Literature mining supports a next-generation modeling approach to predict cellular byproduct secretion.

TitleLiterature mining supports a next-generation modeling approach to predict cellular byproduct secretion.
Publication TypeJournal Article
Year of Publication2016
AuthorsKing ZA, O'Brien EJ, Feist AM, Palsson BO
JournalMetab Eng
PubMed Date12/2016

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 microbiology, cancer biology, and biotechnology. 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. The latest genome-scale model of metabolism correctly predicts byproduct secretion for 35/89 (39%) of designs. The next-generation genome-scale model of metabolism and gene expression (ME-model) correctly predicts byproduct secretion for 40/89 (45%) of designs, and we show that ME-model predictions could be further improved through kinetic parameterization. We analyze the failure modes of these simulations and discuss opportunities to improve prediction of byproduct secretion.

Alternate JournalMetab. Eng.
PubMed ID27986597
Cover Image: 



417 Powell-Focht Bioengineering Hall

9500 Gilman Drive La Jolla, CA 92093-0412

Contact Us

Contact Us

In Silico Lab:  858-822-1144

Wet Lab:  858-246-1625

FAX:   858-822-3120

Website Concerns:


Visit the Official SBRG YouTube Channel

User Login