Behaviors and Strategies of Bacterial Navigation in Chemical and Nonchemical Gradients

Abstract

Navigation of cells to the optimal environmental condition is critical for their survival and growth. Escherichia coli 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.

Networked Chemoreceptors Benefit Bacterial Chemotaxis Performance

“…However, the kinase responses in networked receptor arrays can be highly cooperative (15–17), indicating that their activity controls are effectively coupled. Although the molecular basis for this coupling is not yet clear, various phenomenological models, based on the Monod-Wyman-Changeaux (MWC) model of allosteric transitions in proteins (18), have been successfully applied to quantitatively describe the signaling properties of this system (3, 16, 19, 20) and the way in which it controls bacterial behaviors (21). However, to date there have been no direct comparisons of the signaling and behavioral properties of cells with uncoupled core-signaling complexes versus cells with networked signaling arrays.…”

Barrier Crossing in Escherichia coli Chemotaxis

“…Predictive models have been developed based on knowledge of the bacterial signaling pathway and quantitative molecular and celluar experiments [10–13]. A modeling framework based on the intracellular signaling dynamics and the motor response has also been developed to study cellular and population behaviors [14–17]. …”

“…In our previous work, a mean field theory based on intracellular signaling dynamics was developed for studying population level bacterial chemotaxis behaviors [15, 17]. Briefly, the tumbling rate z t = τ −1 ( a/a 0 ) H is modulated by chemoreceptors activity a , where τ and a 0 are the average run time and activity of chemoreceptors at steady state, H (≈ 10) is the Hill coefficient [23].…”

pH-Taxis of Biohybrid Microsystems

“…Temperature responses and chemotaxis of S. marcescens have been characterized and have also been found to resemble those of E. coli 2932. Since a common signaling machinery is suggested for chemotaxis, thermotaxis, and pH-taxis25, it is expected that S. marcescens also possesses a similar pH-tactic behavior as E. coli .…”