Comparison between model results and experimental data for <i>E. coli</i> thermotaxis.

<p>The blue symbols represent the cell density data obtained in Ref. <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003672#pcbi.1003672-Demir2" target="_blank">[37]</a> at min after applying a shallow temperature gradient . The black dashed line is the inverse speed profile, where is a quadratic fitting to the measured swimming speed in Ref. <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003672#pcbi.1003672-Demir2" target="_blank">[37]</a>. The red solid line corresponds to model Eq. (13) with and . Other parameters used are the same to those in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003672#pcbi-1003672-g004" target="_blank">Fig. 4B</a>.</p

Inverted response to temperature changes and bacterial thermotaxis.

<p>(A) The steady-state methylation level subtract the critical methylation level, , and the receptor response to temperature changes, , as a function of temperature. The critical temperature is determined by the crossing point where (or equivalently ). Tar acts as a warm sensor for and a cold sensor for , which drives the cells towards from both sides. (B) The steady-state cell distribution, , as a function of temperature. For illustrative purposes, we assume that the swimming speed, , increases linearly with temperature and that the motor dissociation constant is , with a constant parameter . Three cases are considered. The red solid line corresponds the case where both and are constant; the blue dot-dashed lines is for the case of constant (i.e. ) and ; the green dashed line is generated by using and . Evidently, the steady-state cell distribution can be changed by the temperature dependence of the speed , but it is insensitive to the temperature dependence of motor sensitivity . Here, we fix in all numerical examples. Other parameters used include: , , , , , , , , , and .</p

Behaviors and Strategies of Bacterial Navigation in Chemical and Nonchemical Gradients

<div><p>Navigation of cells to the optimal environmental condition is critical for their survival and growth. <i>Escherichia coli</i> cells, for example, can detect various chemicals and move up or down those chemical gradients (i.e., chemotaxis). Using the same signaling machinery, they can also sense other external factors such as pH and temperature and navigate from both sides toward some intermediate levels of those stimuli. This mode of precision sensing is more sophisticated than the (unidirectional) chemotaxis strategy and requires distinctive molecular mechanisms to encode and track the preferred external conditions. To systematically study these different bacterial taxis behaviors, we develop a continuum model that incorporates microscopic signaling events in single cells into macroscopic population dynamics. A simple theoretical result is obtained for the steady state cell distribution in general. In particular, we find the cell distribution is controlled by the intracellular sensory dynamics as well as the dependence of the cells' speed on external factors. The model is verified by available experimental data in various taxis behaviors (including bacterial chemotaxis, pH taxis, and thermotaxis), and it also leads to predictions that can be tested by future experiments. Our analysis help reveal the key conditions/mechanisms for bacterial precision-sensing behaviors and directly connects the cellular taxis performances with the underlying molecular parameters. It provides a unified framework to study bacterial navigation in complex environments with chemical and non-chemical stimuli.</p></div

The cell density profiles under two opposing chemical and thermal gradients.

<p>The temperature gradient used here is from to in a channel of length . Different density profiles correspond to different attractant (MesAsp) gradients 0.0, 3.0, 5.0, and 6.0 but the same concentration at the middle point: . Other parameters used are the same to those in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003672#pcbi-1003672-g004" target="_blank">Fig. 4B</a>.</p

The cell density distributions for bacterial chemotaxis.

<p>Different density profiles correspond to different gradients of varying steepness . Symbols represent the experimental data from Ref. <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003672#pcbi.1003672-Kalinin1" target="_blank">[32]</a>, whereas lines denote the fitting of our Eq. (7) to the data. We used , for MeAsp here.</p

Illustration of the multiscale architecture of our unified model.

<p>Environmental signals are sensed by the transmembrane receptor-kinase complexes which controls the level of the intracellular response regulator (CheY-P). The response regulator controls the rotational direction of flagellar motors and the bacterial tumble frequency. Some environmental factors (such as temperature) can also affect bacterial swimming speed and motor switching dynamics. The population distribution of cells is finally shaped by the alternating tumble and swim behaviors.</p

Tunability and accuracy of bacterial pH taxis.

<p>(A) The preferred pH versus the logarithm of the Tar/Tsr ratio to base for three representative parameter regimes: , , and . The red symbols represent the experimental data from Ref. <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003672#pcbi.1003672-Yang1" target="_blank">[8]</a> and seem to coincide with the model curve for . (B) The standard deviations of the cell distributions as a function of for the three representative parameter regimes: , , and . In the above numerical examples, we have fixed , and .</p

MOESM1 of Mycoalgae biofilm: development of a novel platform technology using algae and fungal cultures

Additional file 1: Figure S1. ATR-FTIR spectra of the pure cultures (Chlorella vulgaris; Mucor sp.) and mycoalgae biofilm. Region 1 is the fatty acid region, Region 2 is the protein region, Region 3 is mixed region and Region 4 is polysaccharide region

Schematic illustration of the effective potential for chemotaxis, pH taxis, and thermotaxis.

<p>In the case of chemotaxis, decreases monotonically as the chemoattractant concentration increases. For pH taxis, decreases with pH for Tsr-only mutant cells and increase with pH for Tar-only mutant. Based on the push-pull mechanism, for the wild-type <i>E. coli</i> represents the balancing effect between Tar and Tsr, leading to a local minimum in the effective potential. In the case of thermotaxis, can be shifted by the effect of temperature-dependent swimming speed . It is, however, insensitive to other temperature effects such as the temperature dependence of motor response, , parameterized by .</p

High Upconversion Efficiency from Hetero Triplet–Triplet Annihilation in Multiacceptor Systems

We report the observation of very high triplet–triplet annihilation (TTA) upconversion efficiency in single-sensitizer/multiacceptor systems. A hetero-TTA process between triplet acceptors of different types is believed to account for the synergistic effect leading to the high upconversion efficiency. The upconversion quantum yield of the dual-acceptor system is much higher than the sum of the two single-acceptor systems