000125794 001__ 125794
000125794 005__ 20241125101136.0
000125794 0247_ $$2doi$$a10.1093/bioinformatics/btad053
000125794 0248_ $$2sideral$$a133251
000125794 037__ $$aART-2023-133251
000125794 041__ $$aeng
000125794 100__ $$aOarga, Alexandru$$uUniversidad de Zaragoza
000125794 245__ $$aCONTRABASS: exploiting flux constraints in genome-scale models for the detection of vulnerabilities
000125794 260__ $$c2023
000125794 5060_ $$aAccess copy available to the general public$$fUnrestricted
000125794 5203_ $$aMotivation: Despite the fact that antimicrobial resistance is an increasing health concern, the pace of production of new drugs is slow due to the high cost and uncertain success of the process. The development of high-throughput technologies has allowed the integration of biological data into detailed genome-scale models of multiple organisms. Such models can be exploited by means of computational methods to identify system vulnerabilities such as chokepoint reactions and essential reactions. These vulnerabilities are appealing drug targets that can lead to novel drug developments. However, the current approach to compute these vulnerabilities is only based on topological data and ignores the dynamic information of the model. This can lead to misidentified drug targets.
Results: This work computes flux constraints that are consistent with a certain growth rate of the modelled organism, and integrates the computed flux constraints into the model to improve the detection of vulnerabilities. By exploiting these flux constraints, we are able to obtain a directionality of the reactions of metabolism consistent with a given growth rate of the model, and consequently, a more realistic detection of vulnerabilities can be performed. Several sets of reactions that are system vulnerabilities are defined and the relationships among them are studied. The approach for the detection of these vulnerabilities has been implemented in the Python tool CONTRABASS. Such tool, for which an online web server has also been implemented, computes flux constraints and generates a report with the detected vulnerabilities.
000125794 536__ $$9info:eu-repo/grantAgreement/EUR/AEI/TED2021-130449B-I00$$9info:eu-repo/grantAgreement/ES/MICINN/PID2020-113969RB-I00
000125794 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000125794 590__ $$a4.4$$b2023
000125794 592__ $$a2.574$$b2023
000125794 591__ $$aBIOCHEMICAL RESEARCH METHODS$$b11 / 85 = 0.129$$c2023$$dQ1$$eT1
000125794 593__ $$aBiochemistry$$c2023$$dQ1
000125794 591__ $$aMATHEMATICAL & COMPUTATIONAL BIOLOGY$$b7 / 66 = 0.106$$c2023$$dQ1$$eT1
000125794 593__ $$aComputational Mathematics$$c2023$$dQ1
000125794 591__ $$aBIOTECHNOLOGY & APPLIED MICROBIOLOGY$$b38 / 174 = 0.218$$c2023$$dQ1$$eT1
000125794 593__ $$aStatistics and Probability$$c2023$$dQ1
000125794 593__ $$aComputer Science Applications$$c2023$$dQ1
000125794 593__ $$aMolecular Biology$$c2023$$dQ1
000125794 593__ $$aComputational Theory and Mathematics$$c2023$$dQ1
000125794 594__ $$a11.2$$b2023
000125794 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000125794 700__ $$aBannerman, Bridget P
000125794 700__ $$0(orcid)0000-0002-7093-228X$$aJúlvez, Jorge$$uUniversidad de Zaragoza
000125794 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát.
000125794 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000125794 773__ $$g39, 2 (2023), [7 pp.]$$pBioinformatics$$tBioinformatics$$x1367-4803
000125794 8564_ $$s501978$$uhttps://zaguan.unizar.es/record/125794/files/texto_completo.pdf$$yVersión publicada
000125794 8564_ $$s2750094$$uhttps://zaguan.unizar.es/record/125794/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000125794 909CO $$ooai:zaguan.unizar.es:125794$$particulos$$pdriver
000125794 951__ $$a2024-11-22-12:01:04
000125794 980__ $$aARTICLE