Advertisement

Agent-Based Model of Resistant Bacterial Evolution in an Heterogeneous Medium

  • Rubén A. Castañeda-Martínez
  • Dora-Luz FloresEmail author
  • Carlos Castro
  • Balam Benítez
Chapter
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 209)

Abstract

In this chapter, an agent-based model was developed using NetLogo to study bacterial evolution and the process in which bacterial populations are able to acquire antibiotic resistance in an heterogeneous medium with an escalated antibiotic gradient. Molecular interactions between wild-type (WT) E. coli and the antibiotic trimethoprim (TMP) were abstracted and conceptually represented to correctly model the complex biological system and interactions. The antibiotic gradient was spatially separated in a plate, starting from 1 \(\times \) minimal inhibitory concentration (MIC) of WT E. coli and scaling by 10-fold up to 1000 \(\times \) MIC. A set of experiments was performed under the same initial conditions, varying between five different dnaQ alleles, to measure the mean antibiotic resistance, bacterial population, deaths caused by antibiotic and deaths caused by the lack of nutrients to determine if the simulations had biological meaning. One hundred simulations were carried out for every allele. Each run of the model had different, but similar results, meaning that the inherent variability was eliminated and the overall behavior of the model was properly characterized. Tukey tests were performed to measure significant differences between results from the different alleles. A typical bacterial growth curve with well defined phases was obtained. Bacteria were able to acquire antibiotic resistance and migrate from the borders towards the center of the plate, where the concentration of TMP was 1000 \(\times \) MIC. Those results alongside the graphical environment gives enough prove that the model is well implemented to study the bacterial evolution and the process in which they are able to develop antibiotic resistance.

References

  1. 1.
    Abar, S., Theodoropoulos, G.K., Lemarinier, P., O’Hare, G.M.: Agent based modelling and simulation tools: a review of the state-of-art software. Comput. Sci. Rev., 1–21 (2017)Google Scholar
  2. 2.
    Amigoni, F., Schiaffonati, V.: Multiagent-based simulation in biology: a critical analysis. In: Model-Based Reasoning in Science, Technology, and Medicine, pp. 179–191 (2007)CrossRefGoogle Scholar
  3. 3.
    Banin, E., Hughes, D., Kuipers, O.P.: Editorial: bacterial pathogen, antibiotics and antibiotic resistance. FEMS Microbiol. Rev. 41(3), 450–452 (2017)CrossRefGoogle Scholar
  4. 4.
    Bauer, A.L., Beauchemin, C.A.A., Perelson, A.S.: Agent-based modeling of host-pathogen systems: the successes and challenges. Inf. Sci. 179(10), 1379–1389 (2009)CrossRefGoogle Scholar
  5. 5.
    Baym, M., Lieberman, T.D., Kelsic, E.D., Chait, R., Gross, R., Yelin, I., Kishony, R.: Spatiotemporal microbial evolution on antibiotic landscapes. Antibiot. Resist. 353(6304), 1147–1152 (2016)Google Scholar
  6. 6.
    Bayrak, E.S., Wang, T., Jerums, M., Coufal, M., Goudar, C., Cinar, A., Undey, C.: In silico cell cycle predictor for mammalian cell culture bioreactor using agent-based modeling approach. IFAC-PapersOnLine 49(7), 200–205 (2016)CrossRefGoogle Scholar
  7. 7.
    Carley, K.M., Fridsma, D.B., Casma, E., Yahja, A., Altman, N., Chen, L.-C., Kaminsky, B., Nave, D.: Bio war: scalable agent-based model of bioattacks. Syst. Hum. 36(2), 252–264 (2006)CrossRefGoogle Scholar
  8. 8.
    Castro, C., Luz, D., David, F., Vásquez, C., Vargas, E., Gutiérrez, E., Franklin, L., Muñoz, M.: An agent-based model of the fission yeast cell cycle. Curr. Genet. (2018)Google Scholar
  9. 9.
    D’Acunto, B., Frunzo, L.: Qualitative analysis and simulations of a free boundary problem for multispecies biofilm models. Math. Comput. Model. 53(9–10), 1596–1606 (2011)MathSciNetCrossRefGoogle Scholar
  10. 10.
    D’Acunto, B., Frunzo, L., Klapper, I., Mattei, M.R.: Modeling multispecies biofilms including new bacterial species invasion. Math. Biosci. 259, 20–26 (2015)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Duployez, C., Robert, J., Vachée, A.: Trimethoprim susceptibility in E. coli community-acquired urinary tract infections in France. Medecine et Maladies Infectieuses 48(6), 410–413 (2018)CrossRefGoogle Scholar
  12. 12.
    Gautam, S., Kalidindi, R., Humayun, M.Z.: SOS induction and mutagenesis by dnaQ missense alleles in wild type cells. Mutat. Res.—Fundam. Mol. Mech. Mutagen. 735(1–2), 46–50 (2012)CrossRefGoogle Scholar
  13. 13.
    Gilbert, N., Troitzsch, K.G.: Simulation for the Social Scientist (1999)Google Scholar
  14. 14.
    Gross, L.J.: Computer systems and models, use of. Encycl. Biodivers. 1, 845–853 (2013)CrossRefGoogle Scholar
  15. 15.
    Hegreness, M., Shoresh, N., Damian, D., Hartl, D., Kishony, R.: Accelerated evolution of resistance in multidrug environments. PNAS 105(37), 13977–13981 (2008)CrossRefGoogle Scholar
  16. 16.
    Hermsen, R., Deris, J.B., Hwa, T.: On the rapidity of antibiotic resistance evolution facilitated by a concentration gradient. PNAS 109(27), 10775–10780 (2012)CrossRefGoogle Scholar
  17. 17.
    Janion, C.: Some aspects of the SOS response system—a critical survey. 48(3), 599–607 (2001)Google Scholar
  18. 18.
    Khataee, H.R., Aris, T.N.M., Sulaiman, M.N.: An agent-based model of muscle contraction process as a bio-robotic process. In: 2011 5th Malaysian Conference in Software Engineering. MySEC 2011, pp. 55–60 (2011)Google Scholar
  19. 19.
    Laxminarayan, R., Duse, A., Wattal, C., Zaidi, A.K., Wertheim, H.F., Sumpradit, N., Vlieghe, E., Hara, G.L., Gould, I.M., Goossens, H., Greko, C., So, A.D., Bigdeli, M., Tomson, G., Woodhouse, W., Ombaka, E., Peralta, A.Q., Qamar, F.N., Mir, F., Kariuki, S., Bhutta, Z.A., Coates, A., Bergstrom, R., Wright, G.D., Brown, E.D., Cars, O.: Antibiotic resistance-the need for global solutions. Lancet Infect. Dis. 13(12), 1057–1098 (2013)CrossRefGoogle Scholar
  20. 20.
    Lee, H., Popodi, E., Tang, H., Foster, P.L.: Rate and molecular spectrum of spontaneous mutations in the bacterium Escherichia coli as determined by whole-genome sequencing. Adsorpt. J. Int. Adsorpt. Soc. 109(41), 2–4 (2012)Google Scholar
  21. 21.
    Mattei, M.R., Frunzo, L., D’Acunto, B., Esposito, G., Pirozzi, F.: Modelling microbial population dynamics in multispecies biofilms including Anammox bacteria. Ecol. Model. 304, 44–58 (2015)CrossRefGoogle Scholar
  22. 22.
    Murli, S., Walker, G.C.: SOS mutagenesis. Curr. Opin. Genet. Dev. 3(5), 719–725 (1993)CrossRefGoogle Scholar
  23. 23.
    Northrup, S.H., Erickson, H.P.: Kinetics of protein-protein association explained by Brownian dynamics computer simulation. Proc. Natl. Acad. Sci. 89, 3338–3342 (1992)CrossRefGoogle Scholar
  24. 24.
    Piddock, L.J.V.: Understanding drug resistance will improve the treatment of bacterial infections. Antimicrob. Resist. 15, 639–640 (2017)Google Scholar
  25. 25.
    Scholar, E.: Trimethoprim. (1) (2007)Google Scholar
  26. 26.
    Tung, C.-k., Pourmand, N., Austin, R.H.: Connected microenvironments. Science, 1764–1767 (2011)Google Scholar
  27. 27.
    Wilensky, U.: NetLogo. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL (1999)Google Scholar
  28. 28.
    Wilensky, U., Rand, W.: An Introduction to Agent-Based Modeling. MIT Press (2015)Google Scholar
  29. 29.
    Yu, J.S., Bagheri, N.: Multi-class and multi-scale models of complex biological phenomena. Curr. Opin. Biotechnol. 39, 167–173 (2016)CrossRefGoogle Scholar
  30. 30.
    Zhang, L., Wang, Z., Sagotsky, J.A., Deisboeck, T.S.: Multiscale agent-based cancer modeling. J. Math. Biol. 58(4–5), 545–559 (2009)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Rubén A. Castañeda-Martínez
    • 1
  • Dora-Luz Flores
    • 2
    Email author
  • Carlos Castro
    • 2
  • Balam Benítez
    • 2
  1. 1.EnsenadaMexico
  2. 2.Autonomous University of Baja CaliforniaEnsenadaMexico

Personalised recommendations