title: Literature mining supports a next-generation modeling approach to predict cellular byproduct secretion.
abstract: 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.
authors.affiliation: Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive #0412, La Jolla, CA 92093-0412, USA.
authors.forename: Zachary A
authors.initials: ZA
authors.lastname: King
authors.affiliation: Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive #0412, La Jolla, CA 92093-0412, USA.
authors.forename: Edward J
authors.initials: EJ
authors.lastname: O'Brien
authors.affiliation: Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive #0412, La Jolla, CA 92093-0412, USA; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220, DK-2800 Kongens, Lyngby, Denmark.
authors.forename: Adam M
authors.initials: AM
authors.lastname: Feist
authors.affiliation: Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive #0412, La Jolla, CA 92093-0412, USA; Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive #0412, La Jolla, CA 92093-0412, USA; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220, DK-2800 Kongens, Lyngby, Denmark. Electronic address: palsson@ucsd.edu.
authors.forename: Bernhard O
authors.initials: BO
authors.lastname: Palsson
mesh_terms: D001704:Biopolymers / Q000378:metabolism*; D003198:Computer Simulation; D057225:Data Mining / Q000379:methods*; D004926:Escherichia coli / Q000378:metabolism*; D029968:Escherichia coli Proteins / Q000378:metabolism*; D015964:Gene Expression Regulation, Bacterial / Q000502:physiology; D064688:Metabolic Flux Analysis / Q000379:methods*; D008954:Models, Biological; D010506:Periodicals as Topic
chemical_list: D001704:Biopolymers; D029968:Escherichia coli Proteins
keywords: Constraint-based modeling; Escherichia coli; Genome-scale model; Literature mining; ME-model