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All issues Volume 17 (2022) Math. Model. Nat. Phenom., 17 (2022) 29 References
Open Access
Issue
Math. Model. Nat. Phenom.
Volume 17, 2022
Article Number 29
Number of page(s) 15
DOI https://doi.org/10.1051/mmnp/2022031
Published online 18 August 2022
  1. G.V. Alekseev, R.V. Brizitskii and Z.Y. Saritskaya, Stability estimates of extremum problem’s solutions for nonlinear convection-diffusion-reaction equation. Sib. J. Ind. Math. 10 (2016) 155–167. [Google Scholar]
  2. M. Ananda, I. Rusmana and A. Akhdiya, Quorum quenching of Bacillus cereus INT1c against Pseudomonas syringae. J. Phys.: Conf. Series 1277 (2019) 012010. [CrossRef] [Google Scholar]
  3. K. Anguige, J.R. King, J.P. Ward and P. Williams, Mathematical modelling of therapies targeted at bacterial quorum sensing. Math. Biosci. 192 (2004) 39–83. [CrossRef] [MathSciNet] [Google Scholar]
  4. A.K. Bhardwaj, K. Vinothkumar and N. Rajpara, Bacterial quorum sensing inhibitors: attractive alternatives for control of infectious pathogens showing multiple drug resistance. Recent Pat. Antiinfect. Drug Discov. 8 (2013) 68–83. [CrossRef] [Google Scholar]
  5. R.V. Brizitskii and Z.Y. Saritskaya, Optimization analysis of the inverse coefficient problem for the nonlinear convectiondiffusion-reaction equation. J. Inv. Ill-Posed Probl. 9 (2018) 821–834. [CrossRef] [Google Scholar]
  6. A.Y. Chebotarev, A.E. Kovtanyuk, G.V. Grenkin, N.D. Botkin and K.-H. Hoffmann, Nondegeneracy of optimality conditions in control problems for a radiative-conductive heat transfer model. Appl. Math. Comput. 289 (2016) 371–380. [MathSciNet] [Google Scholar]
  7. J.D. Dockery and J.P. Keener, A mathematical model for quorum sensing in Pseudomonas aeruginosa. Bull. Math. Biol. 63 (2001) 95–116. [CrossRef] [Google Scholar]
  8. M.G. Fagerlind, P. Nilsson, M. Harlen, S. Karlsson, S.A. Rice and S. Kjelleberg, Modeling the effect of acylated homoserine lactone antagonists in Pseudomonas aeruginosa. Biosystems 80 (2005) 201–213. [CrossRef] [PubMed] [Google Scholar]
  9. A. Fekete, C. Kuttler, M. Rothballer, B.A. Hense, D. Fischer, K. Buddrus-Schiemann, M. Lucio, J. Muller, P. Schmitt-Kopplin and A. Hartmann, Dynamic regulation of N-acyl-homoserine lactone production and degradation in Pseudomonas putida IsoF. FEMS Microbiol. Ecol. 72 (2010) 22–34. [CrossRef] [Google Scholar]
  10. M. Fernandez, M. Porcel, J. de la Torre, M.A. Molina-Henares, A. Daddaoua, M.A. Llamas, A. Roca, V. Carriel, I. Garzon, J.L. Ramos, M. Alaminos and E. Duque, Analysis of the pathogenic potential of nosocomial Pseudomonas putida strains. Front. Microbiol. 6 (2015) 71. [CrossRef] [Google Scholar]
  11. A.V. Fursikov, Optimal Control of Distributed Systems. Theory and Applications. American Math. Soc. (2000). [Google Scholar]
  12. A.B. Goryachev, Understanding bacterial cell-cell communication with computational modeling. Chem. Rev. 111 (2011) 238–250. [CrossRef] [PubMed] [Google Scholar]
  13. A.D. Ioffe and V.M. Tikhomirov, Theory of Extremal Problems. North-Holland, Amsterdam (1979). [Google Scholar]
  14. S. James, P. Nilsson, G. James, S. Kjelleberg and T. Fagersötrm, Luminescence control in the marine bacterium Vibrio fischeri: an analysis of the dynamics of lux regulation. J. Mol. Biol. 296 (2000) 1127–1137. [CrossRef] [Google Scholar]
  15. Q. Jiang, J. Chen, C. Yang, Y. Yin and K. Yao, Quorum Sensing: a prospective therapeutic target for bacterial diseases. BioMed. Res. Int. 2019 (2019) 2015978. [Google Scholar]
  16. A. Koerber, J. King, J. Ward, P. Williams, J. Croft and R. Sockett, A mathematical model of partial-thickness burn-wound infection by Pseudomonas aeruginosa: quorum sensing and the build-up to invasion. Bull. Math. Biol. 64 (2002) 239–259. [CrossRef] [Google Scholar]
  17. A.E. Kovtanyuk, A.Y. Chebotarev and A.A. Astrakhantseva, Inverse extremum problem for a model of endovenous laser ablation. J. Inv. Ill-Posed Probl. 29 (2021) 467–476. [CrossRef] [Google Scholar]
  18. C. Kuttler, Reaction—diffusion equations and their application on bacterial communication. Handbook of Statistics (Chapter 4) (2017) 55–91. [CrossRef] [Google Scholar]
  19. C. Kuttler and A. Maslovskaya, Computer simulation of communication in bacterial populations under external impact of signal-degrading enzymes. CEUR Workshop Proc. 2783 (2020) 163–179. [Google Scholar]
  20. C. Kuttler and A. Maslovskaya, Wave effects in stochastic time lagging reaction-diffusion model of quorum-sensing in bacterial populations. Proc. Int. Conf. Days on Diffraction 2020 (2020) 62–67. [CrossRef] [Google Scholar]
  21. C. Kuttler and A. Maslovskaya, Hybrid stochastic fractional-based approach to modeling bacterial quorum sensing. Appl. Math. Model. 93 (2021) 360–375. [CrossRef] [MathSciNet] [Google Scholar]
  22. C. Kuttler, A. Maslovskaya and L. Moroz, Numerical simulation of time-fractional diffusion-wave processes applied to communication in bacterial populations. Proc. of the IEEE, “Days on Diffraction” (2021) 114–119. [Google Scholar]
  23. J.-L. Lions and E. Magenes, Non-homogeneous boundary value problems and applications. Springer-Verlag (1972). [Google Scholar]
  24. L. Molina, Z. Udaondo, E. Duque, M. Fernandez, C. Molina-Santiago, A. Roca, M. Porcel, J. de la Torre, A. Segura, P. Plesiat, K. Jeannot and J.-L. Ramos, Antibiotic resistance determinants in a Pseudomonas putida strain isolated from a hospital. PLoS ONE 9 (2014) e81604. [CrossRef] [PubMed] [Google Scholar]
  25. J. Perez-Velazquez and M. Gölgeli, R. Garcia-Contreras, Mathematical modelling of bacterial quorum sensing: a review. Bull. Math. Biol. 76 (2016) 1585–1639. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  26. S. Peter, P. Oberhettinger, L. Schuele, A. Dinkelacker, W. Vogel, D. Dorfel, D. Bezdan, S. Ossowski, M. Marschal, J. Liese and M. Willmann, Genomic characterization of clinical and environmental Pseudomonas putida group strains and determination of their role in the transfer of antimicrobial resistance genes to Pseudomonas aeruginosa. BMC Genomics 18 (2017) 859. [CrossRef] [PubMed] [Google Scholar]
  27. S.T. Rutherford and B.L. Bassler, Bacterial quorum sensing: its role in virulence and possibilities for its control. Cold Spring Harb. Perspect. Med. 2 (2012) a012427. [CrossRef] [Google Scholar]
  28. K. Saurav, R. Bar-Shalom, M. Haber, I. Burgsdorf, G. Oliviero, V. Costantino, D. Morgenstern and L. Steindler, In search of alternative antibiotic drugs: Quorum-quenching activity in sponges and their bacterial isolates. Front. Microbiol. 7 (2016) 416. [CrossRef] [Google Scholar]
  29. A. Steidle, M. Allesen-Holm, K. Riedel, G. Berg, M. Givskov, S. Molin and L. Eberl, Identification and characterization of an N-Acylhomoserine lactone-dependent quorum-sensing system in Pseudomonas putida Strain IsoF. Appl. Environ. Microbiol. 68 (2002) 6371–6382. [CrossRef] [PubMed] [Google Scholar]
  30. J.P. Ward, J.R. King, A.J. Koerber, P. Williams, J.M. Croft and R.E. Sockett, Mathematical modelling of quorum sensing in bacteria. Math. Med. Biol. 18 (2001) 263–292. [CrossRef] [Google Scholar]
  31. J. Ward, Mathematical modeling of quorum-sensing control in biofilms. Control of biofilm infections by signal manipulation, edited by N. Balaban. Springer, Berlin (2008) 79–108. [CrossRef] [Google Scholar]
  32. A. Weimer, M. Kohlstedt, D.C. Volke, P.I. Nikel and C. Wittmannet, Industrial biotechnology of Pseudomonas putida: advances and prospects. Appl. Microbiol. Biotechnol. 104 (2020) 7745–7766. [CrossRef] [PubMed] [Google Scholar]
  33. N.A. Whitehead, A.M.L. Barnard, H. Slater, N.J.L. Simpson and G.P.C. Salmond, Quorum-sensing in Gram-negative bacteria. FEMS Microbiol. Rev. 25 (2001) 365–404. [CrossRef] [Google Scholar]
  34. A. Yajima, Recent progress in the chemistry and chemical biology of microbial signaling molecules: quorum-sensing pheromones and microbial hormones. Tetrahedr. Lett. 55 (2014) 2773–2780. [CrossRef] [Google Scholar]

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