Nov 7, 2018

Guiding the Refinement of Biochemical Knowledgebases with Ensembles of Metabolic Networks and Machine Learning

BioRxiv : the Preprint Server for Biology
Gregory L Medlock, Jason A Papin

Abstract

Mechanistic models are becoming common in biology and medicine. These models are often more generalizable than data-driven models because they explicitly represent biological knowledge, enabling simulation of scenarios that were not used to construct the model. While this generalizability has advantages, it also creates a dilemma: how should model curation efforts be focused to improve model performance? Here, we develop a machine learning-guided solution to this problem for genome-scale metabolic models. We generate an ensemble of candidate models consistent with experimental data, then perform in silico ensemble simulations for which improved predictiveness is desired. We apply unsupervised and supervised learning to the simulation output to identify structural variation in ensemble members that maximally influences variance in simulation outcomes across the ensemble. The resulting structural variants are high priority candidates for curation through targeted experimentation. We demonstrate this approach, called A utomated M etabolic M odel E nsemble- D riven E limination of U ncertainty with S tatistical learning ( AMMEDEUS ), by applying it to 29 bacterial species to identify curation targets that improve gene essentiality ...Continue Reading

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Mentioned in this Paper

Genes
Metabolic Networks
SHFM1
Structure
Simulation
Learning
Experimentation

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