Siu H J Chan1, Jingyi Cai2, Lin Wang1, Margaret N Simons-Senftle1, Costas D Maranas1. 1. Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16801, USA. 2. Beijing Key Lab of Bioprocess, College of Life and Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.
Abstract
MOTIVATION: In a genome-scale metabolic model, the biomass produced is defined to have a molecular weight (MW) of 1 g mmol-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 the Supplementary Material. CONTACT: costas@psu.edu or joshua.chan@connect.polyu.hk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: In a genome-scale metabolic model, the biomass produced is defined to have a molecular weight (MW) of 1 g mmol-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 the Supplementary Material. CONTACT: costas@psu.edu or joshua.chan@connect.polyu.hk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Jean-Christophe Lachance; Colton J Lloyd; Jonathan M Monk; Laurence Yang; Anand V Sastry; Yara Seif; Bernhard O Palsson; Sébastien Rodrigue; Adam M Feist; Zachary A King; Pierre-Étienne Jacques Journal: PLoS Comput Biol Date: 2019-04-22 Impact factor: 4.475
Authors: Víctor A López-Agudelo; Tom A Mendum; Emma Laing; HuiHai Wu; Andres Baena; Luis F Barrera; Dany J V Beste; Rigoberto Rios-Estepa Journal: PLoS Comput Biol Date: 2020-06-15 Impact factor: 4.475