Quantitative modeling of bacterial quorum sensing dynamics in time and space

doi:10.1088/1674-1056/abb225

Abstract

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.

Keywords: bacterial quorum sensing signaling molecules mathematical modeling dynamic analysis

Received: 20 June 2020
Revised: 14 August 2020
Accepted manuscript online: 25 August 2020

PACS:	87.17.Aa	(Modeling, computer simulation of cell processes)
	87.18.Vf	(Systems biology)
	87.15.km	(Protein-protein interactions)

Corresponding Authors: ^†Corresponding author. E-mail: wzs@xmu.edu.cn第一通讯作者 ^‡Corresponding author. E-mail: jianweishuai@xmu.edu.cn

About author:

†Corresponding author. E-mail: wzs@xmu.edu.cn

‡Corresponding author. E-mail: jianweishuai@xmu.edu.cn

* 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	Melke et al.	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	Mcintosh et al.	ODE	A negative feedback is required for state changing of the QS system in Sinorhizobium meliloti
2016	Barbarossa et al.	DDE & bifurcation analysis	The delay QS system is sufficient to explain the biological observations
2016	Marenda et al.	ODE	Demonstrating how tube height overtakes the role of producer density in triggering sensor activation
QS systems in Gram-positive bacteria
2004	Gustafsson et al.	ODE	Altering agr activity hardly affects RNAIII levels but changes the cells sensitivity to AIP
2005	Koerber et al.	ODE & Monte-Carlo	The first stochastic model for bacterial QS system
2007	Karlsson et al.	ODE & bifurcation analysis	A putative ComX-dependent repressor which inhibits the expression of comCDE and comX is determined
QS systems in Gram-positive and-negative bacteria
2009	Banik et al.	ODE	The key dimensionless parameters that control the QS system of Vibrio harveyi is determined
2009	Long et al.	ODE	Quantifying the integration of QS system in Vibrio harveyi
2014	Hunter et al.	ODE	Qrr in Vibrio cholerae is more abundant and more sensitive to the changes in LuxO than Vibrio harveyi
Synthetic QS systems in bacteria
2001	Nilsson et al.	ODE	This study quantitatively explored the implications of the components for AHL regulation under different situations
2002	Lee et al.	ODE	The first molecular mechanism-based model in a recombinant E. coli system
2004	You et al.	ODE	A cell density control circuit that incorporates cell death was designed.
2005	Chen et al.	ODE & bifurcation analysis	Noises are essential for inducing the system cooperative behaviors
2006	Li et al.	ODE & stochastic simulations	Biological steps were determined to be exist in the synthesis of AI-2
2013	Saeidi et al.	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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Quantitative modeling of bacterial quorum sensing dynamics in time and space

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