Literature DB >> 33510864

The ecology of plasmid-coded antibiotic resistance: a basic framework for experimental research and modeling.

Martin Zwanzig1.   

Abstract

Many antibiotic resistance genes are associated with plasmids. The ecological success of these mobile genetic elements within microbial communities depends on varying mechanisms to secure their own propagation, not only on environmental selection. Among the most important are the cost of plasmids and their ability to be transferred to new hosts through mechanisms such as conjugation. These are regulated by dynamic control systems of the conjugation machinery and genetic adaptations that plasmid-host pairs can acquire in coevolution. However, in complex communities, these processes and mechanisms are subject to a variety of interactions with other bacterial species and other plasmid types. This article summarizes basic plasmid properties and ecological principles particularly important for understanding the persistence of plasmid-coded antibiotic resistance in aquatic environments. Through selected examples, it further introduces to the features of different types of simulation models such as systems of ordinary differential equations and individual-based models, which are considered to be important tools to understand these complex systems. This ecological perspective aims to improve the way we study and understand the dynamics, diversity and persistence of plasmids and associated antibiotic resistance genes.
© 2020 The Author(s).

Entities:  

Keywords:  Agent-based modeling; Antibiotic resistance; Biotic interactions; Evolution; Fitness; Horizontal gene transfer; Individual heterogeneity; Plasmid ecology; Simulation model

Year:  2020        PMID: 33510864      PMCID: PMC7807137          DOI: 10.1016/j.csbj.2020.12.027

Source DB:  PubMed          Journal:  Comput Struct Biotechnol J        ISSN: 2001-0370            Impact factor:   7.271


  116 in total

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8.  Simulating the Influence of Conjugative-Plasmid Kinetic Values on the Multilevel Dynamics of Antimicrobial Resistance in a Membrane Computing Model.

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