Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userFast migration and emergent population dynamics
188
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
We consider population dynamics on a network of patches, each of which has a the same local dynamics, with different population scales (carrying capacities). It is reasonable to assume that if the patches are coupled by very fast migration the whole system will look like an individual patch with a large effective carrying capacity. This is called a well-mixed system. We show that, in general, it is not true that the well-mixed system has the same dynamics as each local patch. Different global dynamics can emerge from coupling, and usually must be figured out for each individual case. We give a general condition which must be satisfied for well-mixed systems to have the same dynamics as the constituent patches.
rate research
Read More
Recently the A/H1N1-2009 virus pandemic appeared in Mexico and in other nations. We present a study of this pandemic in the Mexican case using the SIR model to describe epidemics. This model is one of the simplest models but it has been a successful description of some epidemics of closed populations. We consider the data for the Mexican case and use the SIR model to make some predictions. Then, we generalize the SIR model in order to describe the spatial dynamics of the disease. We make a study of the spatial and temporal spread of the infected population with model parameters that are consistent with temporal SIR model parameters obtained by fitting to the Mexican case.
Amoeboid cell migration is characterized by frequent changes of the direction of motion and resembles a persistent random walk on long time scales. Although it is well known that cell migration is typically driven by the actin cytoskeleton, the cause of this migratory behavior remains poorly understood. We analyze the spontaneous dynamics of actin assembly due to nucleation promoting factors, where actin filaments lead to an inactivation of the nucleators. We show that this system exhibits excitable dynamics and can spontaneously generate waves, which we analyse in detail. By using a phase-field approach, we show that these waves can generate cellular random walks. We explore how the characteristics of these persistent random walks depend on the parameters governing the actin-nucleator dynamics. In particular, we find that the effective diffusion constant and the persistence time depend strongly on the speed of filament assembly and the rate of nucleator inactivation. Our findings point to a deterministic origin of the random walk behavior and suggest that cells could adapt their migration pattern by modifying the pool of available actin.
To fulfill their killing functions, cytotoxic T lymphocytes (CTLs) need to migrate to search for their target cells in complex biological microenvironments, a key component of which is extracellular matrix (ECM). The mechanisms underlying CTLs navigation are not well understood so far. Here we use a collagen assay as a model for the ECM and analyze the migration trajectories of primary human CTLs in collagen matrices with different concentrations. We observe different migration patterns for individual T cells. Three different motility types can be distinguished: slow, fast and mixed motilities. Slow CTLs remain nearly stationary within the collagen matrix and show slightly anti-persistent motility, while the fast ones move quickly and persistent (i.e. with not too large turning angles). The dynamics of the mixed type consists of periods of slow and fast motions; both states are persistent, but they have different persistencies. The dynamics can be well described by a two-state persistent random walk model. We extract the parameters of the model by analyzing experimental data. The mean square displacements predicted by the model and those measured experimentally are in very good agreement, without any fitting parameter. Potential reasons for the observed two-state motility are discussed. T cells dig the collagen during their migration and form channels, which facilitate the movement of other CTLs in the collagen network.
Microbial communities are ubiquitous in nature and come in a multitude of forms, ranging from communities dominated by a handful of species to communities containing a wide variety of metabolically distinct organisms. This huge range in diversity is not a curiosity - microbial diversity has been linked to outcomes of substantial ecological and medical importance. However, the mechanisms underlying microbial diversity are still under debate, as simple mathematical models only permit as many species to coexist as there are resources. A plethora of mechanisms have been proposed to explain the origins of microbial diversity, but many of these analyses omit a key property of real microbial ecosystems: the propensity of the microbes themselves to change their growth properties within and across generations. In order to explore the impact of this key property on microbial diversity, we expand upon a recently developed model of microbial diversity in fluctuating environments. We implement changes in growth strategy in two distinct ways. First, we consider the regulation of a cells enzyme levels within short, ecological times, and second we consider evolutionary changes driven by mutations across generations. Interestingly, we find that these two types of microbial responses to the environment can have drastically different outcomes. Enzyme regulation may collapse diversity over long enough times while, conversely, strategy-randomizing mutations can produce a rich-get-poorer effect that promotes diversity. This work makes explicit, using a simple serial-dilutions framework, the conflicting ways that microbial adaptation and evolution can affect community diversity.
In the study of the evolution of cooperation, resource limitations are usually assumed just to provide a finite population size. Recently, however, agent-based models have pointed out that resource limitation may modify the original structure of the interactions and allow for the survival of unconditional cooperators in well-mixed populations. Here, we present analytical simplifi
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
