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Volume 168, Issue 11

Unravelling microbial efflux through mathematical modelling Open Access

This article is part of the Antimicrobial Efflux collection.

Abstract

Mathematical modelling is a useful tool that is increasingly used in the life sciences to understand and predict the behaviour of biological systems. This review looks at how this interdisciplinary approach has advanced our understanding of microbial efflux, the process by which microbes expel harmful substances. The discussion is largely in the context of antimicrobial resistance, but applications in synthetic biology are also touched upon. The goal of this paper is to spark further fruitful collaborations between modellers and experimentalists in the efflux community and beyond.

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Keyword(s): efflux pumps , interdisciplinary , mathematical modelling and multidisciplinary
© 2022 The Authors
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License. This article was made open access via a Publish and Read agreement between the Microbiology Society and the corresponding author’s institution.
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2022-11-21
2024-04-25
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References

  1. Theuretzbacher U, Piddock LJV. Non-traditional antibacterial therapeutic options and challenges. Cell Host Microbe 2019; 26:61–72 [View Article] [PubMed]
     [Google Scholar]
  2. Hotinger JA, Morris ST, May AE. The case against antibiotics and for anti-virulence therapeutics. Microorganisms 2021; 9:10 [View Article] [PubMed]
     [Google Scholar]
  3. Fleitas Martínez O, Cardoso MH, Ribeiro SM, Franco OL. Recent advances in anti-virulence therapeutic strategies with a focus on dismantling bacterial membrane microdomains, toxin neutralization, quorum-sensing interference and biofilm inhibition. Front Cell Infect Microbiol 2019; 9:74 [View Article] [PubMed]
     [Google Scholar]
  4. Laws M, Shaaban A, Rahman KM. Antibiotic resistance breakers: current approaches and future directions. FEMS Microbiol Rev 2019; 43:490–516 [View Article] [PubMed]
     [Google Scholar]
  5. Aslam B, Wang W, Arshad MI, Khurshid M, Muzammil S et al. Antibiotic resistance: a rundown of a global crisis. Infect Drug Resist 2018; 11:1645–1658 [View Article] [PubMed]
     [Google Scholar]
  6. Bush K. Past and present perspectives on β-lactamases. Antimicrob Agents Chemother 2018; 62:10 [View Article] [PubMed]
     [Google Scholar]
  7. Du D, Wang-Kan X, Neuberger A, van Veen HW, Pos KM et al. Multidrug efflux pumps: structure, function and regulation. Nat Rev Microbiol 2018; 16:523–539 [View Article] [PubMed]
     [Google Scholar]
  8. Grkovic S, Brown MH, Skurray RA. Transcriptional regulation of multidrug efflux pumps in bacteria. Semin Cell Dev Biol 2001; 12:225–237 [View Article] [PubMed]
     [Google Scholar]
  9. Blair JMA, Smith HE, Ricci V, Lawler AJ, Thompson LJ et al. Expression of homologous RND efflux pump genes is dependent upon AcrB expression: implications for efflux and virulence inhibitor design. J Antimicrob Chemother 2015; 70:424–431 [View Article] [PubMed]
     [Google Scholar]
  10. Karlebach G, Shamir R. Modelling and analysis of gene regulatory networks. Nat Rev Mol Cell Biol 2008; 9:770–780 [View Article] [PubMed]
     [Google Scholar]
  11. Halfon MS. Perspectives on gene regulatory network evolution. Trends Genet 2017; 33:436–447 [View Article] [PubMed]
     [Google Scholar]
  12. Janga SC, Collado-Vides J. Structure and evolution of gene regulatory networks in microbial genomes. Res Microbiol 2007; 158:787–794 [View Article] [PubMed]
     [Google Scholar]
  13. Cherry JL, Adler FR. How to make a biological switch. J Theor Biol 2000; 203:117–133 [View Article] [PubMed]
     [Google Scholar]
  14. Tiwari A, Ray JCJ, Narula J, Igoshin OA. Bistable responses in bacterial genetic networks: designs and dynamical consequences. Math Biosci 2011; 231:76–89 [View Article] [PubMed]
     [Google Scholar]
  15. Otero-Muras I, Mannan AA, Banga JR, Oyarzún DA. Multiobjective optimization of gene circuits for metabolic engineering. IFAC-PapersOnLine 2019; 52:13–16 [View Article]
     [Google Scholar]
  16. Balagaddé FK, Song H, Ozaki J, Collins CH, Barnet M et al. A synthetic Escherichia coli predator-prey ecosystem. Mol Syst Biol 2008; 4:187 [View Article] [PubMed]
     [Google Scholar]
  17. Piddock LJV. Clinically relevant chromosomally encoded multidrug resistance efflux pumps in bacteria. Clin Microbiol Rev 2006; 19:382–402 [View Article] [PubMed]
     [Google Scholar]
  18. Alekshun MN, Levy SB. Regulation of chromosomally mediated multiple antibiotic resistance: the mar regulon. Antimicrob Agents Chemother 1997; 41:2067–2075 [View Article] [PubMed]
     [Google Scholar]
  19. Rodrigo G, Bajic D, Elola I, Poyatos JF. Antagonistic autoregulation speeds up a homogeneous response in Escherichia coli. Sci Rep 2016; 6:36196 [View Article] [PubMed]
     [Google Scholar]
  20. Garcia-Bernardo J, Dunlop MJ. Tunable stochastic pulsing in the Escherichia coli multiple antibiotic resistance network from interlinked positive and negative feedback loops. PLoS Comput Biol 2013; 9:e1003229 [View Article] [PubMed]
     [Google Scholar]
  21. El Meouche I, Siu Y, Dunlop MJ. Stochastic expression of a multiple antibiotic resistance activator confers transient resistance in single cells. Sci Rep 2016; 6:19538 [View Article] [PubMed]
     [Google Scholar]
  22. Kerr R, Jabbari S, Blair JMA, Johnston IG. Dynamic Boolean modelling reveals the influence of energy supply on bacterial efflux pump expression. J R Soc Interface 2022; 19:20210771 [View Article] [PubMed]
     [Google Scholar]
  23. Rodrigo G, Bajić D, Elola I, Poyatos JF. Deconstructing a multiple antibiotic resistance regulation through the quantification of its input function. NPJ Syst Biol Appl 2017; 3:30 [View Article] [PubMed]
     [Google Scholar]
  24. Rossi NA, Dunlop MJ. Customized regulation of diverse stress response genes by the multiple antibiotic resistance activator MarA. PLoS Comput Biol 2017; 13:e1005310 [View Article]
     [Google Scholar]
  25. Youlden GH, Ricci V, Wang-Kan X, Piddock LJV, Jabbari S et al. Time dependent asymptotic analysis of the gene regulatory network of the AcrAB-TolC efflux pump system in gram-negative bacteria. J Math Biol 2021; 82:31 [View Article] [PubMed]
     [Google Scholar]
  26. Rossi NA, Mora T, Walczak AM, Dunlop MJ. Active degradation of mara controls coordination of its downstream targets. PLoS Comput Biol 2018; 14:12 [View Article]
     [Google Scholar]
  27. Prajapat MK, Jain K, Saini S. Control of MarRAB Operon in Escherichia coli via autoactivation and autorepression. Biophys J 2015; 109:1497–1508 [View Article]
     [Google Scholar]
  28. Charlebois DA, Balázsi G, Kærn M. Coherent feedforward transcriptional regulatory motifs enhance drug resistance. Phys Rev E Stat Nonlin Soft Matter Phys 2014; 89:052708 [View Article]
     [Google Scholar]
  29. Diao J, Charlebois DA, Nevozhay D, Bódi Z, Pál C et al. Efflux pump control alters synthetic gene circuit function. ACS Synth Biol 2016; 5:619–631 [View Article]
     [Google Scholar]
  30. Langevin AM, Dunlop MJ. Stress introduction rate alters the benefit of AcrAB-TolC efflux pumps. J Bacteriol 2018; 200:e00525-17 [View Article]
     [Google Scholar]
  31. Wen X, Langevin AM, Dunlop MJ. Antibiotic export by efflux pumps affects growth of neighboring bacteria. Sci Rep 2018; 8:15120 [View Article] [PubMed]
     [Google Scholar]
  32. Gorochowski TE, Matyjaszkiewicz A, Todd T, Oak N, Kowalska K et al. BSim: an agent-based tool for modeling bacterial populations in systems and synthetic biology. PLoS One 2012; 7:e42790 [View Article] [PubMed]
     [Google Scholar]
  33. Lardon LA, Merkey BV, Martins S, Dötsch A, Picioreanu C et al. iDynoMiCS: next-generation individual-based modelling of biofilms. Environ Microbiol 2011; 13:2416–2434 [View Article] [PubMed]
     [Google Scholar]
  34. Sampaio NMV, Blassick CM, Andreani V, Lugagne JB, Dunlop MJ. Dynamic gene expression and growth underlie cell-to-cell heterogeneity in Escherichia coli stress response. Proc Natl Acad Sci U S A 2022; 119:14 [View Article] [PubMed]
     [Google Scholar]
  35. Blair JMA, Piddock LJV. How to measure export via bacterial multidrug resistance efflux pumps. mBio 2016; 7:e00840-16 [View Article] [PubMed]
     [Google Scholar]
  36. Youlden G, McNeil HE, Blair JMA, Jabbari S, King JR. Mathematical modelling highlights the potential for genetic manipulation as an adjuvant to counter efflux-mediated MDR in Salmonella. Bull Math Biol 2022; 84:56 [View Article] [PubMed]
     [Google Scholar]
  37. Perez AM, Gomez MM, Kalvapalle P, O’Brien-Gilbert E, Bennett MR et al. Using cellular fitness to map the structure and function of a major facilitator superfamily effluxer. Mol Syst Biol 2017; 13:12 [View Article] [PubMed]
     [Google Scholar]
  38. Nagano K, Nikaido H. Kinetic behavior of the major multidrug efflux pump AcrB of Escherichia coli. Proc Natl Acad Sci U S A 2009; 106:5854–5858 [View Article] [PubMed]
     [Google Scholar]
  39. Lim SP, Nikaido H. Kinetic parameters of efflux of penicillins by the multidrug efflux transporter AcrAB-TolC of Escherichia coli. Antimicrob Agents Chemother 2010; 54:1800–1806 [View Article] [PubMed]
     [Google Scholar]
  40. Nichols WW. Modeling the kinetics of the permeation of antibacterial agents into growing bacteria and its interplay with efflux. Antimicrob Agents Chemother 2017; 61:10 [View Article] [PubMed]
     [Google Scholar]
  41. Vesselinova N, Alexandrov BS, Wall ME. Dynamical model of drug accumulation in bacteria: sensitivity analysis and experimentally testable predictions. PLoS One 2016; 11:e0165899 [View Article] [PubMed]
     [Google Scholar]
  42. Asuquo AE, Piddock LJ. Accumulation and killing kinetics of fifteen quinolones for Escherichia coli, Staphylococcus aureus and Pseudomonas aeruginosa. J Antimicrob Chemother 1993; 31:865–880 [View Article] [PubMed]
     [Google Scholar]
  43. Whittle EE, McNeil HE, Trampari E, Webber M, Overton TW et al. Efflux impacts intracellular accumulation only in actively growing bacterial cells. mBio 2021; 12:e0260821 [View Article] [PubMed]
     [Google Scholar]
  44. Colijn C, Jones N, Johnston IG, Yaliraki S, Barahona M. Toward precision healthcare: context and mathematical challenges. Front Physiol 2017; 8:136 [View Article] [PubMed]
     [Google Scholar]
  45. Moore H. How to mathematically optimize drug regimens using optimal control. J Pharmacokinet Pharmacodyn 2018; 45:127–137 [View Article] [PubMed]
     [Google Scholar]
  46. Hoyle A, Cairns D, Paterson I, McMillan S, Ochoa G et al. Optimising efficacy of antibiotics against systemic infection by varying dosage quantities and times. PLoS Comput Biol 2020; 16:e1008037 [View Article] [PubMed]
     [Google Scholar]
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