Flagellar number governs bacterial spreading and transport efficiency

Najafi, Javad; Shaebani, M. Reza; John, Thomas; Altegoer, Florian; Bange, Gert; Wagner, Christian

doi:10.1126/sciadv.aar6425

Cited by 40 publications

(44 citation statements)

References 54 publications

(109 reference statements)

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“…Other approaches to simulate self-propelled particle systems include active lattice gas model and (kinetic) Monte Carlo approach [14][15][16][17][18][19][20]. b| Shape and architecture: Extensions of the active Brownian sphere model include additional active torques 7,8 , asymmetric shapes, e.g.…”

Section: Active Brownian Particlesmentioning

confidence: 99%

Computational models for active matter

et al. 2020

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A variety of computational models have been developed to describe active matter at different length and time scales. The diversity of the methods and the challenges in modeling active matterranging from molecular motors and cytoskeletal filaments over artificial and biological swimmers on microscopic to groups of animals on macroscopic scales-mainly originate from their out-ofequilibrium character, multiscale nature, nonlinearity, and multibody interactions. In the present review, various modeling approaches and numerical techniques are addressed, compared, and differentiated to illuminate the innovations and current challenges in understanding active matter. The complexity increases from minimal microscopic models of dry active matter toward microscopic models of active matter in fluids. Complementary, coarse-grained descriptions and continuum models are elucidated. Microscopic details are often relevant and strongly affect collective behaviors, which implies that the selection of a proper level of modeling is a delicate choice, with simple models emphasizing universal properties and detailed models capturing specific features. Finally, current approaches to further advance the existing models and techniques to cope with real-world applications, such as complex media and biological environments, are discussed.2

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Section: Active Brownian Particlesmentioning

confidence: 99%

Computational models for active matter

et al. 2020

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“…The number of flagella is a fundamental control parameter for multi-flagellated bacteria, and its connection to bacterial spreading and speed of locomotion has been previously investigated 23,45 . In the current study, our kinematic model for tangling allows us to examine whether the number of flagella could be linked to the robustness of bundling.…”

Section: Discussionmentioning

confidence: 99%

Geometrical Constraints on the Tangling of Bacterial Flagellar Filaments

Tătulea-Codrean

Lauga

2020

Sci Rep

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Many species of bacteria swim through viscous environments by rotating multiple helical flagella. The filaments gather behind the cell body and form a close helical bundle, which propels the cell forward during a "run". The filaments inside the bundle cannot be continuously actuated, nor can they easily unbundle, if they are tangled around one another. The fact that bacteria can passively form coherent bundles, i.e. bundles which do not contain tangled pairs of filaments, may appear surprising given that flagella are actuated by uncoordinated motors. In this article, we establish the theoretical conditions under which a pair of rigid helical filaments can form a tangled bundle, and we compare these constraints with experimental data collected from the literature. Our results suggest that bacterial flagella are too straight and too far apart to form tangled bundles based on their intrinsic, undeformed geometry alone. This makes the formation of coherent bundles more robust against the passive nature of the bundling process, where the position of individual filaments cannot be controlled.There is arguably no complex geometrical structure more central to biology than the helix. Two helical strands of nucleic acid spiral around each other to form the blueprint of life 1 , the arteries and vein that make up the human umbilical cord are twisted in a helical pattern 2 , and our human hearts beat with a spiralling contraction of the myocardium 3 . Many large eukaryotic cells, particularly in plants, set up helical flows to enhance chemical transport within the cell 4 and some viruses also take this shape 5 . Prokaryotes too have learnt how to assemble blocks of proteins into a helical structure and put it to good use 6-9 . Indeed, many species of bacteria are able to propel themselves through the viscous medium they inhabit by exploiting an apparatus known as flagellum, whereby a relatively rigid helical filament is actuated by a specialised constant-torque rotary motor 10-12 . In the case of multi-flagellated bacteria, the filaments are swept behind the cell body and rotate together in a coherent bundle, which leads to an interval of straight swimming called a "run" 13 . To change swimming direction, at least one motor must switch its sense of rotation, upon which the associated flagellum will leave the bundle and generate an imbalance of forces. The subsequent reorientation of the cell is called a "tumble" 14 .This run-and-tumble behaviour lies at the heart of bacterial motility. It is known that the initial stage of bundling is enabled by an elastohydrodynamic instability of the hook 15 , and from there onwards both the counter-rotation of the cell-body and hydrodynamic interactions between the filaments contribute to the synchronization and formation of the bundle [16][17][18][19][20][21][22] . Computational studies have investigated the effect of the number of flagella on the motility of the cell 23 , and the role played by polymorphic transformations 24 and mismatched motor torques 25 in the bundling and unbundling...

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“…With a proper P(f ) and P(τ ), the experimentally observed run-and-tumble dynamics can be quantitatively explained. Typically, the run-andtumble dynamics is modeled with a time-independent constant transition rate between the two phases [41,42]. In our model, this is the case governed by the poissonian P(τ ).…”

Section: The Run-and-tumble Dynamicsmentioning

confidence: 99%

Langevin Dynamics Driven by a Telegraphic Active Noise

Song

2019

Front. Phys.

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Self-propelled or active particles are referred to as the entities which exhibit anomalous transport violating the fluctuation-dissipation theorem by means of taking up an athermal energy source from the environment. Currently, a variety of active particles and their transport patterns have been quantified based on novel experimental tools such as single-particle tracking. However, the comprehensive theoretical understanding for these processes remains challenging. Effectively the stochastic dynamics of these active particles can be modeled as a Langevin dynamics driven by a particular class of active noise. In this work, we investigate the corresponding Langevin dynamics under a telegraphic active noise. By both analytical and computational approaches, we study in detail the transport and nonequilibrium properties of this process in terms of physical observables such as the velocity autocorrelation, heat current, and the mean squared displacement. It is shown that depending on the properties of the amplitude and duration time of the telegraphic noise various transport patterns emerge. Comparison with other active dynamics models such as the run-and-tumble and Lévy walks is also presented.

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Flagellar number governs bacterial spreading and transport efficiency

Abstract: We show that the flagellar number affects the intrinsic dynamics of swimming bacteria and governs their transport efficiency.

Cited by 40 publications

References 54 publications

Computational models for active matter

Computational models for active matter

Geometrical Constraints on the Tangling of Bacterial Flagellar Filaments

Langevin Dynamics Driven by a Telegraphic Active Noise

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