covariance matrix by minimising the leave-one-out (LOO) mean square error (MSE). This PSO aided OFR automatically determines how many tunable RBF nodes are sufficient for modelling. Compared with the-state-of-the-art local regularisation assisted orthogonal least squares algorithm based on the LOO MSE criterion for constructing fixed-node RBF network models, the PSO tuned RBF model construction produces more parsimonious RBF models with better generalisation performance and is often more efficient in model construction. The effectiveness of the proposed PSO aided OFR algorithm for constructing tunable node RBF models is demonstrated using three real data sets.

Identification of nonlinear systems using generalized kernel models

Nonlinear system identification is considered using a generalized kernel regression model. Unlike the standard kernel model, which employs a fixed common variance for all the kernel regressors, each kernel regressor in the generalized kernel model has an individually tuned diagonal covariance matrix that is determined by maximizing the correlation between the training data and the regressor using a repeated guided random search based on boosting optimization. An efficient construction algorithm based on orthogonal forward regression with leave-one-out (LOO) test statistic and local regularization (LR) is then used to select a parsimonious generalized kernel regression model from the resulting full regression matrix. The proposed modeling algorithm is fully automatic and the user is not required to specify any criterion to terminate the construction procedure. Experimental results involving two real data sets demonstrate the effectiveness of the proposed nonlinear system identification approach.

Ratio selection for classification models

This paper is concerned with the selection of inputs for classification models based on ratios of measured quantities. For this purpose, all possible ratios are built from the quantities involved and variable selection techniques are used to choose a convenient subset of ratios. In this context, two selection techniques are proposed: one based on a pre-selection procedure and another based on a genetic algorithm. In an example involving the financial distress prediction of companies, the models obtained from ratios selected by the proposed techniques compare favorably to a model using ratios usually found in the financial distress literature.

Wigner Transport models of the electron-phonon kinetics in quantum wires

Two quantum-kinetic models of ultrafast electron transport in quantum wires are derived from the generalized electron-phonon Wigner equation. The various assumptions and approximations allowing one to find closed equations for the reduced electron Wigner function are discussed with an emphasis on their physical relevance. The models correspond to the Levinson and Barker-Ferry equations, now generalized to account for a space-dependent evolution. They are applied to study the quantum effects in the dynamics of an initial packet of highly nonequilibrium carriers, locally generated in the wire. The properties of the two model equations are compared and analyzed.

Parallel implementation and one year experiments with the Danish Eulerian Model

Large scale air pollution models are powerful tools, designed to meet the increasing demand in different environmental studies. The atmosphere is the most dynamic component of the environment, where the pollutants can be moved quickly on far distnce. Therefore the air pollution modeling must be done in a large computational domain. Moreover, all relevant physical, chemical and photochemical processes must be taken into account. In such complex models operator splitting is very often applied in order to achieve sufficient accuracy as well as efficiency of the numerical solution. The Danish Eulerian Model (DEM) is one of the most advanced such models. Its space domain (4800 × 4800 km) covers Europe, most of the Mediterian and neighboring parts of Asia and the Atlantic Ocean. Efficient parallelization is crucial for the performance and practical capabilities of this huge computational model. Different splitting schemes, based on the main processes mentioned above, have been implemented and tested with respect to accuracy and performance in the new version of DEM. Some numerical results of these experiments are presented in this paper.

Intelligent agents and distributed models for cooperative task support

Purpose – To describe some research done, as part of an EPSRC funded project, to assist engineers working together on collaborative tasks. Design/methodology/approach – Distributed finite state modelling and agent techniques are used successfully in a new hybrid self-organising decision making system applied to collaborative work support. For the particular application, analysis of the tasks involved has been performed and these tasks are modelled. The system then employs a novel generic agent model, where task and domain knowledge are isolated from the support system, which provides relevant information to the engineers. Findings – The method is applied in the despatch of transmission commands within the control room of The National Grid Company Plc (NGC) – tasks are completed significantly faster when the system is utilised. Research limitations/implications – The paper describes a generic approach and it would be interesting to investigate how well it works in other applications. Practical implications – Although only one application has been studied, the methodology could equally be applied to a general class of cooperative work environments. Originality/value – One key part of the work is the novel generic agent model that enables the task and domain knowledge, which are application specific, to be isolated from the support system, and hence allows the method to be applied in other domains.

e-learning in project management using simulation models: a case study based on the replication of an experiment

Current e-learning systems are increasing their importance in higher education. However, the state of the art of e-learning applications, besides the state of the practice, does not achieve the level of interactivity that current learning theories advocate. In this paper, the possibility of enhancing e-learning systems to achieve deep learning has been studied by replicating an experiment in which students had to learn basic software engineering principles. One group learned these principles using a static approach, while the other group learned the same principles using a system-dynamics-based approach, which provided interactivity and feedback. The results show that, quantitatively, the latter group achieved a better understanding of the principles; furthermore, qualitatively, they enjoyed the learning experience

A nutrient increment method for reducing bias in global biogeochemical models

Assimilation of physical variables into coupled physical/biogeochemical models poses considerable difficulties. One problem is that data assimilation can break relationships between physical and biological variables. As a consequence, biological tracers, especially nutrients, are incorrectly displaced in the vertical, resulting in unrealistic biogeochemical fields. To prevent this, we present the idea of applying an increment to the nutrient field within a data assimilating model to ensure that nutrient-potential density relationships are maintained within a water column during assimilation. After correcting the nutrients, it is assumed that other biological variables rapidly adjust to the corrected nutrient fields. We applied this method to a 17 year run of the 2° NEMO ocean-ice model coupled to the PlankTOM5 ecosystem model. Results were compared with a control with no assimilation, and with a model with physical assimilation but no nutrient increment. In the nutrient incrementing experiment, phosphate distributions were improved both at high latitudes and at the equator. At midlatitudes, assimilation generated unrealistic advective upwelling of nutrients within the boundary currents, which spread into the subtropical gyres resulting in more biased nutrient fields. This result was largely unaffected by the nutrient increment and is probably due to boundary currents being poorly resolved in a 2° model. Changes to nutrient distributions fed through into other biological parameters altering primary production, air-sea CO2 flux, and chlorophyll distributions. These secondary changes were most pronounced in the subtropical gyres and at the equator, which are more nutrient limited than high latitudes.

Ocean acoustic models for low frequency propagation in 2D and 3D environments

In this paper we are mainly concerned with the development of efficient computer models capable of accurately predicting the propagation of low-to-middle frequency sound in the sea, in axially symmetric (2D) and in fully 3D environments. The major physical features of the problem, i.e. a variable bottom topography, elastic properties of the subbottom structure, volume attenuation and other range inhomogeneities are efficiently treated. The computer models presented are based on normal mode solutions of the Helmholtz equation on the one hand, and on various types of numerical schemes for parabolic approximations of the Helmholtz equation on the other. A new coupled mode code is introduced to model sound propagation in range-dependent ocean environments with variable bottom topography, where the effects of an elastic bottom, of volume attenuation, surface and bottom roughness are taken into account. New computer models based on finite difference and finite element techniques for the numerical solution of parabolic approximations are also presented. They include an efficient modeling of the bottom influence via impedance boundary conditions, they cover wide angle propagation, elastic bottom effects, variable bottom topography and reverberation effects. All the models are validated on several benchmark problems and versus experimental data. Results thus obtained were compared with analogous results from standard codes in the literature.

Using continuous ground-based radar and lidar measurements for evaluating the representation of clouds in four operational models

The ability of four operational weather forecast models [ECMWF, Action de Recherche Petite Echelle Grande Echelle model (ARPEGE), Regional Atmospheric Climate Model (RACMO), and Met Office] to generate a cloud at the right location and time (the cloud frequency of occurrence) is assessed in the present paper using a two-year time series of observations collected by profiling ground-based active remote sensors (cloud radar and lidar) located at three different sites in western Europe (Cabauw. Netherlands; Chilbolton, United Kingdom; and Palaiseau, France). Particular attention is given to potential biases that may arise from instrumentation differences (especially sensitivity) from one site to another and intermittent sampling. In a second step the statistical properties of the cloud variables involved in most advanced cloud schemes of numerical weather forecast models (ice water content and cloud fraction) are characterized and compared with their counterparts in the models. The two years of observations are first considered as a whole in order to evaluate the accuracy of the statistical representation of the cloud variables in each model. It is shown that all models tend to produce too many high-level clouds, with too-high cloud fraction and ice water content. The midlevel and low-level cloud occurrence is also generally overestimated, with too-low cloud fraction but a correct ice water content. The dataset is then divided into seasons to evaluate the potential of the models to generate different cloud situations in response to different large-scale forcings. Strong variations in cloud occurrence are found in the observations from one season to the same season the following year as well as in the seasonal cycle. Overall, the model biases observed using the whole dataset are still found at seasonal scale, but the models generally manage to well reproduce the observed seasonal variations in cloud occurrence. Overall, models do not generate the same cloud fraction distributions and these distributions do not agree with the observations. Another general conclusion is that the use of continuous ground-based radar and lidar observations is definitely a powerful tool for evaluating model cloud schemes and for a responsive assessment of the benefit achieved by changing or tuning a model cloud