Modelling ofDictyostelium discoideummovement in a linear gradient of chemoattractant

Abstract

Chemotaxis is a ubiquitous biological phenomenon in which cells detect a spatial gradient of chemoattractant, and then move towards the source. Here we present a position-dependent advection-diffusion model that quantitatively describes the statistical features of the chemotactic motion of the social amoeba Dictyostelium discoideum in a linear gradient of cAMP (cyclic adenosine monophosphate). We fit the model to experimental trajectories that are recorded in a microfluidic setup with stationary cAMP gradients and extract the diffusion and drift coefficients in the gradient direction. Our analysis shows that for the majority of gradients, both coefficients decrease over time and become negative as the cells crawl up the gradient. The extracted model parameters also show that besides the expected drift in the direction of the chemoattractant gradient, we observe a nonlinear dependency of the corresponding variance on time, which can be explained by the model. Furthermore, the results of the model show that the non-linear term in the mean squared displacement of the cell trajectories can dominate the linear term on large time scales.

Synthetic Chemotaxis and Collective Behavior in Active Matter

“…An example illustrating collective behavior based on signaling is provided by Dictyostelium cells; when starving, they produce certain chemicals (cAMP) whose “smell” attracts other cells inducing a positive feedback loop: a local cell accumulation yields an enhanced chemical production, further enhancing the initial smell attracting other cells, etc. Overall, this results in a collapse of Dictyostelium cells followed by sporulation allowing the cells to survive long starvation periods; ,, see ref for a recent model on signaling Dictyostelium . This mechanism is captured in the classical Keller–Segel model , and has recently been established also for E. coli bacteria showing positive chemotaxis to self-produced autoinducers …”

“…This expression for B can now be used to strongly simplify the Keller-Segel instability for active particles. Combining (15) with (14) yields the following instability criterion 6Dρ 0 /K d > 1 or with the quasi-2D area fraction f = πρ 0 (f = πR 2 0 ρ 0 in physical units):…”

Memoryless chemotaxis with discrete cues

“…Bacteria typically execute run-and-tumble dynamics, during which gradient sensing occurs over an extended period of time. However, the model cell in this chapter uses local information to direct its movement instantaneously, which relates to eukaryotic direct spatial gradient measurement, where the cell estimates the gradient across the diameter of its body [ 29 , 30 ]. Theory has been developed to compare the effectiveness of spatial and temporal gradient sensing, which accounts for the discrete nature of cue particles by modelling their arrivals as Poisson-distributed discrete events, without explicitly modelling discrete cues [ 31 ].…”

Dynamics of a helical swimmer crossing viscosity gradients

“…Beyond individual behaviour, microorganisms may also exhibit collective dynamics through chemicallybased communication. For example, when a Dictyostelium cell (a type of mold) starves, it produces a chemical that induces a multicellular aggregation process, which allows the cells to survive long starvation periods [16,[19][20][21]. The mechanism behind this phenomenon is captured in the classical Keller-Segel model [22,23], and has been extended to describe some collective phenomena of E. coli bacteria showing chemo-attraction to self-produced autoinducers [24].…”