↓ Skip to main content

FlexFlux: combining metabolic flux and regulatory network analyses

Overview of attention for article published in BMC Systems Biology, December 2015
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (74th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

twitter
7 X users

Citations

dimensions_citation
55 Dimensions

Readers on

mendeley
133 Mendeley
citeulike
2 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
FlexFlux: combining metabolic flux and regulatory network analyses
Published in
BMC Systems Biology, December 2015
DOI 10.1186/s12918-015-0238-z
Pubmed ID
Authors

Lucas Marmiesse, Rémi Peyraud, Ludovic Cottret

Abstract

Expression of cell phenotypes highly depends on metabolism that supplies matter and energy. To achieve proper utilisation of the different metabolic pathways, metabolism is tightly regulated by a complex regulatory network composed of diverse biological entities (genes, transcripts, proteins, signalling molecules…). The integrated analysis of both regulatory and metabolic networks appears very insightful but is not straightforward because of the distinct characteristics of both networks. The classical method used for metabolic flux analysis is Flux Balance Analysis (FBA), which is constraint-based and relies on the assumption of steady-state metabolite concentrations throughout the network. Regarding regulatory networks, a broad spectrum of methods are dedicated to their analysis although logical modelling remains the major method to take charge of large-scale networks. We present FlexFlux, an application implementing a new way to combine the analysis of both metabolic and regulatory networks, based on simulations that do not require kinetic parameters and can be applied to genome-scale networks. FlexFlux is based on seeking regulatory network steady-states by performing synchronous updates of multi-state qualitative initial values. FlexFlux is then able to use the calculated steady-state values as constraints for metabolic flux analyses using FBA. As input, FlexFlux uses the standards Systems Biology Markup Language (SBML) and SBML Qualitative Models Package ("qual") extension (SBML-qual) file formats and provides a set of FBA based functions. FlexFlux is an open-source java software with executables and full documentation available online at http://lipm-bioinfo.toulouse.inra.fr/flexflux/ . It can be defined as a research tool that enables a better understanding of both regulatory and metabolic networks based on steady-state simulations. FlexFlux integrates well in the flux analysis ecosystem thanks to the support of standard file formats and can thus be used as a complementary tool to existing software featuring other types of analyses.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 133 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 2%
France 1 <1%
Brazil 1 <1%
Singapore 1 <1%
China 1 <1%
Russia 1 <1%
United States 1 <1%
Unknown 125 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 37 28%
Researcher 26 20%
Student > Master 18 14%
Student > Bachelor 13 10%
Student > Doctoral Student 9 7%
Other 17 13%
Unknown 13 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 43 32%
Biochemistry, Genetics and Molecular Biology 28 21%
Computer Science 14 11%
Engineering 11 8%
Chemical Engineering 6 5%
Other 10 8%
Unknown 21 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 February 2016.
All research outputs
#7,113,712
of 25,335,657 outputs
Outputs from BMC Systems Biology
#227
of 1,131 outputs
Outputs of similar age
#103,750
of 403,588 outputs
Outputs of similar age from BMC Systems Biology
#6
of 43 outputs
Altmetric has tracked 25,335,657 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 1,131 research outputs from this source. They receive a mean Attention Score of 3.7. This one has done well, scoring higher than 79% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 403,588 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 74% of its contemporaries.
We're also able to compare this research output to 43 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.