Limits...
Excitation and adaptation in bacteria-a model signal transduction system that controls taxis and spatial pattern formation.

Othmer HG, Xin X, Xue C - Int J Mol Sci (2013)

Bottom Line: Here we discuss models which reproduce many of the important behaviors of the system.The important characteristics of the signal transduction system are excitation and adaptation, and the latter implies that the transduction system can function as a "derivative sensor" with respect to the ligand concentration in that the DC component of a signal is ultimately ignored if it is not too large.We also discuss some of the spatial patterns observed in populations and indicate how cell-level behavior can be embedded in population-level descriptions.

View Article: PubMed Central - PubMed

Affiliation: School of Mathematics, University of Minnesota, Minneapolis,MN 55455, USA. othmer@math.umn.edu.

ABSTRACT
The machinery for transduction of chemotactic stimuli in the bacterium E. coli is one of the most completely characterized signal transduction systems, and because of its relative simplicity, quantitative analysis of this system is possible. Here we discuss models which reproduce many of the important behaviors of the system. The important characteristics of the signal transduction system are excitation and adaptation, and the latter implies that the transduction system can function as a "derivative sensor" with respect to the ligand concentration in that the DC component of a signal is ultimately ignored if it is not too large. This temporal sensing mechanism provides the bacterium with a memory of its passage through spatially- or temporally-varying signal fields, and adaptation is essential for successful chemotaxis. We also discuss some of the spatial patterns observed in populations and indicate how cell-level behavior can be embedded in population-level descriptions.

Show MeSH
Simulated E. coli patterns by a cell-based model. (a) Network formation from an uniform cell lawn; (b) Aggregate formation from the network; (c) Traveling wave formation from a single inoculum in the center. Adapted from [187] with permission.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC3676780&req=5

f8-ijms-14-09205: Simulated E. coli patterns by a cell-based model. (a) Network formation from an uniform cell lawn; (b) Aggregate formation from the network; (c) Traveling wave formation from a single inoculum in the center. Adapted from [187] with permission.

Mentions: where Ds and Df are the diffusion rates, γ defines the secretion rate of attractant by cells, k is the consumption rate of the nutrient, and μ is an unspecified degradation rate. Simulations of the hybrid cell-based model can predict the time sequence of the network and aggregate formation in liquid medium and swarm ring formation in agar as in the experiments of Budrene and Berg (cf.Figure (8)). In particular, these simulations can reproduce the sharp wave front of the swarm rings, in agreement with experiments.


Excitation and adaptation in bacteria-a model signal transduction system that controls taxis and spatial pattern formation.

Othmer HG, Xin X, Xue C - Int J Mol Sci (2013)

Simulated E. coli patterns by a cell-based model. (a) Network formation from an uniform cell lawn; (b) Aggregate formation from the network; (c) Traveling wave formation from a single inoculum in the center. Adapted from [187] with permission.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC3676780&req=5

f8-ijms-14-09205: Simulated E. coli patterns by a cell-based model. (a) Network formation from an uniform cell lawn; (b) Aggregate formation from the network; (c) Traveling wave formation from a single inoculum in the center. Adapted from [187] with permission.
Mentions: where Ds and Df are the diffusion rates, γ defines the secretion rate of attractant by cells, k is the consumption rate of the nutrient, and μ is an unspecified degradation rate. Simulations of the hybrid cell-based model can predict the time sequence of the network and aggregate formation in liquid medium and swarm ring formation in agar as in the experiments of Budrene and Berg (cf.Figure (8)). In particular, these simulations can reproduce the sharp wave front of the swarm rings, in agreement with experiments.

Bottom Line: Here we discuss models which reproduce many of the important behaviors of the system.The important characteristics of the signal transduction system are excitation and adaptation, and the latter implies that the transduction system can function as a "derivative sensor" with respect to the ligand concentration in that the DC component of a signal is ultimately ignored if it is not too large.We also discuss some of the spatial patterns observed in populations and indicate how cell-level behavior can be embedded in population-level descriptions.

View Article: PubMed Central - PubMed

Affiliation: School of Mathematics, University of Minnesota, Minneapolis,MN 55455, USA. othmer@math.umn.edu.

ABSTRACT
The machinery for transduction of chemotactic stimuli in the bacterium E. coli is one of the most completely characterized signal transduction systems, and because of its relative simplicity, quantitative analysis of this system is possible. Here we discuss models which reproduce many of the important behaviors of the system. The important characteristics of the signal transduction system are excitation and adaptation, and the latter implies that the transduction system can function as a "derivative sensor" with respect to the ligand concentration in that the DC component of a signal is ultimately ignored if it is not too large. This temporal sensing mechanism provides the bacterium with a memory of its passage through spatially- or temporally-varying signal fields, and adaptation is essential for successful chemotaxis. We also discuss some of the spatial patterns observed in populations and indicate how cell-level behavior can be embedded in population-level descriptions.

Show MeSH