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Towards creating an extended metabolic model (EMM) for E. coli using enzyme promiscuity prediction and metabolomics data.

Sara A AminElizabeth ChavezVladimir PorokhinNikhil U NairSoha Hassoun
Published in: Microbial cell factories (2019)
We utilized EMMA to augment the iML1515 metabolic model to more fully reflect cellular metabolic activity. This workflow uses enzyme promiscuity as basis to predict hundreds of reactions and metabolites that may exist in E. coli but may have not been documented in iML1515 or other databases. We provide detailed analysis of 23 predicted reactions and 16 associated metabolites. Interestingly, nine of these metabolites, which are in ECMDB, have not previously been documented in any other E. coli databases. Four of the predicted reactions provide putative transformations parallel to those already in iML1515. We suggest adding predicted metabolites and reactions to iML1515 to create an extended metabolic model (EMM) for E. coli.
Keyphrases
  • escherichia coli
  • ms ms
  • electronic health record
  • machine learning