Spreading of nonmotile bacteria on a hard agar plate: Comparison between agent-based and stochastic simulations

Rana, Navdeep; Ghosh, Pushpita; Perlekar, Prasad

doi:10.1103/physreve.96.052403

Cited by 14 publications

(31 citation statements)

References 50 publications

(102 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Simulation Model and Methodsmentioning

confidence: 99%

“…We assume that cells interact mechanically through repulsive interaction which is in accordance with the Hertzian theory of elastic contact. We adopt the same functional form of F rf as employed in previous studies [23, 24, 41, 42] and is given by: , where E represents the elastic modulus of the cells and h corresponds to the overlap between two interacting cells. Figure 1(b) schematically demonstrates the repulsive mechanical interaction between two bacteria where h = d 0 — r 0 and r 0 is the closest distance of approach between the two rod-shaped cells.…”

Section: Simulation Model and Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Mechanistic underpinning of nonuniform collective motions in swarming bacteria

Bera

Mondal

Ghosh

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Self-propelled bacteria can exhibit a large variety of non-equilibrium self-organized phenomena. Swarming is one such fascinating dynamical scenario where a number of motile individuals grouped into clusters and move in synchronized flows and vortices. While precedent investigations in rod-like particles confirm that increased aspect-ratio promotes alignment and order, recent experimental studies in bacteria Bacillus subtilis show a non-monotonic dependence of cell-aspect ratio on their swarming motion. Here, by computer simulations of an agent-based model of self-propelled, mechanically interacting, rod-shaped bacteria in overdamped condition, we explore the collective dynamics of bacterial swarm subjected to a variation of cell-aspect ratio. When modeled with an identical self-propulsion speed across a diverse range of cell aspect ratio, simulations demonstrate that both shorter and longer bacteria exhibit slow dynamics whereas the fastest speed is obtained at an intermediate aspect ratio. Our investigation highlights that the origin of this observed non-monotonic trend of bacterial speed and vorticity with cell-aspect ratio is rooted in the cell-size dependence of motility force. The swarming features remain robust for a wide range of surface density of the cells, whereas asymmetry in friction attributes a distinct effect. Our analysis identifies that at an intermediate aspect ratio, an optimum cell size and motility force promote alignment, which reinforces the mechanical interactions among neighboring cells leading to the overall fastest motion. Mechanistic underpinning of the collective motions reveals that it is a joint venture of the short-range repulsive and the size-dependent motility forces, which determines the characteristics of swarming.

show abstract

Section: Simulation Model and Methodsmentioning

confidence: 99%

Section: Simulation Model and Methodsmentioning

confidence: 99%

Mechanistic underpinning of nonuniform collective motions in swarming bacteria

Bera

Mondal

Ghosh

2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…To understand these factors, we carry out a parallel investigation of three simple well-studied models with different reaction dynamics [18][19][20][21][22]. We will show that, although for equal-diffusivity cases with no selective advantage all the models have similar statistical behavior, a slight imbalance in either the diffusivity or the selective advantage dramatically alters the outcome.…”

Section: Introductionmentioning

confidence: 96%

“…To understand these factors, we carry out a parallel study of three simple well-studied models with different reaction dynamics [13][14][15][16][17]. The models show very different behavior, as evidenced by Fig.…”

mentioning

confidence: 99%

Fixation in competing populations: Diffusion and strategies for survival

Singha

Perlekar

Barma

2020

Phys. Rev. Research

Self Cite

View full text Add to dashboard Cite

How should dispersal strategies be chosen to increase the likelihood of survival of a species? We obtain the answer for the spatially extended versions of three well-known models of two competing species with unequal diffusivities. Though identical at the mean-field level, the three models exhibit drastically different behavior leading to different optimal strategies for survival, with or without a selective advantage for one species. With conserved total particle number, dispersal has no effect on survival probability. With a fluctuating number, faster dispersal is advantageous if intraspecies competition is present, while moving slower is the optimal strategy for the disadvantaged species if there is no intraspecies competition: it is imperative to include fluctuations to properly formulate survival strategies.

show abstract

“…Indeed, several models have been introduced in the literature [16][17][18], e.g. to investigate biofilm jamming [19], nematic ordering [20,21], role of psl trails [22], nutrient concentration [23], phase separation [24], front propagation [25].…”

mentioning

confidence: 99%

In-silico modeling of early-stage biofilm formation

et al. 2021

View full text Add to dashboard Cite

Several bacteria and bacteria strands form biofilms in different environmental conditions, e.g. pH, temperature, nutrients, etc. Biofilm growth, therefore, is an extremely robust process. Because of this, while biofilm growth is a complex process affected by several variables, insights into biofilm formation could be obtained studying simple schematic models. In this manuscript, we describe a hybrid molecular dynamics and Monte Carlo model for the simulation of the early stage formation of a biofilm, to explicitly demonstrate that it is possible to account for most of the processes expected to be relevant. The simulations account for the growth and reproduction of the bacteria, for their interaction and motility, for the synthesis of extracellular polymeric substances and Psl trails. We describe the effect of these processes on the early stage formation of biofilms, in two dimensions, and also discuss preliminary three-dimensional results.

show abstract

Spreading of nonmotile bacteria on a hard agar plate: Comparison between agent-based and stochastic simulations

Cited by 14 publications

References 50 publications

Mechanistic underpinning of nonuniform collective motions in swarming bacteria

Mechanistic underpinning of nonuniform collective motions in swarming bacteria

Fixation in competing populations: Diffusion and strategies for survival

In-silico modeling of early-stage biofilm formation

Contact Info

Product

Resources

About