RT Journal Article SR Electronic T1 Literature mining supports a next-generation modeling approach to predict cellular byproduct secretion JF bioRxiv FD Cold Spring Harbor Laboratory SP 066944 DO 10.1101/066944 A1 Zachary A. King A1 Edward J. O’Brien A1 Adam M. Feist A1 Bernhard O. Palsson YR 2016 UL http://biorxiv.org/content/early/2016/08/01/066944.abstract AB The metabolic byproducts secreted by growing cells can be easily measured and provide a window into the state of a cell; they have been essential to the development of microbiology1, cancer biology2, and biotechnology3. Progress in computational modeling of cells has made it possible to predict metabolic byproduct secretion with bottom-up reconstructions of metabolic networks. However, owing to a lack of data, it has not been possible to validate these predictions across a wide range of strains and conditions. Through literature mining, we were able to generate a database of Escherichia coli strains and their experimentally measured byproduct secretions. We simulated these strains in six historical genome-scale models of E. coli, and we report that the predictive power of the models has increased as they have expanded in size and scope. Next-generation models of metabolism and gene expression are even more capable than previous models, but parameterization poses new challenges.