MetaNetX/MNXref: unified namespace for metabolic models
Channel: SIB - Swiss Institute of Bioinformatics
Date: 2021-04-30

What you will discover: a database of reconciled metabolites and biochemical reactions

You are: a molecular or computational biologist studying genome-scale metabolic networks, a synthetic biology or system biology researcher

Speaker: Marco Pagni, Senior Scientist at SIB

Click to access specific sections of the talk: 
- 00:50 Introducing the database challenge to represent metabolism
- 04:55 Presenting MetaNetX/MNXref 
- 05:33 Modelling Genome-Scale Metabolic Networks
- 09:20 Latest reconciliation and dataset in MetaNetX
- 10:16 Examples of applications: cross-links, MEMOTE, synthetic biology...
- 11:27 The MetaNetX website

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

Other resources mentioned in this talk: 
- https://www.rhea-db.org/
- http://bigg.ucsd.edu/ 
- https://www.genome.jp/kegg/kegg1.html
- https://www.ebi.ac.uk/chebi/
- https://www.kbase.us/
- https://metacyc.org/
- https://memote.io/

Any question about this talk? Contact Marco at marco.pagni[at]sib.swiss

More about the speaker:
Marco Pagni is a senior scientist and project leader at Vital-IT. He studied biology, obtained a PhD in experimental Microbiology and joined the newly created SIB Swiss Institute of Bioinformatics in 1998. He has since collaborated with many groups within SIB on topics related to sequence analysis, sequence automated annotation, relational database management system, high performance computing, numerical simulations and algorithmic developments. In parallel, he has been collaborating with wet lab researchers, participating to many research projects often related to microbiology. He contributed to over sixty papers in peer-reviewed journals on topics spanning a broad range of research topics. His most recent contributions are focused on fungal genomics, metagenomics and genome-scale metabolic networks.

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