Literature DB >> 23131418

Software applications for flux balance analysis.

Meiyappan Lakshmanan1, Geoffrey Koh, Bevan K S Chung, Dong-Yup Lee.   

Abstract

Flux balance analysis (FBA) is a widely used computational method for characterizing and engineering intrinsic cellular metabolism. The increasing number of its successful applications and growing popularity are possibly attributable to the availability of specific software tools for FBA. Each tool has its unique features and limitations with respect to operational environment, user-interface and supported analysis algorithms. Presented herein is an in-depth evaluation of currently available FBA applications, focusing mainly on usability, functionality, graphical representation and inter-operability. Overall, most of the applications are able to perform basic features of model creation and FBA simulation. COBRA toolbox, OptFlux and FASIMU are versatile to support advanced in silico algorithms to identify environmental and genetic targets for strain design. SurreyFBA, WEbcoli, Acorn, FAME, GEMSiRV and MetaFluxNet are the distinct tools which provide the user friendly interfaces in model handling. In terms of software architecture, FBA-SimVis and OptFlux have the flexible environments as they enable the plug-in/add-on feature to aid prospective functional extensions. Notably, an increasing trend towards the implementation of more tailored e-services such as central model repository and assistance to collaborative efforts was observed among the web-based applications with the help of advanced web-technologies. Furthermore, most recent applications such as the Model SEED, FAME, MetaFlux and MicrobesFlux have even included several routines to facilitate the reconstruction of genome-scale metabolic models. Finally, a brief discussion on the future directions of FBA applications was made for the benefit of potential tool developers.

Entities:  

Keywords:  FBA tools; flux balance analysis (FBA); genome-scale model reconstruction; systems biology; web applications

Mesh:

Year:  2012        PMID: 23131418     DOI: 10.1093/bib/bbs069

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  33 in total

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4.  Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0.

Authors:  Laurent Heirendt; Sylvain Arreckx; Thomas Pfau; Sebastián N Mendoza; Anne Richelle; Almut Heinken; Hulda S Haraldsdóttir; Jacek Wachowiak; Sarah M Keating; Vanja Vlasov; Stefania Magnusdóttir; Chiam Yu Ng; German Preciat; Alise Žagare; Siu H J Chan; Maike K Aurich; Catherine M Clancy; Jennifer Modamio; John T Sauls; Alberto Noronha; Aarash Bordbar; Benjamin Cousins; Diana C El Assal; Luis V Valcarcel; Iñigo Apaolaza; Susan Ghaderi; Masoud Ahookhosh; Marouen Ben Guebila; Andrejs Kostromins; Nicolas Sompairac; Hoai M Le; Ding Ma; Yuekai Sun; Lin Wang; James T Yurkovich; Miguel A P Oliveira; Phan T Vuong; Lemmer P El Assal; Inna Kuperstein; Andrei Zinovyev; H Scott Hinton; William A Bryant; Francisco J Aragón Artacho; Francisco J Planes; Egils Stalidzans; Alejandro Maass; Santosh Vempala; Michael Hucka; Michael A Saunders; Costas D Maranas; Nathan E Lewis; Thomas Sauter; Bernhard Ø Palsson; Ines Thiele; Ronan M T Fleming
Journal:  Nat Protoc       Date:  2019-03       Impact factor: 13.491

5.  Using bioconductor package BiGGR for metabolic flux estimation based on gene expression changes in brain.

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6.  COBRApy: COnstraints-Based Reconstruction and Analysis for Python.

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Journal:  BMC Syst Biol       Date:  2013-08-08

7.  Exploring metabolism flexibility in complex organisms through quantitative study of precursor sets for system outputs.

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8.  Sybil--efficient constraint-based modelling in R.

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9.  Flux Balance Analysis with Objective Function Defined by Proteomics Data-Metabolism of Mycobacterium tuberculosis Exposed to Mefloquine.

Authors:  Daniel Montezano; Laura Meek; Rashmi Gupta; Luiz E Bermudez; José C M Bermudez
Journal:  PLoS One       Date:  2015-07-28       Impact factor: 3.240

10.  Reconstruction and analysis of human kidney-specific metabolic network based on omics data.

Authors:  Ai-Di Zhang; Shao-Xing Dai; Jing-Fei Huang
Journal:  Biomed Res Int       Date:  2013-10-05       Impact factor: 3.411

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