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This chapter delves into MetQuest, a computational tool that enumerates all possible reaction pathways that are feasible in genome-scale metabolic networks given a set of available metabolites. MetQuest accomplishes this by leveraging a guided breadth-first search framework, followed by dynamic programming. It enables us to perform interesting microbial community analyses, such as identifying metabolic exchanges between community members. Here, we specifically demonstrate how MetQuest can be employed to determine the Metabolic Support Index (MSI) for a pairwise microbial community. MSI is a metric that reflects an organism’s benefit from the other member in co-culture, shedding light on the dynamics governing community interactions. Here, we present a pipeline to compute the MSI for two-membered communities from their genome-scale metabolic models. We illustrate this approach using the case of Acinetobacter baumannii and Klebsiella pneumoniae, two bacteria known to cross-feed metabolites. The MetQuest Python protocol used here is available from https://github.com/RamanLab/metquest/tree/master/MSI_Protocol .

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Evaluating Metabolic Support in Pairwise Microbial Communities Using MetQuest

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  • 发表日期：

    2026年

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  • Pratyay Sengupta,
  • Sandhya Vasudevan,
  • Karthik Raman

摘要

This chapter delves into MetQuest, a computational tool that enumerates all possible reaction pathways that are feasible in genome-scale metabolic networks given a set of available metabolites. MetQuest accomplishes this by leveraging a guided breadth-first search framework, followed by dynamic programming. It enables us to perform interesting microbial community analyses, such as identifying metabolic exchanges between community members. Here, we specifically demonstrate how MetQuest can be employed to determine the Metabolic Support Index (MSI) for a pairwise microbial community. MSI is a metric that reflects an organism’s benefit from the other member in co-culture, shedding light on the dynamics governing community interactions. Here, we present a pipeline to compute the MSI for two-membered communities from their genome-scale metabolic models. We illustrate this approach using the case of Acinetobacter baumannii and Klebsiella pneumoniae, two bacteria known to cross-feed metabolites. The MetQuest Python protocol used here is available from https://github.com/RamanLab/metquest/tree/master/MSI_Protocol .