Minimal biophysical model of combined antibiotic action.
Phenomenological relations such as Ohm's or Fourier's law have a venerable history in physics but are still scarce in biology. This situation restrains predictive theory. Here, we build on bacterial "growth laws," which capture physiological feedback between translation and cell...
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Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1008529&type=printable |
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| author | Bor Kavčič Gašper Tkačik Tobias Bollenbach |
| author_facet | Bor Kavčič Gašper Tkačik Tobias Bollenbach |
| author_sort | Bor Kavčič |
| collection | DOAJ |
| description | Phenomenological relations such as Ohm's or Fourier's law have a venerable history in physics but are still scarce in biology. This situation restrains predictive theory. Here, we build on bacterial "growth laws," which capture physiological feedback between translation and cell growth, to construct a minimal biophysical model for the combined action of ribosome-targeting antibiotics. Our model predicts drug interactions like antagonism or synergy solely from responses to individual drugs. We provide analytical results for limiting cases, which agree well with numerical results. We systematically refine the model by including direct physical interactions of different antibiotics on the ribosome. In a limiting case, our model provides a mechanistic underpinning for recent predictions of higher-order interactions that were derived using entropy maximization. We further refine the model to include the effects of antibiotics that mimic starvation and the presence of resistance genes. We describe the impact of a starvation-mimicking antibiotic on drug interactions analytically and verify it experimentally. Our extended model suggests a change in the type of drug interaction that depends on the strength of resistance, which challenges established rescaling paradigms. We experimentally show that the presence of unregulated resistance genes can lead to altered drug interaction, which agrees with the prediction of the model. While minimal, the model is readily adaptable and opens the door to predicting interactions of second and higher-order in a broad range of biological systems. |
| format | Article |
| id | doaj-art-e5488bda95d646de8e635063ab780e50 |
| institution | OA Journals |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-e5488bda95d646de8e635063ab780e502025-08-20T02:22:24ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582021-01-01171e100852910.1371/journal.pcbi.1008529Minimal biophysical model of combined antibiotic action.Bor KavčičGašper TkačikTobias BollenbachPhenomenological relations such as Ohm's or Fourier's law have a venerable history in physics but are still scarce in biology. This situation restrains predictive theory. Here, we build on bacterial "growth laws," which capture physiological feedback between translation and cell growth, to construct a minimal biophysical model for the combined action of ribosome-targeting antibiotics. Our model predicts drug interactions like antagonism or synergy solely from responses to individual drugs. We provide analytical results for limiting cases, which agree well with numerical results. We systematically refine the model by including direct physical interactions of different antibiotics on the ribosome. In a limiting case, our model provides a mechanistic underpinning for recent predictions of higher-order interactions that were derived using entropy maximization. We further refine the model to include the effects of antibiotics that mimic starvation and the presence of resistance genes. We describe the impact of a starvation-mimicking antibiotic on drug interactions analytically and verify it experimentally. Our extended model suggests a change in the type of drug interaction that depends on the strength of resistance, which challenges established rescaling paradigms. We experimentally show that the presence of unregulated resistance genes can lead to altered drug interaction, which agrees with the prediction of the model. While minimal, the model is readily adaptable and opens the door to predicting interactions of second and higher-order in a broad range of biological systems.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1008529&type=printable |
| spellingShingle | Bor Kavčič Gašper Tkačik Tobias Bollenbach Minimal biophysical model of combined antibiotic action. PLoS Computational Biology |
| title | Minimal biophysical model of combined antibiotic action. |
| title_full | Minimal biophysical model of combined antibiotic action. |
| title_fullStr | Minimal biophysical model of combined antibiotic action. |
| title_full_unstemmed | Minimal biophysical model of combined antibiotic action. |
| title_short | Minimal biophysical model of combined antibiotic action. |
| title_sort | minimal biophysical model of combined antibiotic action |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1008529&type=printable |
| work_keys_str_mv | AT borkavcic minimalbiophysicalmodelofcombinedantibioticaction AT gaspertkacik minimalbiophysicalmodelofcombinedantibioticaction AT tobiasbollenbach minimalbiophysicalmodelofcombinedantibioticaction |