Understanding Antimicrobial Resistance Using Genome-Scale Metabolic Modeling
The urgent necessity to fight antimicrobial resistance is universally recognized. In the search of new targets and strategies to face this global challenge, a promising approach resides in the study of the cellular response to antimicrobial exposure and on the impact of global cellular reprogramming...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-05-01
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| Series: | Antibiotics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-6382/12/5/896 |
| _version_ | 1797601312918994944 |
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| author | Tania Alonso-Vásquez Marco Fondi Elena Perrin |
| author_facet | Tania Alonso-Vásquez Marco Fondi Elena Perrin |
| author_sort | Tania Alonso-Vásquez |
| collection | DOAJ |
| description | The urgent necessity to fight antimicrobial resistance is universally recognized. In the search of new targets and strategies to face this global challenge, a promising approach resides in the study of the cellular response to antimicrobial exposure and on the impact of global cellular reprogramming on antimicrobial drugs’ efficacy. The metabolic state of microbial cells has been shown to undergo several antimicrobial-induced modifications and, at the same time, to be a good predictor of the outcome of an antimicrobial treatment. Metabolism is a promising reservoir of potential drug targets/adjuvants that has not been fully exploited to date. One of the main problems in unraveling the metabolic response of cells to the environment resides in the complexity of such metabolic networks. To solve this problem, modeling approaches have been developed, and they are progressively gaining in popularity due to the huge availability of genomic information and the ease at which a genome sequence can be converted into models to run basic phenotype predictions. Here, we review the use of computational modeling to study the relationship between microbial metabolism and antimicrobials and the recent advances in the application of genome-scale metabolic modeling to the study of microbial responses to antimicrobial exposure. |
| first_indexed | 2024-03-11T04:00:06Z |
| format | Article |
| id | doaj.art-2ca8bfb075cf4245be0d5857d9e8ec61 |
| institution | Directory Open Access Journal |
| issn | 2079-6382 |
| language | English |
| last_indexed | 2024-03-11T04:00:06Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Antibiotics |
| spelling | doaj.art-2ca8bfb075cf4245be0d5857d9e8ec612023-11-18T00:12:53ZengMDPI AGAntibiotics2079-63822023-05-0112589610.3390/antibiotics12050896Understanding Antimicrobial Resistance Using Genome-Scale Metabolic ModelingTania Alonso-Vásquez0Marco Fondi1Elena Perrin2Department of Biology, University of Florence, Via Madonna del Piano 6, Sesto F.no, 50019 Florence, ItalyDepartment of Biology, University of Florence, Via Madonna del Piano 6, Sesto F.no, 50019 Florence, ItalyDepartment of Biology, University of Florence, Via Madonna del Piano 6, Sesto F.no, 50019 Florence, ItalyThe urgent necessity to fight antimicrobial resistance is universally recognized. In the search of new targets and strategies to face this global challenge, a promising approach resides in the study of the cellular response to antimicrobial exposure and on the impact of global cellular reprogramming on antimicrobial drugs’ efficacy. The metabolic state of microbial cells has been shown to undergo several antimicrobial-induced modifications and, at the same time, to be a good predictor of the outcome of an antimicrobial treatment. Metabolism is a promising reservoir of potential drug targets/adjuvants that has not been fully exploited to date. One of the main problems in unraveling the metabolic response of cells to the environment resides in the complexity of such metabolic networks. To solve this problem, modeling approaches have been developed, and they are progressively gaining in popularity due to the huge availability of genomic information and the ease at which a genome sequence can be converted into models to run basic phenotype predictions. Here, we review the use of computational modeling to study the relationship between microbial metabolism and antimicrobials and the recent advances in the application of genome-scale metabolic modeling to the study of microbial responses to antimicrobial exposure.https://www.mdpi.com/2079-6382/12/5/896metabolic modelingantimicrobial resistancebacterial metabolism |
| spellingShingle | Tania Alonso-Vásquez Marco Fondi Elena Perrin Understanding Antimicrobial Resistance Using Genome-Scale Metabolic Modeling Antibiotics metabolic modeling antimicrobial resistance bacterial metabolism |
| title | Understanding Antimicrobial Resistance Using Genome-Scale Metabolic Modeling |
| title_full | Understanding Antimicrobial Resistance Using Genome-Scale Metabolic Modeling |
| title_fullStr | Understanding Antimicrobial Resistance Using Genome-Scale Metabolic Modeling |
| title_full_unstemmed | Understanding Antimicrobial Resistance Using Genome-Scale Metabolic Modeling |
| title_short | Understanding Antimicrobial Resistance Using Genome-Scale Metabolic Modeling |
| title_sort | understanding antimicrobial resistance using genome scale metabolic modeling |
| topic | metabolic modeling antimicrobial resistance bacterial metabolism |
| url | https://www.mdpi.com/2079-6382/12/5/896 |
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