Standardizing biomass reactions and ensuring complete mass balance in genome-scale metabolic models

Siu H.J. Chan, Jingyi Cai, Lin Wang, Margaret N. Simons-Senftle, Costas D. Maranas

Research output: Contribution to journalArticle

20 Scopus citations

Abstract

Motivation In a genome-scale metabolic model, the biomass produced is defined to have a molecular weight (MW) of 1 g mmol â 1. This is critical for correctly predicting growth yields, contrasting multiple models and more importantly modeling microbial communities. However, the standard is rarely verified in the current practice and the chemical formulae of biomass components such as proteins, nucleic acids and lipids are often represented by undefined side groups (e.g. X, R). Results We introduced a systematic procedure for checking the biomass weight and ensuring complete mass balance of a model. We identified significant departures after examining 64 published models. The biomass weights of 34 models differed by 5-50%, while 8 models have discrepancies >50%. In total 20 models were manually curated. By maximizing the original versus corrected biomass reactions, flux balance analysis revealed >10% differences in growth yields for 12 of the curated models. Biomass MW discrepancies are accentuated in microbial community simulations as they can cause significant and systematic errors in the community composition. Microbes with underestimated biomass MWs are overpredicted in the community whereas microbes with overestimated biomass weights are underpredicted. The observed departures in community composition are disproportionately larger than the discrepancies in the biomass weight estimate. We propose the presented procedure as a standard practice for metabolic reconstructions. Availability and implementation The MALTAB and Python scripts are available in theSupplementary Material. Contact costas@psu.edu or joshua.chan@connect.polyu.hk Supplementary informationSupplementary dataare available at Bioinformatics online.

Original languageEnglish (US)
Pages (from-to)3603-3609
Number of pages7
JournalBioinformatics
Volume33
Issue number22
DOIs
StatePublished - Nov 15 2017

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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