Acta Scientific Microbiology (ISSN: 2581-3226)

Short Communication Volume 6 Issue 7

The Fitness Landscape Algorithm: A Powerful Tool for Studying Antibiotic Resistance

Ahmed M Saleem*

Gifted School of Basra, Basra, Iraq

*Corresponding Author: Ahmed M Saleem, Gifted School of Basra, Basra, Iraq.

Received: May 29, 2023; Published: June 07, 2023

Introduction

One of the most important problems in contemporary medicine is antibiotic resistance. The efficiency of our present therapies is deteriorating as bacteria acquire resistance to available antibiotics, increasing the frequency of diseases that are challenging or impossible to cure. Researchers have created a range of algorithms and methodologies for investigating antibiotic resistance in bacteria in order to address this issue. I'll discuss a novel methodology for examining bacterial resistance to antibiotics in this post. I'll talk about an algorithm known as the "Fitness Landscape Algorithm." A group of scientists at the University of California, San Diego, under the direction of Dr. Jeffrey Hasty, created this algorithm [1].

References

  1. University of California San Diego. “Researcher pioneers new way to study antibiotic resistance” (2020).
  2. Wright S. “The roles of mutation, inbreeding, crossbreeding, and selection in evolution”. Proceedings of the Sixth International Congress on Genetics 1 (1932): 356-366.
  3. Toprak E., et al. “Evolutionary paths to antibiotic resistance under dynamically sustained drug selection”. Nature Genetics1 (2012): 101-105.
  4. Wong, A., et al. “An evolutionary perspective on antibiotic resistance”. Future Microbiology8 (2017): 751-754.

Citation

Citation: Ahmed M Saleem. “The Fitness Landscape Algorithm: A Powerful Tool for Studying Antibiotic Resistance". Acta Scientific Microbiology 6.7 (2023): 06-07.

Copyright

Copyright: © 2023 Ahmed M Saleem. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




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