Capturing biological pathways is a massive database challenge: it implies representing metabolites in a way that is understandable to both humans and computers, and which enables their interconnection through the biochemical reactions they engage in. A number of public resources depict one or other of these aspects, but achieving interoperability among them is the crux. The database presented in this in silico talk offers a solution to this end. SIB’s Marco Pagni from our Vital-IT group highlights what sets MetaNetX/MNXref apart and how it powers a range of applications, from metabolic models to synthetic biology and bioengineering.

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What has the story of the Tower of Babel to do with the way metabolic networks are currently described? A clue: the diversity of languages used to describe them. As Marco puts it in his talk, “Models published by different groups are very hard to compare and reconcile.”

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Indeed, several databases already exist and focus on one or more layers of metabolism-related information: the description of metabolites (i.e. chemical compounds) per se, the chemical reactions they are involved in, or Genome-Scale Metabolic Networks (i.e. the metabolic models that capture the different reactions involved in a specific biological process).

MetaNetX/MNXref is designed as a “multilingual dictionary” linking the major public resources related to metabolism.

Listen to Marco explaining the challenge building such a resource represents, and how the resulting dataset in its latest release, which includes over 1 million metabolites and over 50K reactions, can be used to identify essential reactions in a pathway or guide the design of experimental studies.

Moretti S et al. MetaNetX/MNXref: unified namespace for metabolites and biochemical reactions in the context of metabolic models, Nucleid Acids Research 2021. DOI: 10.1093/nar/gkaa992