title: Generalized framework for context-specific metabolic model extraction methods.
abstract: Genome-scale metabolic models (GEMs) are increasingly applied to investigate the physiology not only of simple prokaryotes, but also eukaryotes, such as plants, characterized with compartmentalized cells of multiple types. While genome-scale models aim at including the entirety of known metabolic reactions, mounting evidence has indicated that only a subset of these reactions is active in a given context, including: developmental stage, cell type, or environment. As a result, several methods have been proposed to reconstruct context-specific models from existing genome-scale models by integrating various types of high-throughput data. Here we present a mathematical framework that puts all existing methods under one umbrella and provides the means to better understand their functioning, highlight similarities and differences, and to help users in selecting a most suitable method for an application.
authors.affiliation: Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology Potsdam-Golm, Germany.
authors.forename: Semidán
authors.initials: S
authors.lastname: Robaina Estévez
authors.affiliation: Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology Potsdam-Golm, Germany.
authors.forename: Zoran
authors.initials: Z
authors.lastname: Nikoloski
keywords: context-specific models; data integration; genome-scale models; high-throughput data; mathematical programming