In this note, we consider a nonlinear diffusion equation with a bistable reaction term arising in population dynamics. Given a rather general initial data, we investigate its behavior for small times as the reaction coefficient tends to infinity: we prove a generation of interface property.

In this paper, we study the numerical approximation of a size-structured population model whose dependency on the environment is managed by the evolution of a vital resource. We show that this is a difficult task: some numerical methods are not suitable for a long-time integration. We analyze the reasons for the failure.

Two main approaches have been considered for modelling the dynamics of the SIS model on complex metapopulations, i.e, networks of populations connected by migratory flows whose configurations are described in terms of the connectivity distribution of nodes (patches) and the conditional probabilities of connections among classes of nodes sharing the same degree. In the first approach migration and transmission/recovery process alternate sequentially, and, in the second one, both processes occur simultaneously. Here we follow the second approach and give a necessary and sufficient condition for the instability of the disease-free equilibrium in generic networks under the assumption of limited (or frequency-dependent) transmission. Moreover, for uncorrelated networks and under the assumption of non-limited (or density-dependent) transmission, we give a bounding interval for the dominant eigenvalue of the Jacobian matrix of the model equations around the disease-free equilibrium. Finally, for this latter case, we study numerically the prevalence of the infection across the metapopulation as a function of the patch connectivity.

2D shallow water equations with depth-averaged k−ε model is considered. A meshless method based on multiquadric radial basis functions is described. This methods is based on the collocation formulation and does not require the generation of a grid and any integral evaluation. The application of this method to a flow in horizontal channel, taken as an experimental device, is presented. The results of computations are compared with experimental data and are found to be satisfactory

In this paper we deal with a model describing the evolution in time of the density of a neural population in a state space, where the state is given by Izhikevich’s two - dimensional single neuron model. The main goal is to mathematically describe the occurrence of a significant phenomenon observed in neurons populations, the synchronization. To this end, we are making the transition to phase density population, and use Malkin theorem to calculate the phase deviations of a weakly coupled population model.

We study controllability for a nonhomogeneous string and ring under an axial stretching tension that varies with time. We consider the boundary control for a string and distributed control for a ring. For a string, we are looking for a control f(t) ∈ L2(0, T) that drives the state solution to rest. We show that for a ring, two forces are required to achieve controllability. The controllability problem is reduced to a moment problem for the control. We describe the set of initial data which may be driven to rest by the control. The proof is based on an auxiliary basis property result.

The aim of this study was to describe and analyze the regulation and spatio-temporal dynamics of melanocyte migration in vitro and its coupling to cell division and interaction with the matrix. The melanocyte lineage is particularly interesting because it is involved in both embryonic development and oncogenesis/metastasis (melanoma). Biological experiments were performed on two melanocyte cell lines established from wild-type and β-catenin-transgenic mice (bcat*). The multi-functional β-catenin molecule is known to be able to regulate the transcription of various genes involved in cell proliferation and migration, particularly in the melanocyte lineage. We also investigated fibronectin, an extra-cellular matrix protein that binds integrins, thereby providing adhesion points for cells and encouraging migration. As the migration of individual cells were followed, automated methods were required for processing the large amount of data generated by the time-lapse video-microscopy. A model-based approach for automated cell tracking was evaluated on a sample by comparison with manual tracking. This method was found reliable in studying overall cell behaviour. Its application to all the observed sequences provided insight into the factors affecting melanocyte migration in vitro: melanocytes of mutated form of β-catenin showed higher division rates and no contact inhibition of growth was induced by the resulting increase in cell density. However, cell density limited the amplitude of cell displacements, although their motility was less affected. The high fibronectin concentration bound to substratum promoted cell migration and motility, the effect being more intense for wild-type cells than for cells with β-catenin over-expression. During the division process, cell migration speed increased rapidly then decreased slowly. Analyses of such data is expected to lead both to biological answers and to a framework for a better modeling processes in the future.

We study a class of bistable reaction-diffusion systems used to model two competing species. Systems in this class possess two uniform stable steady states representing semi-trivial solutions. Principally, we are interested in the case where the ratio of the diffusion coefficients is small, i.e. in the near-degenerate case. First, limiting arguments are presented to relate solutions to such systems to those of the degenerate case where one species is assumed not to diffuse. We then consider travelling wave solutions that connect the two stable semi-trivial states of the non-degenerate system. Next, a general energy function for the full system is introduced. Using this and the limiting arguments, we are able to determine the wave direction for small diffusion coefficient ratios. The results obtained only require knowledge of the system kinetics.

Epidemiologic data suggest that schools and daycare facilities likely play a major role in the dissemination of influenza. Pathogen transmission within such small, inhomogenously mixed populations is difficult to model using traditional approaches. We developed simulation based mathematical tool to investigate the effects of social contact networks on pathogen dissemination in a setting analogous to a daycare center or grade school. Here we show that interventions that decrease mixing within child care facilities, including limiting the size of social clusters, reducing the contact frequency between social clusters, and eliminating large gatherings, could diminish pathogen dissemination. Moreover, these measures may amplify the effectiveness of vaccination or antiviral prophylaxis, even if the vaccine is not uniformly effective or antiviral compliance is incomplete. Similar considerations should apply to other small, imperfectly mixed populations, such as offices and schools.

Experimental evidence points to a rich variety of physical scenarios that arise when a laminar flame propagates through a pre-mixture of evaporating liquid fuel and a gaseous oxidant. In this paper new results of time-dependent numerical simulations of rich off-stoichiometric spray flame propagation in a two-dimensional channel are presented. A constant density model is adopted, thereby eliminating the Darrieus-Landau instability. It is demonstrated that there exists a narrow band of vaporization Damkohler numbers (the ratio of a characteristic flow time to a characteristic evaporation time) for which the flame propagation is oscillatory. For values outside this range steady state propagation is attained but with a curved (cellular) flame front. The critical range for the non-steady propagation is also found to be a function of the Lewis number of the deficient reactant.

Cell motility is an integral part of a diverse set of biological processes. The quest for mathematical models of cell motility has prompted the development of automated approaches for gathering quantitative data on cell morphology, and the distribution of molecular players involved in cell motility. Here we review recent approaches for quantifying cell motility, including automated cell segmentation and tracking. Secondly, we present our own novel method for tracking cell boundaries of moving cells, the Electrostatic Contour Migration Method (ECMM), as an alternative to the generally accepted level set method (LSM). ECMM smoothly tracks regions of the cell boundary over time to compute local membrane displacements using the simple underlying concept of electrostatics. It offers substantial speed increases and reduced computational overheads in comparison to the LSM. We conclude with general considerations regarding boundary tracking in the context of mathematical modelling.

Plant growth depends essentially on nutrients coming from the roots and metabolites produced by the plant. Appearance of new branches is determined by concentrations of certain plant hormones. The most important of them are Auxin and Cytokinin. Auxin is produced in the growing, Cytokinin in either roots or in growing parts. Many dynamical models of this phenomena have been studied in [1]. In [5], the authors deal with one branch model. In this work, we focus our interest on a multiple branch model. We deal with the transport equation in domains of different sizes. A variational reduction type method [3] based on asymptotic partial decomposition introduced in [2] (see also [4]) is used. In this work we consider the transport equation in decomposed domain with a general right hand side

Approximate aggregation techniques consist of introducing certain approximations that allow one to reduce a complex system involving many coupled variables obtaining a simpler ʽʽaggregated systemʼʼ governed by a few variables. Moreover, they give results that allow one to extract information about the complex original system in terms of the behavior of the reduced one. Often, the feature that allows one to carry out such a reduction is the presence of different time scales in the system under consideration. In this work we deal with aggregation techniques in stochastic discrete time models and their application to the study of multiregional models, i.e., of models for an age structured population distributed amongst different spatial patches and in which migration between the patches is usually fast with respect to the demography (reproduction-survival) in each patch. Stochasticity in population models can be of two kinds: environmental and demographic. We review the formulation and the main properties of the dynamics of the different models for populations evolving in discrete time and subjected to the effects of environmental and demographic stochasticity. Then we present different stochastic multiregional models with two time scales in which migration is fast with respect to demography and we review the main relationships between the dynamics of the original complex system and the aggregated simpler one. Finally, and within the context of models with environmental stochasticity in which the environmental variation is Markovian, we make use these techniques to analyze qualitatively the behavior of two multiregional models in which the original complex system is intractable. In particular we study conditions under which the population goes extinct or grows exponentially.

We study discrete spectrum in spectral gaps of an elliptic periodic second order differential operator in L2(ℝd) perturbed by a decaying potential. It is assumed that a perturbation is nonnegative and has a power-like behavior at infinity. We find asymptotics in the large coupling constant limit for the number of eigenvalues of the perturbed operator that have crossed a given point inside the gap or the edge of the gap. The corresponding asymptotics is power-like and depends on the observation point.

We analyze a stochastic neuronal network model which corresponds to an all-to-all network of discretized integrate-and-fire neurons where the synapses are failure-prone. This network exhibits different phases of behavior corresponding to synchrony and asynchrony, and we show that this is due to the limiting mean-field system possessing multiple attractors. We also show that this mean-field limit exhibits a first-order phase transition as a function of the connection strength — as the synapses are made more reliable, there is a sudden onset of synchronous behavior. A detailed understanding of the dynamics involves both a characterization of the size of the giant component in a certain random graph process, and control of the pathwise dynamics of the system by obtaining exponential bounds for the probabilities of events far from the mean.