An Agent-Based Lattice Model for the Emergence of Anti-Microbial Resistance

Research output: Contribution to journalArticle

Abstract

This work introduces a new probabilistic agent-based lattice model for studying the emergence of anti-microbial resistance (AMR) and proposes a new proxy to measure it: the average death probability of the population under the action of the AMD. Both analytical studies and computer simulations of the microscopic behaviour of a bacterial culture interacting with anti-microbial drugs on a discrete lattice are carried out by focusing on the dynamics of this quantity. A unique genotype-phenotype map and classes of AMDs follow as emergent properties and their effects on the possible reversal of resistance are analysed. We also discuss briefly the possibility of using machine learning techniques to learn the model parameters.
Original languageEnglish
Article number110080
JournalJournal of Theoretical Biology
Early online date12 Nov 2019
DOIs
Publication statusE-pub ahead of print - 12 Nov 2019

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Agent-based Model
Proxy
Lattice Model
antibiotic resistance
Computer Simulation
Genotype
Phenotype
artificial intelligence
Reversal
computer simulation
Pharmaceutical Preparations
Population
Learning systems
Machine Learning
Drugs
death
phenotype
drugs
genotype
Computer simulation

Bibliographical note

© 2019, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

Keywords

  • Probabilistic model
  • Perceptron
  • Resistance reversal
  • Single-drug protocol

Cite this

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abstract = "This work introduces a new probabilistic agent-based lattice model for studying the emergence of anti-microbial resistance (AMR) and proposes a new proxy to measure it: the average death probability of the population under the action of the AMD. Both analytical studies and computer simulations of the microscopic behaviour of a bacterial culture interacting with anti-microbial drugs on a discrete lattice are carried out by focusing on the dynamics of this quantity. A unique genotype-phenotype map and classes of AMDs follow as emergent properties and their effects on the possible reversal of resistance are analysed. We also discuss briefly the possibility of using machine learning techniques to learn the model parameters.",
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