Constraint-based models predict metabolic and associated cellular functions

By Aarash Bordbar, Jonathan M. Monk, Zachary A. King, Bernhard O. Palsson. Nature reviews genetics, 2014

835 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/7b0aa266562518af198631641cbd6db50ca8362f

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Citations in the corpus

Fast automated reconstruction of genome-scale metabolic models for microbial species and communities (2018, background): “To this end, genome-scale metabolic models provide a mechanistic basis allowing to predict the effects of, e.g. gene knockouts, or nutritional changes (1,2).”
BiGG Models: A platform for integrating, standardizing and sharing genome-scale models (2015, methodology): “GEMs can be used to predict cellular phenotypes (8), contextualize omics data (9–11), design cell factories (12,13) and understand evolutionary trajectories (14).”
Insights from 20 years of bacterial genome sequencing (2015, background): “This has enabled several developments on large-scale network analysis (McCloskey et al. 2013) that can have several applications (Bordbar et al. 2014).”
Gut microbiota functions: metabolism of nutrients and other food components (2017, background): “, biomass production, metabolic fluxes through the network that satisfy this objective are predicted [146].”
Quantitative and logic modelling of gene and molecular networks (2015, background): “I focus on dynamic models of biochemical reactions in homogeneous (that is, well-stirred) compartments; this Review does not cover the structural analysis of networks (21) and steady-state analysis (22), nor does it describe methods to spatially model reaction diffusion, which have been covered else”
An atlas of human metabolism (2020, background): “A major advantage of GEMs is their ability to integrate different molecular datatypes to enable the interpretation of such data within the context of metabolism (23).”
Using Genome-Scale Models to Predict Biological Capabilities (2015, positive): “The ﬁrst GEM was created for Haemophilus inﬂuenza and appeared shortly after this ﬁrst genome was sequenced (Edwards and Palsson, 1999), and GEMs have now grown to the level where they enable predictive biology (Bordbar et al., 2014; McCloskey et al., 2013; Oberhardt et al., 2009).”
The effects of temperature on aerobic metabolism: towards a mechanistic understanding of the responses of ectotherms to a changing environment (2015, background): “Such ‘systems biology’ approaches, coupled with analytical methods such as hierarchical analysis (ter Kuile and Westerhof, 2001), hold the promise of disentangling the mechanisms via which temperature affects complex metabolic networks and intact cells (Bordbar et al., 2014).”
Escher: A Web Application for Building, Sharing, and Embedding Data-Rich Visualizations of Biological Pathways (2015, methodology): “This information is provided by a c on- straint-b ased r econstruction and a nalysis (COBRA) model, a collection of all the reactions, metabolites, and genes known to exist in an organism (also called a genome-scale model (GEM) or constraint-based model (CBM)) [22].”