@article{Bernstein2023,

title = {Evaluating \textit{E. coli} genome‐scale metabolic model accuracy with high‐throughput mutant fitness data},

author = {David B Bernstein and Batu Akkas and Morgan N Price and Adam P Arkin},

doi = {10.15252/msb.202311566},

issn = {1744-4292},

year  = {2023},

date = {2023-12-06},

journal = {Molecular Systems Biology},

volume = {19},

number = {12},

publisher = {Springer Science and Business Media LLC},

abstract = {AbstractThe 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.},

keywords = {Applied Mathematics, Computational Theory and Mathematics, General Agricultural and Biological Sciences, General Biochemistry, General Immunology and Microbiology, Genetics and Molecular Biology, Information Systems},

pubstate = {published},

tppubtype = {article}

}