125794 20241125101136.0 doi 10.1093/bioinformatics/btad053 sideral 133251 ART-2023-133251 eng Oarga, Alexandru Universidad de Zaragoza CONTRABASS: exploiting flux constraints in genome-scale models for the detection of vulnerabilities 2023 Access copy available to the general public Unrestricted Motivation: Despite the fact that antimicrobial resistance is an increasing health concern, the pace of production of new drugs is slow due to the high cost and uncertain success of the process. The development of high-throughput technologies has allowed the integration of biological data into detailed genome-scale models of multiple organisms. Such models can be exploited by means of computational methods to identify system vulnerabilities such as chokepoint reactions and essential reactions. These vulnerabilities are appealing drug targets that can lead to novel drug developments. However, the current approach to compute these vulnerabilities is only based on topological data and ignores the dynamic information of the model. This can lead to misidentified drug targets. Results: This work computes flux constraints that are consistent with a certain growth rate of the modelled organism, and integrates the computed flux constraints into the model to improve the detection of vulnerabilities. By exploiting these flux constraints, we are able to obtain a directionality of the reactions of metabolism consistent with a given growth rate of the model, and consequently, a more realistic detection of vulnerabilities can be performed. Several sets of reactions that are system vulnerabilities are defined and the relationships among them are studied. The approach for the detection of these vulnerabilities has been implemented in the Python tool CONTRABASS. Such tool, for which an online web server has also been implemented, computes flux constraints and generates a report with the detected vulnerabilities. info:eu-repo/grantAgreement/EUR/AEI/TED2021-130449B-I00 info:eu-repo/grantAgreement/ES/MICINN/PID2020-113969RB-I00 info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 4.4 2023 BIOCHEMICAL RESEARCH METHODS 11 / 85 = 0.129 2023 Q1 T1 MATHEMATICAL & COMPUTATIONAL BIOLOGY 7 / 66 = 0.106 2023 Q1 T1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY 38 / 174 = 0.218 2023 Q1 T1 2.574 2023 Biochemistry 2023 Q1 Computational Mathematics 2023 Q1 Statistics and Probability 2023 Q1 Computer Science Applications 2023 Q1 Molecular Biology 2023 Q1 Computational Theory and Mathematics 2023 Q1 11.2 2023 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Bannerman, Bridget P Júlvez, Jorge Universidad de Zaragoza (orcid)0000-0002-7093-228X 5007 520 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Ingen.Sistemas y Automát. 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. 39, 2 (2023), [7 pp.] Bioinformatics Bioinformatics 1367-4803 501978 http://zaguan.unizar.es/record/125794/files/texto_completo.pdf Versión publicada 2750094 http://zaguan.unizar.es/record/125794/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:125794 articulos driver 2024-11-22-12:01:04 ARTICLE