214 research outputs found
Collective predator evasion: Putting the criticality hypothesis to the test
According to the criticality hypothesis, collective biological systems should
operate in a special parameter region, close to so-called critical points,
where the collective behavior undergoes a qualitative change between different
dynamical regimes. Critical systems exhibit unique properties, which may
benefit collective information processing such as maximal responsiveness to
external stimuli. Besides neuronal and gene-regulatory networks, recent
empirical data suggests that also animal collectives may be examples of
self-organized critical systems. However, open questions about
self-organization mechanisms in animal groups remain: Evolutionary adaptation
towards a group-level optimum (group-level selection), implicitly assumed in
the "criticality hypothesis", appears in general not reasonable for
fission-fusion groups composed of non-related individuals. Furthermore,
previous theoretical work relies on non-spatial models, which ignore
potentially important self-organization and spatial sorting effects. Using a
generic, spatially-explicit model of schooling prey being attacked by a
predator, we show first that schools operating at criticality perform best.
However, this is not due to optimal response of the prey to the predator, as
suggested by the "criticality hypothesis", but rather due to the spatial
structure of the prey school at criticality. Secondly, by investigating
individual-level evolution, we show that strong spatial self-sorting effects at
the critical point lead to strong selection gradients, and make it an
evolutionary unstable state. Our results demonstrate the decisive role of
spatio-temporal phenomena in collective behavior, and that individual-level
selection is in general not a viable mechanism for self-tuning of unrelated
animal groups towards criticality
Collective motion of active Brownian particles in one dimension
We analyze a model of active Brownian particles with non-linear friction and
velocity coupling in one spatial dimension. The model exhibits two modes of
motion observed in biological swarms: A disordered phase with vanishing mean
velocity and an ordered phase with finite mean velocity. Starting from the
microscopic Langevin equations, we derive mean-field equations of the
collective dynamics. We identify the fixed points of the mean-field equations
corresponding to the two modes and analyze their stability with respect to the
model parameters. Finally, we compare our analytical findings with numerical
simulations of the microscopic model.Comment: submitted to Eur. Phys J. Special Topic
Active Brownian particles with velocity-alignment and active fluctuations
We consider a model of active Brownian particles with velocity-alignment in
two spatial dimensions with passive and active fluctuations. Hereby, active
fluctuations refers to purely non-equilibrium stochastic forces correlated with
the heading of an individual active particle. In the simplest case studied
here, they are assumed as independent stochastic forces parallel (speed noise)
and perpendicular (angular noise) to the velocity of the particle. On the other
hand, passive fluctuations are defined by a noise vector independent of the
direction of motion of a particle, and may account for example for thermal
fluctuations.
We derive a macroscopic description of the active Brownian particle gas with
velocity-alignment interaction. Hereby, we start from the individual based
description in terms of stochastic differential equations (Langevin equations)
and derive equations of motion for the coarse grained kinetic variables
(density, velocity and temperature) via a moment expansion of the corresponding
probability density function.
We focus here in particular on the different impact of active and passive
fluctuations on the onset of collective motion and show how active fluctuations
in the active Brownian dynamics can change the phase-transition behaviour of
the system. In particular, we show that active angular fluctuation lead to an
earlier breakdown of collective motion and to emergence of a new bistable
regime in the mean-field case.Comment: 5 figures, 22 pages, submitted to New Journal of Physic
Steering cell migration by alternating blebs and actin-rich protrusions
Background
High directional persistence is often assumed to enhance the efficiency of chemotactic migration. Yet, cells in vivo usually display meandering trajectories with relatively low directional persistence, and the control and function of directional persistence during cell migration in three-dimensional environments are poorly understood.
Results
Here, we use mesendoderm progenitors migrating during zebrafish gastrulation as a model system to investigate the control of directional persistence during migration in vivo. We show that progenitor cells alternate persistent run phases with tumble phases that result in cell reorientation. Runs are characterized by the formation of directed actin-rich protrusions and tumbles by enhanced blebbing. Increasing the proportion of actin-rich protrusions or blebs leads to longer or shorter run phases, respectively. Importantly, both reducing and increasing run phases result in larger spatial dispersion of the cells, indicative of reduced migration precision. A physical model quantitatively recapitulating the migratory behavior of mesendoderm progenitors indicates that the ratio of tumbling to run times, and thus the specific degree of directional persistence of migration, are critical for optimizing migration precision.
Conclusions
Together, our experiments and model provide mechanistic insight into the control of migration directionality for cells moving in three-dimensional environments that combine different protrusion types, whereby the proportion of blebs to actin-rich protrusions determines the directional persistence and precision of movement by regulating the ratio of tumbling to run times
Constructing a Stochastic Model of Bumblebee Flights from Experimental Data
PMCID: PMC3592844This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Low Reynolds number hydrodynamics of asymmetric, oscillating dumbbell pairs
Active dumbbell suspensions constitute one of the simplest model system for
collective swimming at low Reynolds number. Generalizing recent work, we derive
and analyze stroke-averaged equations of motion that capture the effective
hydrodynamic far-field interaction between two oscillating, asymmetric
dumbbells in three space dimensions. Time-averaged equations of motion, as
those presented in this paper, not only yield a considerable speed-up in
numerical simulations, they may also serve as a starting point when deriving
continuum equations for the macroscopic dynamics of multi-swimmer suspensions.
The specific model discussed here appears to be particularly useful in this
context, since it allows one to investigate how the collective macroscopic
behavior is affected by changes in the microscopic symmetry of individual
swimmers.Comment: 10 pages, to appear in EPJ Special Topic
High-statistics modeling of complex pedestrian avoidance scenarios
Quantitatively modeling the trajectories and behavior of pedestrians walking
in crowds is an outstanding fundamental challenge deeply connected with the
physics of flowing active matter, from a scientific point of view, and having
societal applications entailing individual safety and comfort, from an
application perspective.
In this contribution, we review a pedestrian dynamics modeling approach,
previously proposed by the authors, aimed at reproducing some of the
statistical features of pedestrian motion. Comparing with high-statistics
pedestrian dynamics measurements collected in real-life conditions (from
hundreds of thousands to millions of trajectories), we modeled quantitatively
the statistical features of the undisturbed motion (i.e. in absence of
interactions with other pedestrians) as well as the avoidance dynamics
triggered by a pedestrian incoming in the opposite direction. This was
accomplished through (coupled) Langevin equations with potentials including
multiple preferred velocity states and preferred paths. In this chapter we
review this model, discussing some of its limitations, in view of its extension
toward a more complex case: the avoidance dynamics of a single pedestrian
walking through a crowd that is moving in the opposite direction. We analyze
some of the challenges connected to this case and present extensions to the
model capable of reproducing some features of the motion
Active Brownian Particles. From Individual to Collective Stochastic Dynamics
We review theoretical models of individual motility as well as collective
dynamics and pattern formation of active particles. We focus on simple models
of active dynamics with a particular emphasis on nonlinear and stochastic
dynamics of such self-propelled entities in the framework of statistical
mechanics. Examples of such active units in complex physico-chemical and
biological systems are chemically powered nano-rods, localized patterns in
reaction-diffusion system, motile cells or macroscopic animals. Based on the
description of individual motion of point-like active particles by stochastic
differential equations, we discuss different velocity-dependent friction
functions, the impact of various types of fluctuations and calculate
characteristic observables such as stationary velocity distributions or
diffusion coefficients. Finally, we consider not only the free and confined
individual active dynamics but also different types of interaction between
active particles. The resulting collective dynamical behavior of large
assemblies and aggregates of active units is discussed and an overview over
some recent results on spatiotemporal pattern formation in such systems is
given.Comment: 161 pages, Review, Eur Phys J Special-Topics, accepte
Similarly Efficacious Anti-Malarial Drugs SJ733 and Pyronaridine Differ in Their Ability to Remove Circulating Parasites in Mice
BACKGROUND: Artemisinin-based combination therapy (ACT) has been a mainstay for malaria prevention and treatment. However, emergence of drug resistance has incentivised development of new drugs. Defining the kinetics with which circulating parasitized red blood cells (pRBC) are lost after drug treatment, referred to as the parasite clearance curve , has been critical for assessing drug efficacy; yet underlying mechanisms remain partly unresolved. The clearance curve may be shaped both by the rate at which drugs kill parasites, and the rate at which drug-affected parasites are removed from circulation.
METHODS: In this context, two anti-malarials, SJ733, and an ACT partner drug, pyronaridine were compared against sodium artesunate in mice infected with Plasmodium berghei (strain ANKA). To measure each compound\u27s capacity for pRBC removal in vivo, flow cytometric monitoring of a single cohort of fluorescently-labelled pRBC was employed, and combined with ex vivo parasite culture to assess parasite maturation and replication.
RESULTS: These three compounds were found to be similarly efficacious in controlling established infection by reducing overall parasitaemia. While sodium artesunate acted relatively consistently across the life-stages, single-dose SJ733 elicited a biphasic effect, triggering rapid, partly phagocyte-dependent removal of trophozoites and schizonts, followed by arrest of residual ring-stages. In contrast, pyronaridine abrogated maturation of younger parasites, with less pronounced effects on mature parasites, while modestly increasing pRBC removal.
CONCLUSIONS: Anti-malarials SJ733 and pyronaridine, though similarly efficacious in reducing overall parasitaemia in mice, differed markedly in their capacity to arrest replication and remove pRBC from circulation. Thus, similar parasite clearance curves can result for anti-malarials with distinct capacities to inhibit, kill and clear parasites
- …