A Stochastic Description of Dictyostelium Chemotaxis

Amselem, Gabriel; Theves, Matthias; Bae, Albert; Bodenschatz, Eberhard; Beta, Carsten

doi:10.1371/journal.pone.0037213

Cited by 61 publications

(101 citation statements)

References 44 publications

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“…There are many directions for improvement. More quantitative comparisons could be made by detailed measurement of single-cell statistics [17, 30], leading to nonlinear or anisotropic terms in Eq. 2.…”

Section: Distinguishing Between Potential Collective Chemotaxis Modelsmentioning

confidence: 99%

Emergent Collective Chemotaxis without Single-Cell Gradient Sensing

et al. 2016

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Many eukaryotic cells chemotax, sensing and following chemical gradients. However, experiments have shown that even under conditions when single cells cannot chemotax, small clusters may still follow a gradient. This behavior has been observed in neural crest cells, in lymphocytes, and during border cell migration in Drosophila, but its origin remains puzzling. Here, we propose a new mechanism underlying this “collective guidance”, and study a model based on this mechanism both analytically and computationally. Our approach posits that contact inhibition of locomotion (CIL), where cells polarize away from cell-cell contact, is regulated by the chemoattractant. Individual cells must measure the mean attractant value, but need not measure its gradient, to give rise to directional motility for a cell cluster. We present analytic formulas for how cluster velocity and chemotactic index depend on the number and organization of cells in the cluster. The presence of strong orientation effects provides a simple test for our theory of collective guidance.

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Section: Distinguishing Between Potential Collective Chemotaxis Modelsmentioning

confidence: 99%

Emergent Collective Chemotaxis without Single-Cell Gradient Sensing

et al. 2016

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“…Examples are the works on dicstyostelium [2], also under influence of chemotactic stimulus [3], on human motile keratinocytes and fibroblasts [4], on motile pieces of physarum [5], on flagellate eukaryotes (Euglena) [6], also in time dependent light fields [7], and on more complex organisms as gastropods [8], zooplankton [9], birds [10] and zebra-tail fish [11]. One has also considered motion in the presence of boundaries which might substantially change its effective properties [12,13].…”

Section: Introductionmentioning

confidence: 99%

Diffusion of active particles with stochastic torques modeled asα-stable noise

Nötel¹,

Sokolov²,

Schimansky‐Geier³

2016

J. Phys. A: Math. Theor.

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We investigate the stochastic dynamics of an active particle moving at a constant speed under the influence of a fluctuating torque. In our model the angular velocity is generated by a constant torque and random fluctuations described as a Lévy-stable noise. Two situations are investigated.First, we study white Lévy noise where the constant speed and the angular noise generate a persistent motion characterized by the persistence time τ D . At this time scale the crossover from ballistic to normal diffusive behavior is observed. The corresponding diffusion coefficient can be obtained analytically for the whole class of symmetric α-stable noises. As typical for models with noise-driven angular dynamics, the diffusion coefficient depends non-monotonously on the angular noise intensity. As second example, we study angular noise as described by an OrnsteinUhlenbeck process with correlation time τ c driven by the Cauchy white noise. We discuss the asymptotic diffusive properties of this model and obtain the same analytical expression for the diffusion coefficient as in the first case which is thus independent on τ c . Remarkably, for τ c > τ D the crossover from a non-Gaussian to a Gaussian distribution of displacements takes place at a time τ G which can be considerably larger than the persistence time τ D .

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“…Using microfluidic techniques, experimental conditions can be much better controlled; e.g., a linear gradient can be generated and kept constant, or even fast switched (38,39), the object distribution can be easily analyzed (40), and the objects can be tracked individually (41)(42)(43). In this paper we present a microfluidic assay for the quantitative study of autochemotaxis.…”

mentioning

confidence: 99%

Chemotaxis and autochemotaxis of self-propelling droplet swimmers

Jin

Krüger

Maaß

2017

Proc. Natl. Acad. Sci. U.S.A.

260

257

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Chemotaxis and autochemotaxis play an important role in many essential biological processes. We present a self-propelling artificial swimmer system that exhibits chemotaxis as well as negative autochemotaxis. Oil droplets in an aqueous surfactant solution are driven by interfacial Marangoni flows induced by micellar solubilization of the oil phase. We demonstrate that chemotaxis along micellar surfactant gradients can guide these swimmers through a microfluidic maze. Similarly, a depletion of empty micelles in the wake of a droplet swimmer causes negative autochemotaxis and thereby trail avoidance. We studied autochemotaxis quantitatively in a microfluidic device of bifurcating channels: Branch choices of consecutive swimmers are anticorrelated, an effect decaying over time due to trail dispersion. We modeled this process by a simple one-dimensional diffusion process and stochastic Langevin dynamics. Our results are consistent with a linear surfactant gradient force and diffusion constants appropriate for micellar diffusion and provide a measure of autochemotactic feedback strength vs. stochastic forces. This assay is readily adaptable for quantitative studies of both artificial and biological autochemotactic systems.artificial swimmers | chemotaxis | autochemotaxis | microfluidics L ocomotion of living bacteria or cells can be random or oriented. Oriented motion comprises the various "taxis" strategies by which bacteria or cells react to changes in their environment (1). Among these, chemotaxis is one of the best-studied examples (2, 3): Cells and microorganisms are able to sense certain chemicals (chemoattractants or chemorepellents) and move toward or away from them. This is an essential function in many biological processes, e.g., wound healing, fertilization, pathogenic species invading a host, or colonization dynamics (4, 5). When the chemoattractant or chemorepellent is produced by the microorganisms themselves, the system exhibits positive or negative autochemotaxis. Thus, chemotaxis provides a mechanism of interindividual communication. Modeling such communication strategies is key to understanding the collective behavior of microorganisms (6-8) as well as flocks of animals like fire ants (9, 10).To model the swimming motion of microorganisms, various self-propelling artificial swimmer systems have been developed based on different mechanisms. Generally, there are two classes of swimmers: systems driven by and aligning with external fields (11-14), including chemotactic gradients, and selfpropelled swimmers, which move autonomously in homogeneous environments (15-21). Many autonomous swimmers additionally react to external fields, e.g., phototactic gradients (22).Biological autochemotactic systems exhibit very complex behaviors (23,24), where physical effects are intermingling with effects from various bioprocesses such as cell migration, metabolism, and division. To untangle these effects, there have been some design proposals for artificial systems, such as in ref.25, and simulations on the dynamics o...

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A Stochastic Description of Dictyostelium Chemotaxis

Cited by 61 publications

References 44 publications

Emergent Collective Chemotaxis without Single-Cell Gradient Sensing

Emergent Collective Chemotaxis without Single-Cell Gradient Sensing

Diffusion of active particles with stochastic torques modeled asα-stable noise

Chemotaxis and autochemotaxis of self-propelling droplet swimmers

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