Software applications toward quantitative metabolic flux analysis and modeling

Dandekar, Thomas; Fieselmann, Astrid; Majeed, Saman; Ahmed, Zeeshan

doi:10.1093/bib/bbs065

Cited by 43 publications

(23 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To visualize these flux data is also important to activation/deactivation of individual reactions. FluxMap [40,41] is one of the tools to incorporate such type of data. A recent review article [42] also lists such tools.…”

Section: Resultsmentioning

confidence: 99%

Metabolomic pathway visualization tool outsourcing editing function

Sugimoto

2015

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

View full text Add to dashboard Cite

Recent rapid improvements of measuring instrument enables us to perform various omics studies to simultaneous profile multiple molecules, which provides a holistic view of various molecular interactions, such as signal transaction, protein interactions, and metabolic pathways. Metabolomics is recently emerged omics that can identify and quantify low weight metabolites usually defined as organic molecules whose size is <; 1500 Da. In comparison to the other omics, the development of software tools to deal with metabolomic data is not matured. Conventional pathway drawing and visualization tool provide tool-specific unique functions, however, such user interface requires users to learn the usage and prevention for the use of these tools. Here, we developed a more generic pathway visualization tool. This tool incorporate pathway data yielded by common drawing tools, e.g. MS PowerPoint, and visualize the quantified values on the pathways. The statistical results also can be overlaid on each metabolite. The developed tools facilitate the interpreting metabolomic data in pathway forms.

show abstract

Section: Resultsmentioning

confidence: 99%

Metabolomic pathway visualization tool outsourcing editing function

Sugimoto

2015

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

View full text Add to dashboard Cite

show abstract

“…In general, other tools for metabolic model visualization tend to be less comprehensive, and to be less closely coupled to the model; see [183-186] for recent reviews.…”

Section: Resultsmentioning

confidence: 99%

A genome-scale metabolic flux model of Escherichia coli K–12 derived from the EcoCyc database

Weaver

Keseler

Mackie

et al. 2014

BMC Systems Biology

View full text Add to dashboard Cite

BackgroundConstraint-based models of Escherichia coli metabolic flux have played a key role in computational studies of cellular metabolism at the genome scale. We sought to develop a next-generation constraint-based E. coli model that achieved improved phenotypic prediction accuracy while being frequently updated and easy to use. We also sought to compare model predictions with experimental data to highlight open questions in E. coli biology.ResultsWe present EcoCyc–18.0–GEM, a genome-scale model of the E. coli K–12 MG1655 metabolic network. The model is automatically generated from the current state of EcoCyc using the MetaFlux software, enabling the release of multiple model updates per year. EcoCyc–18.0–GEM encompasses 1445 genes, 2286 unique metabolic reactions, and 1453 unique metabolites. We demonstrate a three-part validation of the model that breaks new ground in breadth and accuracy: (i) Comparison of simulated growth in aerobic and anaerobic glucose culture with experimental results from chemostat culture and simulation results from the E. coli modeling literature. (ii) Essentiality prediction for the 1445 genes represented in the model, in which EcoCyc–18.0–GEM achieves an improved accuracy of 95.2% in predicting the growth phenotype of experimental gene knockouts. (iii) Nutrient utilization predictions under 431 different media conditions, for which the model achieves an overall accuracy of 80.7%. The model’s derivation from EcoCyc enables query and visualization via the EcoCyc website, facilitating model reuse and validation by inspection. We present an extensive investigation of disagreements between EcoCyc–18.0–GEM predictions and experimental data to highlight areas of interest to E. coli modelers and experimentalists, including 70 incorrect predictions of gene essentiality on glucose, 80 incorrect predictions of gene essentiality on glycerol, and 83 incorrect predictions of nutrient utilization.ConclusionSignificant advantages can be derived from the combination of model organism databases and flux balance modeling represented by MetaFlux. Interpretation of the EcoCyc database as a flux balance model results in a highly accurate metabolic model and provides a rigorous consistency check for information stored in the database.

show abstract

“…The workflow of RetroPath that finds and enumerates the pathways consists of (i) Computing the scope: the algorithm performs a two-step search, first a forward search to determine all compounds that are reachable, and then a backward step in order to add to the scope starting from the target compound any reaction that can be involved in a target-producing pathway imported into the chassis; (ii) Construction of the stoichiometry matrix corresponding to the target scope starting from precursors that are endogenous to the chassis; (iii) Apply elementary flux modes (30) in order to determine all target-producing pathways.…”

Section: Methodsmentioning

confidence: 99%

XTMS: pathway design in an eXTended metabolic space

Carbonell

Parutto

Hérisson

et al. 2014

Nucleic Acids Research

104

View full text Add to dashboard Cite

As metabolic engineering and synthetic biology progress toward reaching the goal of a more sustainable use of biological resources, the need of increasing the number of value-added chemicals that can be produced in industrial organisms becomes more imperative. Exploring, however, the vast possibility of pathways amenable to engineering through heterologous genes expression in a chassis organism is complex and unattainable manually. Here, we present XTMS, a web-based pathway analysis platform available at http://xtms.issb.genopole.fr, which provides full access to the set of pathways that can be imported into a chassis organism such as Escherichia coli through the application of an Extended Metabolic Space modeling framework. The XTMS approach consists on determining the set of biochemical transformations that can potentially be processed in vivo as modeled by molecular signatures, a specific coding system for derivation of reaction rules for metabolic reactions and enumeration of all the corresponding substrates and products. Most promising routes are described in terms of metabolite exchange, maximum allowable pathway yield, toxicity and enzyme efficiency. By answering such critical design points, XTMS not only paves the road toward the rationalization of metabolic engineering, but also opens new processing possibilities for non-natural metabolites and novel enzymatic transformations.

show abstract

Software applications toward quantitative metabolic flux analysis and modeling

Cited by 43 publications

References 56 publications

Metabolomic pathway visualization tool outsourcing editing function

Metabolomic pathway visualization tool outsourcing editing function

A genome-scale metabolic flux model of Escherichia coli K–12 derived from the EcoCyc database

XTMS: pathway design in an eXTended metabolic space

Contact Info

Product

Resources

About