Effects of an advection term in nonlocal lotka-volterra equations

Nonlocal Lotka-Volterra equations have the property that solutions concentrate as Dirac masses in the limit of small diffusion. In this paper, we show how the presence of an advection term changes the location of the concentration points in the limit of small diffusion and slow drift. The mathematical interest lies in the formalism of constrained Hamilton-Jacobi equations. Our motivations come from previous models of evolutionary dynamics in phenotype-structured populations [R.H. Chisholm, T. Lorenzi, A. Lorz, et al., Cancer Res., 75, 930-939, 2015], where the diffusion operator models the effects of heritable variations in gene expression, while the advection term models the effect of stress-induced adaptation

Bridging the gap between individual-based and continuum models of growing cell populations

Continuum models for the spatial dynamics of growing cell populations have been widely used to investigate the mechanisms underpinning tissue development and tumour invasion. These models consist of nonlinear partial differential equations that describe the evolution of cellular densities in response to pressure gradients generated by population growth. Little prior work has explored the relation between such continuum models and related single-cell-based models. We present here a simple stochastic individual-based model for the spatial dynamics of multicellular systems whereby cells undergo pressure-driven movement and pressure-dependent proliferation. We show that nonlinear partial differential equations commonly used to model the spatial dynamics of growing cell populations can be formally derived from the branching random walk that underlies our discrete model. Moreover, we carry out a systematic comparison between the individual-based model and its continuum counterparts, both in the case of one single cell population and in the case of multiple cell populations with different biophysical properties. The outcomes of our comparative study demonstrate that the results of computational simulations of the individual-based model faithfully mirror the qualitative and quantitative properties of the solutions to the corresponding nonlinear partial differential equations. Ultimately, these results illustrate how the simple rules governing the dynamics of single cells in our individual-based model can lead to the emergence of complex spatial patterns of population growth observed in continuum models

A stochastic individual-based model to explore the role of spatial interactions and antigen recognition in the immune response against solid tumours

Spatial interactions between cancer and immune cells, as well as the recognition of tumour antigens by cells of the immune system, play a key role in the immune response against solid tumours. The existing mathematical models generally focus only on one of these key aspects. We present here a spatial stochastic individual-based model that explicitly captures antigen expression and recognition. In our model, each cancer cell is characterised by an antigen profile which can change over time due to either epimutations or mutations. The immune response against the cancer cells is initiated by the dendritic cells that recognise the tumour antigens and present them to the cytotoxic T cells. Consequently, T cells become activated against the tumour cells expressing such antigens. Moreover, the differences in movement between inactive and active immune cells are explicitly taken into account by the model. Computational simulations of our model clarify the conditions for the emergence of tumour clearance, dormancy or escape, and allow us to assess the impact of antigenic heterogeneity of cancer cells on the efficacy of immune action. Ultimately, our results highlight the complex interplay between spatial interactions and adaptive mechanisms that underpins the immune response against solid tumours, and suggest how this may be exploited to further develop cancer immunotherapies

A hybrid discrete-continuum approach to model Turing pattern formation

Since its introduction in 1952, with a further refinement in 1972 by Gierer and Meinhardt, Turing's (pre-)pattern theory (the chemical basis of morphogenesis) has been widely applied to a number of areas in developmental biology, where evolving cell and tissue structures are naturally observed. The related pattern formation models normally comprise a system of reaction-diffusion equations for interacting chemical species (morphogens), whose heterogeneous distribution in some spatial domain acts as a template for cells to form some kind of pattern or structure through, for example, differentiation or proliferation induced by the chemical pre-pattern. Here we develop a hybrid discrete-continuum modelling framework for the formation of cellular patterns via the Turing mechanism. In this framework, a stochastic individual-based model of cell movement and proliferation is combined with a reaction-diffusion system for the concentrations of some morphogens. As an illustrative example, we focus on a model in which the dynamics of the morphogens are governed by an activator-inhibitor system that gives rise to Turing pre-patterns. The cells then interact with the morphogens in their local area through either of two forms of chemically-dependent cell action: Chemotaxis and chemically-controlled proliferation. We begin by considering such a hybrid model posed on static spatial domains, and then turn to the case of growing domains. In both cases, we formally derive the corresponding deterministic continuum limit and show that that there is an excellent quantitative match between the spatial patterns produced by the stochastic individual-based model and its deterministic continuum counterpart, when sufficiently large numbers of cells are considered. This paper is intended to present a proof of concept for the ideas underlying the modelling framework, with the aim to then apply the related methods to the study of specific patterning and morphogenetic processes in the future

Modelling the Immune Response to Cancer: An Individual-Based Approach Accounting for the Difference in Movement Between Inactive and Activated T Cells

A growing body of experimental evidence indicates that immune cells move in an unrestricted search pattern if they are in the pre-activated state, whilst they tend to stay within a more restricted area upon activation induced by the presence of tumour antigens. This change in movement is not often considered in the existing mathematical models of the interactions between immune cells and cancer cells. With the aim to fill such a gap in the existing literature, in this work we present a spatially structured individual-based model of tumour–immune competition that takes explicitly into account the difference in movement between inactive and activated immune cells. In our model, a Lévy walk is used to capture the movement of inactive immune cells, whereas Brownian motion is used to describe the movement of antigen-activated immune cells. The effects of activation of immune cells, the proliferation of cancer cells and the immune destruction of cancer cells are also modelled. We illustrate the ability of our model to reproduce qualitatively the spatial trajectories of immune cells observed in experimental data of single-cell tracking. Computational simulations of our model further clarify the conditions for the onset of a successful immune action against cancer cells and may suggest possible targets to improve the efficacy of cancer immunotherapy. Overall, our theoretical work highlights the importance of taking into account spatial interactions when modelling the immune response to cancer cells

Comparative study between discrete and continuum models for the evolution of competing phenotype-structured cell populations in dynamical environments

Deterministic continuum models formulated as nonlocal partial differential equations for the evolutionary dynamics of populations structured by phenotypic traits have been used recently to address open questions concerning the adaptation of asexual species to periodically fluctuating environmental conditions. These models are usually defined on the basis of population-scale phenomenological assumptions and cannot capture adaptive phenomena that are driven by stochastic variability in the evolutionary paths of single individuals. In light of these considerations, in this paper we develop a stochastic individual-based model for the coevolution of two competing phenotype-structured cell populations that are exposed to time-varying nutrient levels and undergo spontaneous, heritable phenotypic changes with different probabilities. Here, the evolution of every cell is described by a set of rules that result in a discrete-time branching random walk on the space of phenotypic states, and nutrient levels are governed by a difference equation in which a sink term models nutrient consumption by the cells. We formally show that the deterministic continuum counterpart of this model comprises a system of nonlocal partial differential equations for the cell population density functions coupled with an ordinary differential equation for the nutrient concentration. We compare the individual-based model and its continuum analog, focusing on scenarios whereby the predictions of the two models differ. The results obtained clarify the conditions under which significant differences between the two models can emerge due to bottleneck effects that bring about both lower regularity of the density functions of the two populations and more pronounced demographic stochasticity. In particular, bottleneck effects emerge in the presence of lower probabilities of phenotypic variation and are more apparent when the two populations are characterized by lower fitness initial mean phenotypes and smaller initial levels of phenotypic heterogeneity. The emergence of these effects, and thus the agreement between the two modeling approaches, is also dependent on the initial proportions of the two populations. As an illustrative example, we demonstrate the implications of these results in the context of the mathematical modeling of the early stage of metastatic colonization of distant organs

Evolutionary dynamics of competing phenotype-structured populations in periodically fluctuating environments

Living species, ranging from bacteria to animals, exist in environmental conditions that exhibit spatial and temporal heterogeneity which requires them to adapt. Risk-spreading through spontaneous phenotypic variations is a known concept in ecology, which is used to explain how species may survive when faced with the evolutionary risks associated with temporally varying environments. In order to support a deeper understanding of the adaptive role of spontaneous phenotypic variations in fluctuating environments, we consider a system of non-local partial differential equations modelling the evolutionary dynamics of two competing phenotype-structured populations in the presence of periodically oscillating nutrient levels. The two populations undergo heritable, spontaneous phenotypic variations at different rates. The phenotypic state of each individual is represented by a continuous variable, and the phenotypic landscape of the populations evolves in time due to variations in the nutrient level. Exploiting the analytical tractability of our model, we study the long-time behaviour of the solutions to obtain a detailed mathematical depiction of the evolutionary dynamics. The results suggest that when nutrient levels undergo small and slow oscillations, it is evolutionarily more convenient to rarely undergo spontaneous phenotypic variations. Conversely, under relatively large and fast periodic oscillations in the nutrient levels, which bring about alternating cycles of starvation and nutrient abundance, higher rates of spontaneous phenotypic variations confer a competitive advantage. We discuss the implications of our results in the context of cancer metabolism

Testing the nature of S0 galaxies using planetary nebula kinematics in NGC 1023

We investigate the manner in which lenticular galaxies are formed by studying their stellar kinematics: an S0 formed from a fading spiral galaxy should display similar cold outer disc kinematics to its progenitor, while an S0 formed in a minor merger should be more dominated by random motions. In a pilot study to attempt to distinguish between these scenarios, we have measured the planetary nebula (PN) kinematics of the nearby S0 system NGC 1023. Using the Planetary Nebula Spectrograph, we have detected and measured the line-of-sight velocities of 204 candidate PNe in the field of this galaxy. Out to intermediate radii, the system displays the kinematics of a normal rotationally-supported disc system. After correction of its rotational velocities for asymmetric drift, the galaxy lies just below the spiral galaxy Tully-Fisher relation, as one would expect for a fading system. However, at larger radii the kinematics undergo a gradual but major transition to random motion with little rotation. This transition does not seem to reflect a change in the viewing geometry or the presence of a distinct halo component, since the number counts of PNe follow the same simple exponential decline as the stellar continuum with the same projected disc ellipticity out to large radii. The galaxy's small companion, NGC 1023A, does not seem to be large enough to have caused the observed modification either. This combination of properties would seem to indicate a complex evolutionary history in either the transition to form an S0 or in the past life of the spiral galaxy from which the S0 formed. More data sets of this type from both spirals and S0s are needed in order to definitively determine the relationship between these types of system.Comment: Accepted for publication in MNRAS. Version with full resolution figure 1 can be found at http://www.nottingham.ac.uk/~ppzmrm/N1023_PNS.accepted.pd

Visible and near-infrared observations of asteroid 2012 DA14 during its closest approach of February 15, 2013

Near-Earth asteroid 2012 DA14 made its closest approach on February 15, 2013, when it passed at a distance of 27,700 km from the Earth's surface. It was the first time an asteroid of moderate size was predicted to approach that close to the Earth, becoming bright enough to permit a detailed study from ground-based telescopes. Asteroid 2012 DA14 was poorly characterized before its closest approach. We acquired data using several telescopes on four Spanish observatories: the 10.4m Gran Telescopio Canarias (GTC) and the 3.6m Telescopio Nazionale Galileo (TNG), both in the El Roque de los Muchachos Observatory (ORM, La Palma); the 2.2m CAHA telescope, in the Calar Alto Observatory (Almeria); the f/3 0.77m telescope in the La Hita Observatory (Toledo); and the f/8 1.5m telescope in the Sierra Nevada Observatory (OSN, Granada). We obtained visible and near-infrared color photometry, visible spectra and time-series photometry. Visible spectra together with color photometry of 2012 DA14 show that it can be classified as an L-type asteroid, a rare spectral type with a composition similar to that of carbonaceous chondrites. The time-series photometry provides a rotational period of 8.95 +- 0.08 hours after the closest approach, and there are indications that the object suffered a spin-up during this event. The large amplitude of the light curve suggests that the object is very elongated and irregular, with an equivalent diameter of around 18m. We obtain an absolute magnitude of H_R = 24.5 +- 0.2, corresponding to H_V = 25.0 +- 0.2. The GTC photometry also gives H_V = 25.29 +- 0.14. Both values agree with the value listed at the Minor Planet Center shortly after discovery. From the absolute photometry, together with some constraints on size and shape, we compute a geometric albedo of p_V = 0.44 +- 0.20, which is slightly above the range of albedos known for L-type asteroids (0.082 - 0.405).Comment: 7 pages, 4 figures, 1 table. Accepted in A&A (June 17 2013

Discrete and continuum phenotype-structured models for the evolution of cancer cell populations under chemotherapy

We present a stochastic individual-based model for the phenotypic evolution of cancer cell populations under chemotherapy. In particular, we consider the case of combination cancer therapy whereby a chemotherapeutic agent is administered as the primary treatment and an epigenetic drug is used as an adjuvant treatment. The cell population is structured by the expression level of a gene that controls cell proliferation and chemoresistance. In order to obtain an analytical description of evolutionary dynamics, we formally derive a deterministic continuum counterpart of this discrete model, which is given by a nonlocal parabolic equation for the cell population density function. Integrating computational simulations of the individual-based model with analysis of the corresponding continuum model, we perform a complete exploration of the model parameter space. We show that harsher environmental conditions and higher probabilities of spontaneous epimutation can lead to more effective chemotherapy, and we demonstrate the existence of an inverse relationship between the efficacy of the epigenetic drug and the probability of spontaneous epimutation. Taken together, the outcomes of the model provide theoretical ground for the development of anticancer protocols that use lower concentrations of chemotherapeutic agents in combination with epigenetic drugs capable of promoting the re-expression of epigenetically regulated genes