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Simulating Multilevel Dynamics of Antimicrobial Resistance in a Membrane Computing Model.

Marcelino Campos1,2,3, Rafael Capilla4, Fernando Naya4

  • 1Department of Microbiology, Ramón y Cajal University Hospital, IRYCIS, Madrid, Spain.

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|January 31, 2019
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Summary

This study introduces a novel membrane computing model to simulate antibiotic resistance evolution. The model analyzes complex biological interactions, predicting how factors like patient flow and antibiotic use impact resistance dynamics.

Keywords:
antibiotic resistancecomputer modelingmathematical modelingmembrane computingmultilevel

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Area of Science:

  • Computational Biology
  • Bio-inspired Computing
  • Evolutionary Biology

Background:

  • Antibiotic resistance is a complex, multi-hierarchical problem involving genes, cells, populations, and environments.
  • Existing simulation methods struggle to capture the intricate dynamics of antibiotic resistance.
  • Membrane computing offers a promising framework due to its structural resemblance to biological systems.

Purpose of the Study:

  • To develop and apply a novel membrane computing model (P systems) for simulating antibiotic resistance.
  • To explore the hierarchical interactive dynamics of antibiotic resistance at multiple biological organization levels.
  • To analyze the influence of various factors on the evolution of antibiotic resistance phenotypes.

Main Methods:

  • Utilized membrane computing (P systems) to model biological entities and their interactions.
  • Simulated nested membrane-surrounded entities with capabilities for division, propagation, transfer, mutation, and selection.
  • Examined various scenarios including patient flow rates, cross-transmission, antibiotic treatment proportions, and drug selective strengths.

Main Results:

  • The model successfully reproduces complex biological landscapes relevant to antibiotic resistance.
  • Predicted the effects of patient flow, transmission rates, and antibiotic usage on resistance evolution.
  • Evaluated the selective strengths of drugs and the impact of initial resistance composition.

Conclusions:

  • The developed P system model provides a powerful tool for analyzing multilevel evolutionary biology of antibiotic resistance.
  • This approach facilitates case studies on hierarchical dynamics and offers insights into predicting resistance.
  • The methodology can be expanded for broader multilevel analysis of complex microbial landscapes.