scholarly journals Flux Coupling Analysis of Genome-Scale Metabolic Network Reconstructions

2004 ◽  
Vol 14 (2) ◽  
pp. 301-312 ◽  
Author(s):  
A. P. Burgard
Keyword(s):  
Metabolic Network ◽  
Coupling Analysis ◽  
Flux Coupling ◽  
Genome Scale ◽  
Flux Coupling Analysis

scholarly journals Efficient enzyme coupling algorithms identify functional pathways in genome-scale metabolic models

2019 ◽  
Author(s):  
Dikshant Pradhan ◽  
Jason A. Papin ◽  
Paul A. Jensen
Keyword(s):  
Metabolic Engineering ◽  
Mathematical Framework ◽  
Continuous Variables ◽  
Convex Constraints ◽  
Coupling Analysis ◽  
Flux Coupling ◽  
Scale Models ◽  
Metabolic Models ◽  
Genome Scale ◽  
Flux Coupling Analysis

AbstractFlux coupling identifies sets of reactions whose fluxes are “coupled" or correlated in genome-scale models. By identified sets of coupled reactions, modelers can 1.) reduce the dimensionality of genome-scale models, 2.) identify reactions that must be modulated together during metabolic engineering, and 3.) identify sets of important enzymes using high-throughput data. We present three computational tools to improve the efficiency, applicability, and biological interpretability of flux coupling analysis.The first algorithm (cachedFCF) uses information from intermediate solutions to decrease the runtime of standard flux coupling methods by 10-100 fold. Importantly, cachedFCF makes no assumptions regarding the structure of the underlying model, allowing efficient flux coupling analysis of models with non-convex constraints.We next developed a mathematical framework (FALCON) that incorporates enzyme activity as continuous variables in genome-scale models. Using data from gene expression and fitness assays, we verified that enzyme sets calculated directly from FALCON models are more functionally coherent than sets of enzymes collected from coupled reaction sets.Finally, we present a method (delete-and-couple) for expanding enzyme sets to allow redundancies and branches in the associated metabolic pathways. The expanded enzyme sets align with known biological pathways and retain functional coherence. The expanded enzyme sets allow pathway-level analyses of genome-scale metabolic models.Together, our algorithms extend flux coupling techniques to enzymatic networks and models with transcriptional regulation and other non-convex constraints. By expanding the efficiency and flexibility of flux coupling, we believe this popular technique will find new applications in metabolic engineering, microbial pathogenesis, and other fields that leverage network modeling.

On flux coupling analysis of metabolic subsystems

2012 ◽  
Vol 302 ◽  
pp. 62-69 ◽  
Author(s):  
Sayed-Amir Marashi ◽  
Laszlo David ◽  
Alexander Bockmayr
Keyword(s):  
Coupling Analysis ◽  
Flux Coupling ◽  
Flux Coupling Analysis

Quantitative flux coupling analysis

2018 ◽  
Vol 78 (5) ◽  
pp. 1459-1484 ◽  
Author(s):  
Mojtaba Tefagh ◽  
Stephen P. Boyd
Keyword(s):  
Coupling Analysis ◽  
Flux Coupling ◽  
Flux Coupling Analysis

scholarly journals Generic flux coupling analysis

2015 ◽  
Vol 262 ◽  
pp. 28-35
Author(s):  
Arne C. Reimers ◽  
Yaron Goldstein ◽  
Alexander Bockmayr
Keyword(s):  
Coupling Analysis ◽  
Flux Coupling ◽  
Flux Coupling Analysis

scholarly journals FFCA: a feasibility-based method for flux coupling analysis of metabolic networks

2011 ◽  
Vol 12 (1) ◽  
pp. 236 ◽  
Author(s):  
Laszlo David ◽  
Sayed-Amir Marashi ◽  
Abdelhalim Larhlimi ◽  
Bettina Mieth ◽  
Alexander Bockmayr
Keyword(s):  
Metabolic Networks ◽  
Coupling Analysis ◽  
Flux Coupling ◽  
Flux Coupling Analysis

scholarly journals Genome-Scale Metabolic Network Analysis of the Opportunistic Pathogen Pseudomonas aeruginosa PAO1

2008 ◽  
Vol 190 (8) ◽  
pp. 2790-2803 ◽  
Author(s):  
Matthew A. Oberhardt ◽  
Jacek Puchałka ◽  
Kimberly E. Fryer ◽  
Vítor A. P. Martins dos Santos ◽  
Jason A. Papin
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Metabolic Network ◽  
Opportunistic Pathogen ◽  
Scale Model ◽  
Modeling Framework ◽  
Major Life ◽  
Metabolic Versatility ◽  
A Genome ◽  
Scale Properties ◽  
Genome Scale

ABSTRACT Pseudomonas aeruginosa is a major life-threatening opportunistic pathogen that commonly infects immunocompromised patients. This bacterium owes its success as a pathogen largely to its metabolic versatility and flexibility. A thorough understanding of P. aeruginosa's metabolism is thus pivotal for the design of effective intervention strategies. Here we aim to provide, through systems analysis, a basis for the characterization of the genome-scale properties of this pathogen's versatile metabolic network. To this end, we reconstructed a genome-scale metabolic network of Pseudomonas aeruginosa PAO1. This reconstruction accounts for 1,056 genes (19% of the genome), 1,030 proteins, and 883 reactions. Flux balance analysis was used to identify key features of P. aeruginosa metabolism, such as growth yield, under defined conditions and with defined knowledge gaps within the network. BIOLOG substrate oxidation data were used in model expansion, and a genome-scale transposon knockout set was compared against in silico knockout predictions to validate the model. Ultimately, this genome-scale model provides a basic modeling framework with which to explore the metabolism of P. aeruginosa in the context of its environmental and genetic constraints, thereby contributing to a more thorough understanding of the genotype-phenotype relationships in this resourceful and dangerous pathogen.

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