29,584 research outputs found
Invasion and adaptive evolution for individual-based spatially structured populations
The interplay between space and evolution is an important issue in population
dynamics, that is in particular crucial in the emergence of polymorphism and
spatial patterns. Recently, biological studies suggest that invasion and
evolution are closely related. Here we model the interplay between space and
evolution starting with an individual-based approach and show the important
role of parameter scalings on clustering and invasion. We consider a stochastic
discrete model with birth, death, competition, mutation and spatial diffusion,
where all the parameters may depend both on the position and on the trait of
individuals. The spatial motion is driven by a reflected diffusion in a bounded
domain. The interaction is modelled as a trait competition between individuals
within a given spatial interaction range. First, we give an algorithmic
construction of the process. Next, we obtain large population approximations,
as weak solutions of nonlinear reaction-diffusion equations with Neumann's
boundary conditions. As the spatial interaction range is fixed, the
nonlinearity is nonlocal. Then, we make the interaction range decrease to zero
and prove the convergence to spatially localized nonlinear reaction-diffusion
equations, with Neumann's boundary conditions. Finally, simulations based on
the microscopic individual-based model are given, illustrating the strong
effects of the spatial interaction range on the emergence of spatial and
phenotypic diversity (clustering and polymorphism) and on the interplay between
invasion and evolution. The simulations focus on the qualitative differences
between local and nonlocal interactions
The impact of cell crowding and active cell movement on vascular tumour growth
A multiscale model for vascular tumour growth is presented which includes systems of ordinary differential equations for the cell cycle and regulation of apoptosis in individual cells, coupled to partial differential equations for the spatio-temporal dynamics of nutrient and key signalling chemicals. Furthermore, these subcellular and tissue layers are incorporated into a cellular automaton framework for cancerous and normal tissue with an embedded vascular network. The model is the extension of previous work and includes novel features such as cell movement and contact inhibition. We presented a detailed simulation study of the effects of these additions on the invasive behaviour of tumour cells and the tumour's response to chemotherapy. In particular, we find that cell movement alone increases the rate of tumour growth and expansion, but that increasing the tumour cell carrying capacity leads to the formation of less invasive dense hypoxic tumours containing fewer tumour cells. However, when an increased carrying capacity is combined with significant tumour cell movement, the tumour grows and spreads more rapidly, accompanied by large spatio-temporal fluctuations in hypoxia, and hence in the number of quiescent cells. Since, in the model, hypoxic/quiescent cells produce VEGF which stimulates vascular adaptation, such fluctuations can dramatically affect drug delivery and the degree of success of chemotherapy
On the variations of the principal eigenvalue with respect to a parameter in growth-fragmentation models
We study the variations of the principal eigenvalue associated to a
growth-fragmentation-death equation with respect to a parameter acting on
growth and fragmentation. To this aim, we use the probabilistic
individual-based interpretation of the model. We study the variations of the
survival probability of the stochastic model, using a generation by generation
approach. Then, making use of the link between the survival probability and the
principal eigenvalue established in a previous work, we deduce the variations
of the eigenvalue with respect to the parameter of the model
Immune-mediated competition in rodent malaria is most likely caused by induced changes in innate immune clearance of merozoites
Malarial infections are often genetically diverse, leading to competitive interactions between parasites. A quantitative understanding of the competition between strains is essential to understand a wide range of issues, including the evolution of virulence and drug resistance. In this study, we use dynamical-model based Bayesian inference to investigate the cause of competitive suppression of an avirulent clone of Plasmodium chabaudi (AS) by a virulent clone (AJ) in immuno-deficient and competent mice. We test whether competitive suppression is caused by clone-specific differences in one or more of the following processes: adaptive immune clearance of merozoites and parasitised red blood cells (RBCs), background loss of merozoites and parasitised RBCs, RBC age preference, RBC infection rate, burst size, and within-RBC interference. These processes were parameterised in dynamical mathematical models and fitted to experimental data. We found that just one parameter μ, the ratio of background loss rate of merozoites to invasion rate of mature RBCs, needed to be clone-specific to predict the data. Interestingly, μ was found to be the same for both clones in single-clone infections, but different between the clones in mixed infections. The size of this difference was largest in immuno-competent mice and smallest in immuno-deficient mice. This explains why competitive suppression was alleviated in immuno-deficient mice. We found that competitive suppression acts early in infection, even before the day of peak parasitaemia. These results lead us to argue that the innate immune response clearing merozoites is the most likely, but not necessarily the only, mediator of competitive interactions between virulent and avirulent clones. Moreover, in mixed infections we predict there to be an interaction between the clones and the innate immune response which induces changes in the strength of its clearance of merozoites. What this interaction is unknown, but future refinement of the model, challenged with other datasets, may lead to its discovery
Adaptive dynamics in logistic branching populations
We consider a trait-structured population subject to mutation, birth and
competition of logistic type, where the number of coexisting types may
fluctuate. Applying a limit of rare mutations to this population while keeping
the population size finite leads to a jump process, the so-called `trait
substitution sequence', where evolution proceeds by successive invasions and
fixations of mutant types. The probability of fixation of a mutant is
interpreted as a fitness landscape that depends on the current state of the
population. It was in adaptive dynamics that this kind of model was first
invented and studied, under the additional assumption of large population.
Assuming also small mutation steps, adaptive dynamics' theory provides a
deterministic ODE approximating the evolutionary dynamics of the dominant trait
of the population, called `canonical equation of adaptive dynamics'. In this
work, we want to include genetic drift in this models by keeping the population
finite. Rescaling mutation steps (weak selection) yields in this case a
diffusion on the trait space that we call `canonical diffusion of adaptive
dynamics', in which genetic drift (diffusive term) is combined with directional
selection (deterministic term) driven by the fitness gradient. Finally, in
order to compute the coefficients of this diffusion, we seek explicit
first-order formulae for the probability of fixation of a nearly neutral mutant
appearing in a resident population. These formulae are expressed in terms of
`invasibility coefficients' associated with fertility, defense, aggressiveness
and isolation, which measure the robustness (stability w.r.t. selective
strengths) of the resident type. Some numerical results on the canonical
diffusion are also given
A systematic approach to cancer: evolution beyond selection.
Cancer is typically scrutinized as a pathological process characterized by chromosomal aberrations and clonal expansion subject to stochastic Darwinian selection within adaptive cellular ecosystems. Cognition based evolution is suggested as an alternative approach to cancer development and progression in which neoplastic cells of differing karyotypes and cellular lineages are assessed as self-referential agencies with purposive participation within tissue microenvironments. As distinct self-aware entities, neoplastic cells occupy unique participant/observer status within tissue ecologies. In consequence, neoplastic proliferation by clonal lineages is enhanced by the advantaged utilization of ecological resources through flexible re-connection with progenitor evolutionary stages
Evolution of discrete populations and the canonical diffusion of adaptive dynamics
The biological theory of adaptive dynamics proposes a description of the
long-term evolution of a structured asexual population. It is based on the
assumptions of large population, rare mutations and small mutation steps, that
lead to a deterministic ODE describing the evolution of the dominant type,
called the ``canonical equation of adaptive dynamics.'' Here, in order to
include the effect of stochasticity (genetic drift), we consider self-regulated
randomly fluctuating populations subject to mutation, so that the number of
coexisting types may fluctuate. We apply a limit of rare mutations to these
populations, while keeping the population size finite. This leads to a jump
process, the so-called ``trait substitution sequence,'' where evolution
proceeds by successive invasions and fixations of mutant types. Then we apply a
limit of small mutation steps (weak selection) to this jump process, that leads
to a diffusion process that we call the ``canonical diffusion of adaptive
dynamics,'' in which genetic drift is combined with directional selection
driven by the gradient of the fixation probability, also interpreted as an
invasion fitness. Finally, we study in detail the particular case of multitype
logistic branching populations and seek explicit formulae for the invasion
fitness of a mutant deviating slightly from the resident type. In particular,
second-order terms of the fixation probability are products of functions of the
initial mutant frequency, times functions of the initial total population size,
called the invasibility coefficients of the resident by increased fertility,
defence, aggressiveness, isolation or survival.Comment: Published at http://dx.doi.org/10.1214/105051606000000628 in the
Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute
of Mathematical Statistics (http://www.imstat.org
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