--- _id: '8997' abstract: - lang: eng text: 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. acknowledgement: 'This work was supported in part by Tum stipend of Knafelj foundation (to B.K.), Austrian Science Fund (FWF) standalone grants P 27201-B22 (to T.B.) and P 28844(to G.T.), HFSP program Grant RGP0042/2013 (to T.B.), German Research Foundation (DFG) individual grant BO 3502/2-1 (to T.B.), and German Research Foundation (DFG) Collaborative Research Centre (SFB) 1310 (to T.B.). ' article_number: e1008529 article_processing_charge: Yes article_type: original author: - first_name: Bor full_name: Kavcic, Bor id: 350F91D2-F248-11E8-B48F-1D18A9856A87 last_name: Kavcic orcid: 0000-0001-6041-254X - first_name: Gašper full_name: Tkačik, Gašper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkačik orcid: 0000-0002-6699-1455 - first_name: Tobias full_name: Bollenbach, Tobias id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87 last_name: Bollenbach orcid: 0000-0003-4398-476X citation: ama: Kavcic B, Tkačik G, Bollenbach MT. Minimal biophysical model of combined antibiotic action. PLOS Computational Biology. 2021;17. doi:10.1371/journal.pcbi.1008529 apa: Kavcic, B., Tkačik, G., & Bollenbach, M. T. (2021). Minimal biophysical model of combined antibiotic action. PLOS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1008529 chicago: Kavcic, Bor, Gašper Tkačik, and Mark Tobias Bollenbach. “Minimal Biophysical Model of Combined Antibiotic Action.” PLOS Computational Biology. Public Library of Science, 2021. https://doi.org/10.1371/journal.pcbi.1008529. ieee: B. Kavcic, G. Tkačik, and M. T. Bollenbach, “Minimal biophysical model of combined antibiotic action,” PLOS Computational Biology, vol. 17. Public Library of Science, 2021. ista: Kavcic B, Tkačik G, Bollenbach MT. 2021. Minimal biophysical model of combined antibiotic action. PLOS Computational Biology. 17, e1008529. mla: Kavcic, Bor, et al. “Minimal Biophysical Model of Combined Antibiotic Action.” PLOS Computational Biology, vol. 17, e1008529, Public Library of Science, 2021, doi:10.1371/journal.pcbi.1008529. short: B. Kavcic, G. Tkačik, M.T. Bollenbach, PLOS Computational Biology 17 (2021). date_created: 2021-01-08T07:16:18Z date_published: 2021-01-07T00:00:00Z date_updated: 2024-02-21T12:41:41Z day: '07' ddc: - '570' department: - _id: GaTk doi: 10.1371/journal.pcbi.1008529 external_id: isi: - '000608045000010' file: - access_level: open_access checksum: e29f2b42651bef8e034781de8781ffac content_type: application/pdf creator: dernst date_created: 2021-02-04T12:30:48Z date_updated: 2021-02-04T12:30:48Z file_id: '9092' file_name: 2021_PlosComBio_Kavcic.pdf file_size: 3690053 relation: main_file success: 1 file_date_updated: 2021-02-04T12:30:48Z has_accepted_license: '1' intvolume: ' 17' isi: 1 keyword: - Modelling and Simulation - Genetics - Molecular Biology - Antibiotics - Drug interactions language: - iso: eng month: '01' oa: 1 oa_version: Published Version project: - _id: 25E9AF9E-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P27201-B22 name: Revealing the mechanisms underlying drug interactions - _id: 254E9036-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P28844-B27 name: Biophysics of information processing in gene regulation publication: PLOS Computational Biology publication_identifier: issn: - 1553-7358 publication_status: published publisher: Public Library of Science quality_controlled: '1' related_material: record: - id: '7673' relation: earlier_version status: public - id: '8930' relation: research_data status: public status: public title: Minimal biophysical model of combined antibiotic action tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 17 year: '2021' ...