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Agent-based modelling of biofilm formation and inhibition in Escherichia coli


Affiliations
1 International Institute of Information Technology, Bengaluru 560 100, India
 

Biofilm formation by bacteria such as Escherichia coli is a serious challenge faced in the treatment of infections. Biofilms provide a protected environment for the pathogens, where they may persist despite environmental adversities and treatments causing chronic infections. Furanones, both naturally occurring and synthetic, have been found to inhibit biofilm formation. An agent-based model of the behaviour of E. coli with regard to formation and inhibition of biofilms, is described here. Analytical tools used in this article allow us to find the optimal range of inhibitor concentration for Gram-negative bacteria. This is made possible by appropriate mathematical analysis, reducing the need for laborious experimental verification. The results are seen to be consistent with published experimental data on biofilm thickness of E. coli when acted upon by furanones. Our model permits estimation of concentration of the inhibitors needed to properly curb biofilms. This in turn has therapeutic implications, in that it may help formulate strategies to prevent the formation and growth of biofilms, especially in the context of devices placed inside the body, like catheters and implants.

Keywords

Agent-based modelling, biofilm, Escherichia coli, furanones.
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  • Chole, R. A., Olomu, O. and Wang, E. W., Bacterial biofilm infections in otology. Rec. Adv. Otolaryngol., 2009, 8, 21–29.

  • Jacom, E. B., Becker, I., Shapira, Y. and Levine, K., Bacterial linguistic communication and social intelligence. Trends Microbiol., 2004, 12(8), 366–372.

  • Costerton, J., Stewart, P. and Greenberg, E., Bacterial biofilms: a common cause of persistent infections. Science, 1999, 284, 1318–1322.

  • Roberts, M. E. and Stewart, P. S., Modelling protection from antimicrobial agents in biofilms through the formation of persister cells. Microbiology, 2005, 151, 75–80.

  • Bjarnsholt, T., The role of bacterial biofilms in chronic infections.APMIS, 2013, 121 (Suppl. 136), 1–58; doi: 10.1111/apm.12099.

  • Railsback, S. F. and Grimm, V., Agent-based and Individual-Based Modeling: A Practical Introduction, Princeton University Press, Princeton, NJ, USA, 2011.

  • Schelling, T., Dynamic models of segregation. J. Math. Soc.,1971, 1(2), 143–186.

  • Nisan, N., Roughgarden, T., Tardos, E. and Vazirani, V. V., Algorithmic Game Theory, Cambridge University Press, NY, USA,2007.

  • Shoham, Y. and Leyton-Brown, K., Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations, Cambridge University Press, 2008.

  • Stamatopoulou, I., Sakellariou, I. and Kefalas, P., Formal agentbased modelling and simulation of crowd behaviour in emergency evacuation plans. In 24th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2012), 2012, vol. 1, pp. 1133–1138.

  • Niazi, M. and Hussain, A., Agent based tools for modelling and simulation of self-organization in peer-to-peer, ad-hoc and other complex networks. IEEE Commun. Mag., 2009, 47, 163–173.

  • Totamane, R., Dasgupta, A. and Rao, S., Air cargo demand modelling and prediction. IEEE Syst. J., 2014, 8(1), 52–62; doi:10.1109/JSYST.2012.2218511.

  • North, M. J. and Macal, C. M., Managing Business Complexity: Discovering Strategic Solutions with Agent-based Modelling and Simulation, Oxford University Press, 2007.

  • Bonabeau, E., Agent-based modelling: methods and techniques for simulating human systems. In Proceedings of the National Academy of Sciences, USA, Supplement 3, 2006, pp. 7280–7287.

  • Parsons, S., Rodriguez-Aguilar, J. A. and Klein, M., Auctions and bidding: a guide for computer scientists. ACM Comput. Surv., 2011, 43(2); doi: 10.1145/1883612.1883617.

  • Gilbert, N. and Terna, P., How to build and use agent-based models in social science. Mind Soc., 2000, 1(1), 57–72; doi:10.1007/BF02512229.

  • Prasad, A. S. and Rao, S., A mechanism design approach to resource procurement in cloud computing. IEEE Trans. Comput., 2014, 63(1), 17–30; doi:10.1109/TC.2013.106.

  • Luck, M. and Merelli, E., Agents in bioinformatics. Knowl. Eng.Rev., 2005, 20(2), 117–125.

  • Macal, C. M. and North, M. J., Tutorial on agent-based modelling and simulation part 2: how to model with agents. In Proceedings of the 2006 Winter Simulation Conference (WSC 06), Monterey, California, 2006, pp. 73–83.

  • Athale, C., Mansury, Y. and Deisboeck, T., Simulating the impact of a molecular ‘decision-process’ on cellular phenotype and multicellular patterns in brain tumors. J. Theor. Biol., 2005, 233(4),469–481.

  • Paton, R., Gregory, R., Vlachos, C., Saunders, J. and Wu, H., Evolvable social agents for bacterial systems modeling. IEEE Trans. Nanobiosci., 2004, 3(3), 208–216.

  • Gorochowski, T. E. et al., BSim: an agent-based tool for modelling bacterial populations in systems and synthetic biology. PLoS ONE, 2012, 7.

  • Manfield, M., Nys, R. D. and Kumar, N., Evidence that halogenated furanones from Delisea pulchra inhibit acylated homoserine lactone (AHL)-mediated gene expression by displacing the AHL signal from its receptor protein. Microbiology, 1999, 145,283–291.

  • Picioreanu, C., Kreft, J.-U. and van Loosdrecht, M. C. M., Particlebased multidimensional multispecies biofilm model. Appl. Environ. Microbiol., 2004, 70, 3024–3040.

  • Fredrick, M. R., Kuttler, C., House, B., Muller, J. and Ebere, J., A mathematical model of quorum sensing in patchy biofilm communities with slow background flow. Can. Appl. Math. Q., 2010,18(3).

  • Fozard, J., Lees, M., King, J. and Logan, B., Inhibition of quorum sensing in a computational biofilm simulation. BioSystems, 2012, 109, 105–114.

  • Nys, R. D., Givscov, M., Kumar, N., Kjelleberg, S. and Steinberg, P., Furanones. Prog. Mol. Subcell. Biol., 2006, 42, 55–86.

  • Ren, D., Sims, J. J. and Wood, T. K., Inhibition of biofilm formation and swarming of E. coli by (5z)-4-bromo-5-(bromomethylene)-3-butyl-2(5h)-furanone. Environ. Microbiol., 2001, 3(9911), 731–736.

  • Hou, S., Duo, M., Han, Y., Luk, Y. Y. and Ren, D., Inhibiting microbial biofilm formation by brominated furanones. In Medical Devices Conferences, USA, August 2009.

  • Kreft, J.-U., Picioreanu, C., Wimpenny, J. W. T. and van Loosdrecht, M. C. M., Individual-based modelling of biofilms. Microbiology, 2001, 147, 2897–2912.

  • Monod, J., The growth of bacterial cultures. Annu. Rev. Microbiol., 1949, 3, 371–394.


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  • Agent-based modelling of biofilm formation and inhibition in Escherichia coli

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Authors

Anusha Modwal
International Institute of Information Technology, Bengaluru 560 100, India
Shrisha Rao
International Institute of Information Technology, Bengaluru 560 100, India

Abstract


Biofilm formation by bacteria such as Escherichia coli is a serious challenge faced in the treatment of infections. Biofilms provide a protected environment for the pathogens, where they may persist despite environmental adversities and treatments causing chronic infections. Furanones, both naturally occurring and synthetic, have been found to inhibit biofilm formation. An agent-based model of the behaviour of E. coli with regard to formation and inhibition of biofilms, is described here. Analytical tools used in this article allow us to find the optimal range of inhibitor concentration for Gram-negative bacteria. This is made possible by appropriate mathematical analysis, reducing the need for laborious experimental verification. The results are seen to be consistent with published experimental data on biofilm thickness of E. coli when acted upon by furanones. Our model permits estimation of concentration of the inhibitors needed to properly curb biofilms. This in turn has therapeutic implications, in that it may help formulate strategies to prevent the formation and growth of biofilms, especially in the context of devices placed inside the body, like catheters and implants.

Keywords


Agent-based modelling, biofilm, Escherichia coli, furanones.

References





DOI: https://doi.org/10.18520/cs%2Fv109%2Fi5%2F930-937