CONTRABASS: exploiting flux constraints in genome-scale models for the detection of vulnerabilities

MotivationDespite 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.ResultsThis 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.Availability and implementationCONTRABASS is available as an open source Python package at under GPL-3.0 License. An online web server is available at .Supplementary informationA glossary of terms are available at Bioinformatics online.

Automated summary

Despite the slow pace of new drug production due to high cost and uncertain success, high-throughput technologies and computational methods can be used to identify vulnerabilities in biological models and facilitate novel drug development. However, the current approach only considers topological data, neglecting dynamic information and potentially leading to misidentified drug targets.