Department of Computer Science and Systems Engineering, University of Zaragoza, Zaragoza 50018, Spain.
Lucy Cavendish College, Biological Sciences, University of Cambridge, Cambridge CB3 0BU, UK.
Bioinformatics. 2023 Feb 3;39(2). doi: 10.1093/bioinformatics/btad053.
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.
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.
CONTRABASS is available as an open source Python package at https://github.com/openCONTRABASS/CONTRABASS under GPL-3.0 License. An online web server is available at http://contrabass.unizar.es.
A glossary of terms are available at Bioinformatics online.
尽管抗菌药物耐药性是一个日益严重的健康问题，但由于该过程成本高且成功与否不确定，新药的研发速度仍然缓慢。高通量技术的发展使得可以将生物数据整合到多个生物体的详细基因组规模模型中。可以通过计算方法利用这些模型来识别系统脆弱性，例如瓶颈反应和必需反应。这些脆弱性是有吸引力的药物靶点，可以导致新的药物开发。然而，目前计算这些脆弱性的方法仅基于拓扑数据，忽略了模型的动态信息。这可能导致药物靶点的错误识别。
这项工作计算了与模型生物一定生长速率一致的通量约束，并将计算出的通量约束整合到模型中，以提高脆弱性的检测能力。通过利用这些通量约束，我们能够获得与模型给定生长速率一致的代谢反应的方向性，从而可以更真实地检测脆弱性。定义了几组反应作为系统脆弱性，并研究了它们之间的关系。已经在 Python 工具 CONTRABASS 中实现了检测这些脆弱性的方法。该工具还实现了一个在线网络服务器，用于计算通量约束并生成带有检测到的脆弱性的报告。
CONTRABASS 可作为开源 Python 包在 https://github.com/openCONTRABASS/CONTRABASS 下以 GPL-3.0 许可证获得。一个在线网络服务器可在 http://contrabass.unizar.es 获得。
术语表可在 Bioinformatics 在线获得。
