Quantitative modeling of bacterial quorum sensing dynamics in time and space
Xiang Li(李翔)1,2, Hong Qi(祁宏)3, Xiao-Cui Zhang(张晓翠)1, Fei Xu(徐飞)1, Zhi-Yong Yin(尹智勇)1, Shi-Yang Huang(黄世阳)4, Zhao-Shou Wang(王兆守)4,†, and Jian-Wei Shuai(帅建伟)1,2,5,‡
1Department of Physics, College of Physical Science and Technology, Xiamen University, Xiamen 361005, China 2State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, Xiamen University, Xiamen 361102, China 3Complex Systems Research Center, Shanxi University, Taiyuan 030006, China 4Institute of Biochemical Engineering, Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China 5National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361102, China
Quorum sensing (QS) refers to the cell communication through signaling molecules that regulate many important biological functions of bacteria by monitoring their population density. Although a wide spectrum of studies on the QS system mechanisms have been carried out in experiments, mathematical modeling to explore the QS system has become a powerful approach as well. In this paper, we review the research progress of network modeling in bacterial QS to capture the system’s underlying mechanisms. There are four types of QS system models for bacteria: the Gram-negative QS system model, the Gram-positive QS system model, the model for both Gram-negative and Gram-positive QS system, and the synthetic QS system model. These QS system models are mostly described by the ordinary differential equations (ODE) or partial differential equations (PDE) to study the changes of signaling molecule dynamics in time and space and the cell population density variations. Besides the deterministic simulations, the stochastic modeling approaches have also been introduced to discuss the noise effects on kinetics in QS systems. Taken together, these current modeling efforts advance our understanding of the QS system by providing systematic and quantitative dynamics description, which can hardly be obtained in experiments.
* Project supported by the National Natural Science Foundation of China (Grant Nos. 11704318, 11675134, and 11874310) and the China Postdoctoral ScienceFoundation (Grant No. 2016M602071).
Cite this article:
Xiang Li(李翔), Hong Qi(祁宏), Xiao-Cui Zhang(张晓翠), Fei Xu(徐飞), Zhi-Yong Yin(尹智勇), Shi-Yang Huang(黄世阳), Zhao-Shou Wang(王兆守)†, and Jian-Wei Shuai(帅建伟)‡ Quantitative modeling of bacterial quorum sensing dynamics in time and space 2020 Chin. Phys. B 29 108702
Fig. 1.
An overview of the QS systems in bacterial cell. (a) At low cell density, the concentration of autoinducer is low. (b) At high cell density, the autoinducer concentration reaches a threshold to induce corresponding gene expression to trigger QS. (c) Schematic of the QS system in Gram-negative bacteria. AHL is the autoinducer in the system. (d) Schematic of the QS system in Gram-positive bacteria. AIP is the autoinducer. (e) Schematic of the synthetic QS system in E. coli. The synthetic strategies mainly include regulator modification, promoter modification, and circuitry addition.
Year
Author
Method
Major conclusion
QS systems in Gram-negative bacteria
2000
James et al.
ODE & bifurcation analysis
lux genes can induce luminesce under the shortage of extracellular signal molecule
2001
Dockery et al.
ODE & PDE
The high and low states of auto-inducer are highly controlled by the size and local density of cells
2004
Chen et al.
ODE
Providing an important basis for the precise determination of the rhl QS system
2010
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ODE & bifurcation analysis
The cell density-dependent behavior of LuxR-AHL QS system depends on local cell-clustering and the geometry of the evolution space
2013
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ODE
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2016
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ODE
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ODE
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ODE
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ODE
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2001
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ODE
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2013
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ODE
The model can be used to predict the production of GFP
Table 1.
Representative models of different QS systems in bacteria.
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