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

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.

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

“…While simple inclusion of additional data sets with nonzero ED flux may have rectified this limitation, this a posteriori correction may not be a fair representation of the proposed model and methodology. These limitations are likely to be ameliorated as expanded metabolomic (for example, MetaboLights34) and fluxomic (for example, CeCaFDB35) data sets are becoming increasingly available. Given data sets that span the metabolic capabilities of E. coli , the proposed machine-learning inspired parameterization strategy demonstrated that it is indeed possible to train a single model to predict the genetic and environmentally perturbed phenotypes with fidelity.…”

Maximum entropy modeling of metabolic networks by constraining growth-rate moments predicts coexistence of phenotypes

“…the threshold for the acetate switch. The sample consists of 35 technical replicates collected from control experiments retrieved in the database [19] (same dataset analyzed in [11]). …”

Understanding metabolism with flux analysis: From theory to application

“…13 C metabolic flux analysis techniques have been widely applied in resolving central carbon metabolism in unicellular organisms like Escherichia coli, Corynebacterium glutamicum, Saccharomyces cerevisiae and Bacillus subtilis, which are species with richest resource of flux analysis data according to a recently published database (Zhang et al, 2015). However, application of 13 C metabolic flux analysis in mammalian cells is complicated due to existence of different subcellular compartments in eukaryotic cells, while in most cases only the average 13 C labeling pattern could be measured.…”