Evaluating E. coli genome‐scale metabolic model accuracy with high‐throughput mutant fitness data
[1];
[3]
- Department of Bioengineering University of California Berkeley CA USA
- Environmental Genomics and Systems Biology Division Lawrence Berkeley National Laboratory Berkeley CA USA
- Department of Bioengineering University of California Berkeley CA USA, Environmental Genomics and Systems Biology Division Lawrence Berkeley National Laboratory Berkeley CA USA
Abstract The Escherichia coli genome‐scale metabolic model (GEM) is an exemplar systems biology model for the simulation of cellular metabolism. Experimental validation of model predictions is essential to pinpoint uncertainty and ensure continued development of accurate models. Here, we quantified the accuracy of four subsequent E. coli GEMs using published mutant fitness data across thousands of genes and 25 different carbon sources. This evaluation demonstrated the utility of the area under a precision–recall curve relative to alternative accuracy metrics. An analysis of errors in the latest (iML1515) model identified several vitamins/cofactors that are likely available to mutants despite being absent from the experimental growth medium and highlighted isoenzyme gene‐protein‐reaction mapping as a key source of inaccurate predictions. A machine learning approach further identified metabolic fluxes through hydrogen ion exchange and specific central metabolism branch points as important determinants of model accuracy. This work outlines improved practices for the assessment of GEM accuracy with high‐throughput mutant fitness data and highlights promising areas for future model refinement in E. coli and beyond.
- Research Organization:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Biological and Environmental Research (BER); USDOE National Nuclear Security Administration (NNSA)
- Grant/Contract Number:
- DE‐NA0003920; DE‐AC02‐05CH11231; AC02-05CH11231; NA0003920
- OSTI ID:
- 2203993
- Alternate ID(s):
- OSTI ID: 2203995; OSTI ID: 2234035
- Journal Information:
- Molecular Systems Biology, Journal Name: Molecular Systems Biology Vol. 19 Journal Issue: 12; ISSN 1744-4292
- Publisher:
- Nature Publishing GroupCopyright Statement
- Country of Publication:
- United Kingdom
- Language:
- English
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