Please wait a minute...
Chin. Phys. B, 2020, Vol. 29(10): 108702    DOI: 10.1088/1674-1056/abb225
Special Issue: SPECIAL TOPIC — Modeling and simulations for the structures and functions of proteins and nucleic acids
Topical Review—Modeling and simulations for the structures and functions of proteins and nucleic acids Prev   Next  

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,
1 Department of Physics, College of Physical Science and Technology, Xiamen University, Xiamen 361005, China
2 State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, Xiamen University, Xiamen 361102, China
3 Complex Systems Research Center, Shanxi University, Taiyuan 030006, China
4 Institute of Biochemical Engineering, Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
5 National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361102, China
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      Published:  05 October 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.

[1]
Miller M B, Bassler B L 2001 Annu. Rev. Microbiol. 55 165 DOI: 10.1146/annurev.micro.55.1.165
[2]
Grandclément C, Tannières M, Moréra S, Dessaux Y, Faure D 2016 FEMS Microbiol. Rev. 40 86 DOI: 10.1093/femsre/fuv038
[3]
Diggle S P, Griffin A S, Campbell G S, West S A 2007 Nature 450 411 DOI: 10.1038/nature06279
[4]
Hooshangi S, Bentley W E 2008 Curr. Opin. Biotechnol. 19 550 DOI: 10.1016/j.copbio.2008.10.007
[5]
Hagen S J, Son M, Weiss J T, Young J H 2010 J. Biol. Phys. 36 317 DOI: 10.1007/s10867-010-9186-4
[6]
Ng W L, Bassler B L 2009 Annu. Rev. Genet. 43 197 DOI: 10.1146/annurev-genet-102108-134304
[7]
Tsai C S, Winans S C 2010 Mol. Microbiol. 77 1072 DOI: 10.1111/mmi.2010.77.issue-5
[8]
Daniels R, Vanderleyden J, Michiels J 2004 FEMS Microbiol. Rev. 28 261 DOI: 10.1016/j.femsre.2003.09.004
[9]
Hao Y, Winans S C, Glick B R, Charles T C 2010 Environ. Microbiol. 12 105 DOI: 10.1111/emi.2010.12.issue-1
[10]
Marchand N, Collins C H 2016 ACS Synth. Biol. 5 597 DOI: 10.1021/acssynbio.5b00099
[11]
Annous B A, Fratamico P M, Smith J L 2009 J. Food. Sci. 74 R24 DOI: 10.1111/j.1750-3841.2008.01022.x
[12]
Antunes L C M, Ferreira R B R, Buckner M M C, Finlay B B 2010 Microbiology 156 2271 DOI: 10.1099/mic.0.038794-0
[13]
Rutherford S T, Bassler B L 2012 Cold Spring Harb. Perspect. Med. 2 a012427 DOI: 10.1101/cshperspect.a012427
[14]
Deep A, Chaudhary U, Gupta V J 2011 J. Lab. Physicians 3 4 DOI: 10.4103/0974-2727.78553
[15]
Defoirdt T, Miyamoto C M, Wood T K, Meighen E A, Sorgeloos P, Verstraete W, Bossier P Environ 2007 Microbiol. 9 2486 DOI: 10.1111/j.1462-2920.2007.01367.x
[16]
Fernandes R, Roy V, Wu H C, Bentley W E 2010 Nat. Nanotechnol. 5 213 DOI: 10.1038/nnano.2009.457
[17]
Li J, Wang L, Hashimoto Y, Tsao C Y, Wood T K, Valdes J J, Zafiriou E, Bentley W E 2006 Mol. Syst. Biol. 2 67 DOI: 10.1038/msb4100107
[18]
Daniel R, Rubens J R, Sarpeshkar R, Lu T K 2013 Nature 497 619 DOI: 10.1038/nature12148
[19]
Shuai J W, Jung P 2003 Proc. Natl. Acad. Sci. USA 100 506 DOI: 10.1073/pnas.0236032100
[20]
Shuai J, Jung P 2005 Phys. Rev. Lett. 95 114501 DOI: 10.1103/PhysRevLett.95.114501
[21]
Li X, Zhong J, Gao X, Wu Y, Shuai J, Qi H 2017 Chin. Phys. B 26 128703 DOI: 10.1088/1674-1056/26/12/128703
[22]
Liu Y, Zhang X, Wu Y, Liu W, Li X, Liu R, Liu L, Shuai J 2017 Chin. Phys. B 26 128707 DOI: 10.1088/1674-1056/26/12/128707
[23]
Qi H, Jiang Y, Yin Z, Jiang K, Li L, Shuai J 2018 Phys. Chem. Chem. Phys. 20 1964 DOI: 10.1039/C7CP06726G
[24]
Li X, Chen Y, Qi H, Liu L, Shuai J 2016 Oncotarget 7 34599 DOI: 10.18632/oncotarget.v7i23
[25]
Li X, Liu F, Shuai J W 2016 Acta. Phys. Sin. 65 178704 in Chinese DOI: 10.7498/aps.65.178704
[26]
Li X, Zhong C Q, Yin Z, Qi H, Xu F, He Q, Shuai J 2020 Int. J. Mol. Sci. 21 3061 DOI: 10.3390/ijms21093061
[27]
Pérez-Velázquez J, Gölgeli M, García-Contreras R 2016 Bull. Math. Biol. 78 1585 DOI: 10.1007/s11538-016-0160-6
[28]
Koerber A J, King J R, Williams P 2005 J. Math. Biol. 50 440 DOI: 10.1007/s00285-004-0296-0
[29]
Hunter G A, Keener J P 2014 J. Theor. Biol. 340 38 DOI: 10.1016/j.jtbi.2013.08.034
[30]
Chen M H, Liu H H, Yan F 2019 Phys. Rev. E. 99 062405 DOI: 10.1103/PhysRevE.99.062405
[31]
Chen F, Chen C C, Riadi L, Ju L K 2004 Biotechnol. Prog. 20 1325 DOI: 10.1021/bp049928b
[32]
Mcintosh M, Czuppon P, Best K, Becker A, Pfaffelhuber P 2013 Int. J. Biomath. Biostat. 2 59
[33]
Marenda M, Zanardo M, Trovato A, Seno F, Squartini A 2016 Sci. Rep. 6 39142 DOI: 10.1038/srep39142
[34]
James S, Nilsson P, James G, Kjelleberg S, Fagerström T 2000 J. Mol. Biol. 296 1127 DOI: 10.1006/jmbi.1999.3484
[35]
Melke P, Sahlin P, Levchenko A, Jönsson H 2010 PLoS Comput. Biol. 6 e1000819 DOI: 10.1371/journal.pcbi.1000819
[36]
Dockery J D, Keener J P 2001 Bull. Math. Biol. 63 95 DOI: 10.1006/bulm.2000.0205
[37]
Barbarossa M, Kuttler C 2016 Appl. Sci. 6 149 DOI: 10.3390/app6050149
[38]
Gustafsson E, Nilsson P, Karlsson S, Arvidson S 2004 J. Mol. Microbiol. Biotechnol. 8 232 DOI: 10.1159/000086704
[39]
Karlsson D, Karlsson S, Gustafsson E, Normark B H, Nilsson P 2007 Biosystems 90 211 DOI: 10.1016/j.biosystems.2006.08.005
[40]
Pereira C S, Thompson J A, Xavier K B 2013 FEMS Microbiol. Rev. 37 156 DOI: 10.1111/j.1574-6976.2012.00345.x
[41]
Banik S K, Fenley A T, Kulkarni R V 2009 Phys. Biol. 6 046008 DOI: 10.1088/1478-3975/6/4/046008
[42]
Long T, Tu K C, Wang Y, Mehta P, Ong N P, Bassler B L, Wingreen N S 2009 PLoS Biol. 7 e68 DOI: 10.1371/journal.pbio.1000068
[43]
Teng S W, Schaffer J N, Tu K C, Mehta P, Lu W, Ong N P, Bassler B L, Wingreen N S 2011 Mol. Syst. Biol. 7 491 DOI: 10.1038/msb.2011.30
[44]
Teng S W, Wang Y, Tu K C, Long T, Mehta P, Wingreen N S, Bassler B L, Ong N P 2010 Biophys. J. 98 2024 DOI: 10.1016/j.bpj.2010.01.031
[45]
Lee S B, Bailey J E 2002 Biotechnol. Bioeng. 79 550 DOI: 10.1002/(ISSN)1097-0290
[46]
Nilsson P, Olofsson A, Fagerlind M, Fagerström T, Rice S, Kjelleberg S, Steinberg P 2001 J. Mol. Biol. 309 631 DOI: 10.1006/jmbi.2001.4697
[47]
You L, Cox R S 3rd, Weiss R, Arnold F H 2004 Nature 428 868 DOI: 10.1038/nature02491
[48]
Chen L, Wang R, Zhou T, Aihara K 2005 Bioinformatics 21 2722 DOI: 10.1093/bioinformatics/bti392
[49]
Li J, Wang L, Hashimoto Y, Tsao C Y, Wood T K, Valdes J J, Zafiriou E, Bentley W E 2006 Mol. Syst. Biol. 2 67 DOI: 10.1038/msb4100107
[50]
Tian T, Burrage K 2006 Proc. Natl. Acad. Sci. USA 103 8372 DOI: 10.1073/pnas.0507818103
[51]
Saeidi N, Arshath M, Chang M W, Poh C L 2013 Chem. Eng. Sci. 103 91 DOI: 10.1016/j.ces.2012.12.016
[52]
Wang Z, Wu X, Peng J, Hu Y, Fang B, Huang S 2014 PLoS One 9 e104578 DOI: 10.1371/journal.pone.0104578
[53]
Kannan R E, Supreet S 2018 INAE Letters 3 175 DOI: 10.1007/s41403-018-0047-y
[54]
Shu J, Wu C, Wu Y, Li Z, Shao S, Zhao W, Tang X, Yang H, Shen L, Zuo X 2013 Cell 5 963 DOI: 10.1016/j.cell.2013.05.001
[55]
Chao Y S, Wang H L, Qi Ouyang, Tu Y H 2015 Nat. Phys. 11 772 DOI: 10.1038/nphys3412
[1] Internal friction behavior of Zr59Fe18Al10Ni10Nb3 metallic glass under different aging temperatures
Israa Faisal Ghazi, Israa Meften Hashim, Aravindhan Surendar, Nalbiy Salikhovich Tuguz, Aseel M. Aljeboree, Ayad F. Alkaim, and Nisith Geetha. Chin. Phys. B, 2021, 30(2): 026401.
[2] Structure-dependent behaviors of diode-triggered silicon controlled rectifier under electrostatic discharge stress
Li-Zhong Zhang(张立忠), Yuan Wang(王源), Yan-Dong He(何燕冬). Chin. Phys. B, 2016, 25(12): 128501.
[3] Dynamic analysis of a new chaotic system with fractional order and its generalized projective synchronization
Niu Yu-Jun, Wang Xing-Yuan, Nian Fu-Zhong, Wang Ming-Jun. Chin. Phys. B, 2010, 19(12): 120507.
No Suggested Reading articles found!