Mobile Elements and Antibiotic Resistance
A special issue of Antibiotics (ISSN 2079-6382). This special issue belongs to the section "Mechanism and Evolution of Antibiotic Resistance".
Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 3121
Special Issue Editor
Special Issue Information
Dear Colleagues,
The inappropriate use of antibiotics has driven the dissemination of antibiotic resistance (AR) in most bacteria. The emergence of AR is mainly driven by two processes: (i) chromosomal mutations altering the cellular targets of antibiotics or decreasing their intracellular concentrations, and (ii) horizontal transfer of resistance genes encoded on mobile elements, such as plasmids, integrative and conjugative elements, and bacteriophages. As a result, the main goal of this Special Issue is to seek manuscript submissions that further our understanding on the crossroads of mobile elements and AR, by exploring some of the following topics:
- Evolutionary dynamics between mobile genetic elements and bacteria
- Genome mining and study of AR genes encoded within mobile elements
- Network-based analyses of the bacterial ‘mobilome’
- Transmission of AR and development of mathematical models
- Machine learning algorithms used to predict AR
Dr. João Botelho
Guest Editor
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
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Keywords
- Antibiotic resistance
- Mobile elements
- Evolution
- Mathematical models
- Machine learning
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