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bioRxiv · 10.1101/066944

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

Zachary Andrew King·Edward J O'Brien·Adam M Feist·Bernhard O Palsson
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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 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.

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Zachary Andrew King, Edward J O'Brien, Adam M Feist, Bernhard O Palsson. 2016-08-01. Literature mining supports a next-generation modeling approach to predict cellular byproduct secretion. https://doi.org/10.1101/066944

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