Sketch of the predominant motility patterns.

<p>a) Run-and-tumble, b) Run-reverse, and c) Run-reverse-flick. During a “run” event, a cell moves with high persistence. Runs are interrupted by reorientation events like tumbling or reversal. The time steps indicate the sequence of these events. An average turning angle after tumbling in <i>E. coli</i> bacteria is (a), whereas it is an almost perfect reversal of for many marine bacteria, or cells with twitching motility due to cell appendages, called pili (b). <i>V. alginolyticus</i> (c) alternates reversals (at ) with randomizing flicks (at ) with an average turning angle of .</p

Chemotactic drift speed as a function of for <i>E. coli</i> and <i>V. alginolyticus</i>.

<p>The plot on the left shows ; on the right, the chemotactic drift is normalized by the swimming speed as and coincides with the chemotactic index.</p

Velocity correlation function.

<p>The normalized velocity correlation function is plotted as a function of dimensionless time . The curves are shown for run-and-tumble of <i>E. coli</i> with persistence parameter (red), run-reverse with (green), and run-reverse-flick with alternating and (blue). The analytical expressions are given in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0081936#pone.0081936.e103" target="_blank">Eqs. (12)</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0081936#pone.0081936.e160" target="_blank">(21)</a>, respectively.</p

Comparison of the chemotactic drift speed versus persistence parameter between run-tumble-flick [Eq. (28)] and run-tumble [Eq. (27)].

<p>All parameters are adjusted to <i>E. coli</i> in the gradient with , , , and .</p

Comparison of the model and a real trypanosome during swimming motion (A–D).

<p>The swimming trajectory and dynamic shape of the simulated model trypanosome (top row) compares well with the forward swimming motion of the real trypanosome (middle and bottom row). Snapshots of the real trypanosome are taken at the indicated times from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003967#pcbi.1003967.s005" target="_blank">S4 Video</a>. Fluorescently labelled surface (middle row) or untreated cells (bottom row) were recorded by high speed microscopy (200–500 frames per second). The cells, which exhibit similar speeds and rotational frequencies, show matching cell body conformations at all times over a swimming path of several cell lengths. These periodically repeating shape conformations are initiated by the bending wave passing along the flagellum and determine the trajectory of the swimming parasite.</p

The model trypanosome and a real trypanosome.

<p>(a) Cell body of the model trypanosome without distortion. The elastic network made from vertices connected by springs defines the surface. The blue line connecting a series of vertices represents the flagellum with the helical half-turn. (b, c) Snapshots of the model trypanosome during simulated swimming motion. (d) 3d volume model of a live trypanosome with fluorescently labeled surface. (e) 3d surface model of the cell in (d), with the flagellum highlighted in blue. (f) The same surface model rotated about the horizontal, in order to get a better view on the left-handed half-turn of the flagellum indicated in red.</p

Editorial: Active and intelligent living matter: from fundamentals to applications

Editorial on the research topic: Active and intelligent living matter: from fundamentals to applications.</p

BodilisantA Novel Fluorescent, Highly Affine Histamine H₃ Receptor Ligand

A piperidine-based lead structure for the human histamine H₃ receptor (hH₃R) was coupled with the BODIPY fluorophore and resulted in a strong green fluorescent (quantum yield, 0.92) hH₃R ligand with affinity in the nanomolar concentration range (<i>K</i>_i hH₃R = 6.51 ± 3.31 nM), named Bodilisant. Screening for affinities at histamine and dopamine receptor subtypes showed high hH₃R preference. Bodilisant was used for visualization of hH₃R in hH₃R overexpressing HEK-293 cells with fluorescence confocal laser scanning microscopy. In addition, in native human brain tissues, Bodilisant showed clear and displaceable images of labeled hH₃R

Pili-Induced Clustering of <i>N. gonorrhoeae</i> Bacteria

<div><p>Type IV pili (Tfp) are prokaryotic retractable appendages known to mediate surface attachment, motility, and subsequent clustering of cells. Tfp are the main means of motility for <i>Neisseria gonorrhoeae</i>, the causative agent of gonorrhea. Tfp are also involved in formation of the microcolonies, which play a crucial role in the progression of the disease. While motility of individual cells is relatively well understood, little is known about the dynamics of <i>N. gonorrhoeae</i> aggregation. We investigate how individual <i>N. gonorrhoeae</i> cells, initially uniformly dispersed on flat plastic or glass surfaces, agglomerate into spherical microcolonies within hours. We quantify the clustering process by measuring the area fraction covered by the cells, number of cell aggregates, and their average size as a function of time. We observe that the microcolonies are also able to move but their mobility rapidly vanishes as the size of the colony increases. After a certain critical size they become immobile. We propose a simple theoretical model which assumes a pili-pili interaction of cells as the main clustering mechanism. Numerical simulations of the model quantitatively reproduce the experimental data on clustering and thus suggest that the agglomeration process can be entirely explained by the Tfp-mediated interactions. In agreement with this hypothesis mutants lacking pili are not able to form colonies. Moreover, cells with deficient quorum sensing mechanism show similar aggregation as the wild-type bacteria. Therefore, our results demonstrate that pili provide an essential mechanism for colony formation, while additional chemical cues, for example quorum sensing, might be of secondary importance.</p></div

Comparison of the model and data.

<p>Red curves show the simulation results, which are compared to to the experimental data points. Error bars are showing the standard error of mean. The initial conditions for simulations are taken from an experiment with 1291 particles, with 495 “active” and <i>m</i>_<i>p</i> = 796 “passive” particles. Diffusion constant as a function of cluster size is given in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0137661#pone.0137661.g004" target="_blank">Fig 4</a><i>D</i>₀ = 0.6 <i>μ</i>m² s⁻¹, with minimal and cut off cluster sizes set to <i>a</i>_<i>s</i> = 0.8<i>μm</i>, and <i>a</i>_cut = 3.5<i>μm</i>. The pili length <i>l</i>₀ = 1.6 <i>μ</i>m, growth rate <i>λ</i> = 4.2 × 10⁻⁵ s⁻¹, and a delay time for the establishment of pili-pili contact <i>T</i>_<i>w</i> = 18.3min.</p