Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534
Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534
Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534
Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\26666757
.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117 BMC+Syst+Biol
2015 ; 9
(ä): 93
Nephropedia Template TP
gab.com Text
Twit Text FOAVip
Twit Text #
English Wikipedia
FlexFlux: combining metabolic flux and regulatory network analyses
#MMPMID26666757
Marmiesse L
; Peyraud R
; Cottret L
BMC Syst Biol
2015[Dec]; 9
(ä): 93
PMID26666757
show ga
BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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.