Literature DB >> 25392417

CeCaFDB: a curated database for the documentation, visualization and comparative analysis of central carbon metabolic flux distributions explored by 13C-fluxomics.

Zhengdong Zhang1, Tie Shen2, Bin Rui3, Wenwei Zhou2, Xiangfei Zhou4, Chuanyu Shang4, Chenwei Xin3, Xiaoguang Liu2, Gang Li4, Jiansi Jiang4, Chao Li2, Ruiyuan Li4, Mengshu Han4, Shanping You4, Guojun Yu4, Yin Yi4, Han Wen5, Zhijie Liu6, Xiaoyao Xie7.   

Abstract

The Central Carbon Metabolic Flux Database (CeCaFDB, available at http://www.cecafdb.org) is a manually curated, multipurpose and open-access database for the documentation, visualization and comparative analysis of the quantitative flux results of central carbon metabolism among microbes and animal cells. It encompasses records for more than 500 flux distributions among 36 organisms and includes information regarding the genotype, culture medium, growth conditions and other specific information gathered from hundreds of journal articles. In addition to its comprehensive literature-derived data, the CeCaFDB supports a common text search function among the data and interactive visualization of the curated flux distributions with compartmentation information based on the Cytoscape Web API, which facilitates data interpretation. The CeCaFDB offers four modules to calculate a similarity score or to perform an alignment between the flux distributions. One of the modules was built using an inter programming algorithm for flux distribution alignment that was specifically designed for this study. Based on these modules, the CeCaFDB also supports an extensive flux distribution comparison function among the curated data. The CeCaFDB is strenuously designed to address the broad demands of biochemists, metabolic engineers, systems biologists and members of the -omics community.
© The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

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Year:  2014        PMID: 25392417      PMCID: PMC4383945          DOI: 10.1093/nar/gku1137

Source DB:  PubMed          Journal:  Nucleic Acids Res        ISSN: 0305-1048            Impact factor:   16.971


  48 in total

Review 1.  13C metabolic flux analysis.

Authors:  W Wiechert
Journal:  Metab Eng       Date:  2001-07       Impact factor: 9.783

2.  Metabolic network structure determines key aspects of functionality and regulation.

Authors:  Jörg Stelling; Steffen Klamt; Katja Bettenbrock; Stefan Schuster; Ernst Dieter Gilles
Journal:  Nature       Date:  2002-11-14       Impact factor: 49.962

3.  Quantitative characterization of metabolism and metabolic shifts during growth of the new human cell line AGE1.HN using time resolved metabolic flux analysis.

Authors:  Jens Niklas; Eva Schräder; Volker Sandig; Thomas Noll; Elmar Heinzle
Journal:  Bioprocess Biosyst Eng       Date:  2010-12-25       Impact factor: 3.210

4.  Elementary metabolite units (EMU): a novel framework for modeling isotopic distributions.

Authors:  Maciek R Antoniewicz; Joanne K Kelleher; Gregory Stephanopoulos
Journal:  Metab Eng       Date:  2006-09-17       Impact factor: 9.783

5.  A whole-cell computational model predicts phenotype from genotype.

Authors:  Jonathan R Karr; Jayodita C Sanghvi; Derek N Macklin; Miriam V Gutschow; Jared M Jacobs; Benjamin Bolival; Nacyra Assad-Garcia; John I Glass; Markus W Covert
Journal:  Cell       Date:  2012-07-20       Impact factor: 41.582

6.  In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data.

Authors:  J S Edwards; R U Ibarra; B O Palsson
Journal:  Nat Biotechnol       Date:  2001-02       Impact factor: 54.908

7.  Indole-3-acetic acid regulates the central metabolic pathways in Escherichia coli.

Authors:  C Bianco; E Imperlini; R Calogero; B Senatore; P Pucci; R Defez
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Authors:  Sarah Schatschneider; Claudia Huber; Heiko Neuweger; Tony Francis Watt; Alfred Pühler; Wolfgang Eisenreich; Christoph Wittmann; Karsten Niehaus; Frank-Jörg Vorhölter
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Review 9.  Metabolic networks in motion: 13C-based flux analysis.

Authors:  Uwe Sauer
Journal:  Mol Syst Biol       Date:  2006-11-14       Impact factor: 11.429

10.  H-DBAS: human-transcriptome database for alternative splicing: update 2010.

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Journal:  Nucleic Acids Res       Date:  2009-12-07       Impact factor: 16.971

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  13 in total

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Authors:  Maciek R Antoniewicz
Journal:  J Ind Microbiol Biotechnol       Date:  2015-01-23       Impact factor: 3.346

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Authors:  Ziwei Dai; Jason W Locasale
Journal:  Metab Eng       Date:  2016-09-22       Impact factor: 9.783

3.  A genome-scale Escherichia coli kinetic metabolic model k-ecoli457 satisfying flux data for multiple mutant strains.

Authors:  Ali Khodayari; Costas D Maranas
Journal:  Nat Commun       Date:  2016-12-20       Impact factor: 14.919

Review 4.  13C-Metabolic Flux Analysis: An Accurate Approach to Demystify Microbial Metabolism for Biochemical Production.

Authors:  Weihua Guo; Jiayuan Sheng; Xueyang Feng
Journal:  Bioengineering (Basel)       Date:  2015-12-25

5.  WUFlux: an open-source platform for 13C metabolic flux analysis of bacterial metabolism.

Authors:  Lian He; Stephen G Wu; Muhan Zhang; Yixin Chen; Yinjie J Tang
Journal:  BMC Bioinformatics       Date:  2016-11-04       Impact factor: 3.169

6.  Statistical mechanics for metabolic networks during steady state growth.

Authors:  Daniele De Martino; Anna Mc Andersson; Tobias Bergmiller; Călin C Guet; Gašper Tkačik
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7.  The Design of FluxML: A Universal Modeling Language for 13C Metabolic Flux Analysis.

Authors:  Martin Beyß; Salah Azzouzi; Michael Weitzel; Wolfgang Wiechert; Katharina Nöh
Journal:  Front Microbiol       Date:  2019-05-24       Impact factor: 5.640

8.  Rapid Prediction of Bacterial Heterotrophic Fluxomics Using Machine Learning and Constraint Programming.

Authors:  Stephen Gang Wu; Yuxuan Wang; Wu Jiang; Tolutola Oyetunde; Ruilian Yao; Xuehong Zhang; Kazuyuki Shimizu; Yinjie J Tang; Forrest Sheng Bao
Journal:  PLoS Comput Biol       Date:  2016-04-19       Impact factor: 4.475

9.  Machine learning framework for assessment of microbial factory performance.

Authors:  Tolutola Oyetunde; Di Liu; Hector Garcia Martin; Yinjie J Tang
Journal:  PLoS One       Date:  2019-01-15       Impact factor: 3.240

10.  Datanator: an integrated database of molecular data for quantitatively modeling cellular behavior.

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Journal:  Nucleic Acids Res       Date:  2021-01-08       Impact factor: 16.971

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