- -

Dynamic elementary mode modelling of non-steady state flux data

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

Dynamic elementary mode modelling of non-steady state flux data

Show full item record

Folch-Fortuny, A.; Teusink, B.; Hoefsloot, HC.; Smilde, AK.; Ferrer, A. (2018). Dynamic elementary mode modelling of non-steady state flux data. BMC Systems Biology. 12:1-15. https://doi.org/10.1186/s12918-018-0589-3

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/122511

Files in this item

Item Metadata

Title: Dynamic elementary mode modelling of non-steady state flux data
Author: Folch-Fortuny, Abel Teusink, Bas Hoefsloot, Huub C.J. Smilde, Age K. Ferrer, Alberto
UPV Unit: Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat
Issued date:
Abstract:
[EN] A novel framework is proposed to analyse metabolic fluxes in non-steady state conditions, based on the new concept of dynamic elementary mode (dynEM): an elementary mode activated partially depending on the time point ...[+]
Subjects: Metabolic network , Elementary mode , Dynamic modelling , Principal component analysis , Principal elementary mode analysis , Partial least squares regression discriminant analysis , N-way,Cross validation
Copyrigths: Reconocimiento (by)
Source:
BMC Systems Biology. (issn: 1752-0509 )
DOI: 10.1186/s12918-018-0589-3
Publisher:
Springer (Biomed Central Ltd.)
Publisher version: http://doi.org/10.1186/s12918-018-0589-3
Project ID:
info:eu-repo/grantAgreement/MINECO//DPI2014-55276-C5-1-R/ES/BIOLOGIA SINTETICA PARA LA MEJORA EN BIOPRODUCCION: DISEÑO, OPTIMIZACION, MONITORIZACION Y CONTROL/
Thanks:
This research work was partially supported by the Spanish Ministry of Economy and Competitiveness under the project DPI2014-55276-C5-1R.
Type: Artículo

References

Bro R, Smilde AK. Principal component analysis. Anal Methods. 2014; 6(9):2812–31.

González-Martínez JM, Folch-Fortuny A, Llaneras F, Tortajada M, Picó J, Ferrer A. Metabolic flux understanding of Pichia pastoris grown on heterogenous culture media. Chemometr Intell Lab Syst. 2014; 134:89–99.

Barrett CL, Herrgard MJ, Palsson B. Decomposing complex reaction networks using random sampling, principal component analysis and basis rotation. BMC Syst Biol. 2009; 3(30):1–8. [+]
Bro R, Smilde AK. Principal component analysis. Anal Methods. 2014; 6(9):2812–31.

González-Martínez JM, Folch-Fortuny A, Llaneras F, Tortajada M, Picó J, Ferrer A. Metabolic flux understanding of Pichia pastoris grown on heterogenous culture media. Chemometr Intell Lab Syst. 2014; 134:89–99.

Barrett CL, Herrgard MJ, Palsson B. Decomposing complex reaction networks using random sampling, principal component analysis and basis rotation. BMC Syst Biol. 2009; 3(30):1–8.

Jaumot J, Gargallo R, De Juan A, Tauler R. A graphical user-friendly interface for MCR-ALS: A new tool for multivariate curve resolution in MATLAB. Chemometr Intell Lab Syst. 2005; 76(1):101–10.

Folch-Fortuny A, Tortajada M, Prats-Montalbán JM, Llaneras F, Picó J, Ferrer A. MCR-ALS on metabolic networks: Obtaining more meaningful pathways. Chemometr Intell Lab Syst. 2015; 142:293–303.

Folch-Fortuny A, Marques R, Isidro IA, Oliveira R, Ferrer A. Principal elementary mode analysis (PEMA). Mol BioSyst. 2016; 12(3):737–46.

Hood L. Systems biology: Integrating technology, biology, and computation. Mech Ageing Dev. 2003; 124(1):9–16.

Teusink B, Passarge J, Reijenga CA, Esgalhado E, van der Weijden CC, Schepper M, Walsh MC, Bakker BM, van Dam K, Westerhoff HV, Snoep JL. Can yeast glycolysis be understood in terms of in vitro kinetics of the constituent enzymes? Testing biochemistry. Eur J Biochem / FEBS. 2000; 267(17):5313–29.

Mahadevan R, Edwards JS, Doyle FJ. Dynamic flux balance analysis of diauxic growth in Escherichia coli. Biophys J. 2002; 83(3):1331–40.

Willemsen AM, Hendrickx DM, Hoefsloot HCJ, Hendriks MMWB, Wahl SA, Teusink B, Smilde AK, van Kampen AHC. MetDFBA: incorporating time-resolved metabolomics measurements into dynamic flux balance analysis. Mol BioSyst. 2015; 11(1):137–45.

Barker M, Rayens W. Partial least squares for discrimination. J Chemom. 2003; 17(3):166–73.

Bartel J, Krumsiek J, Theis FJ. Statistical methods for the analysis of high-throughput metabolomics data. Comput Struct Biotechnol J. 2013; 4:201301009.

Hendrickx DM, Hoefsloot HCJ, Hendriks MMWB, Canelas AB, Smilde AK. Global test for metabolic pathway differences between conditions. Anal Chim Acta. 2012; 719:8–15.

Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita KF, Itoh M, Kawashima S, Katayama T, Araki M, Hirakawa M. From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res. 2006; 34(Database issue):354–7.

Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000; 28(1):27–30.

Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M. KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res. 2010; 38(Database issue):355–60.

Andersson CA, Bro R. The N-way Toolbox for MATLAB. Chemometr Intell Lab Syst. 2000; 52(1):1–4.

Terzer M, Stelling J. Large-scale computation of elementary flux modes with bit pattern trees. Bioinformatics. 2008; 24(19):2229–35.

Heerden JHv, Wortel MT, Bruggeman FJ, Heijnen JJ, Bollen YJM, Planqué R, Hulshof J, O’Toole TG, Wahl SA, Teusink B. Lost in Transition: Start-Up of Glycolysis Yields Subpopulations of Nongrowing Cells. Science. 2014; 343(6174):1245114.

Hoops S, Sahle S, Gauges R, Lee C, Pahle J, Simus N, Singhal M, Xu L, Mendes P, Kummer U. COPASI–a COmplex PAthway SImulator. Bioinformatics. 2006; 22(24):3067–74.

Petzold L. Automatic selection of methods for solving stiff and nonstiff systems of ordinary differential equations. SIAM J Sci Stat Comput. 1983; 4:136–48.

Canelas AB, van Gulik WM, Heijnen JJ. Determination of the cytosolic free NAD/NADH ratio in Saccharomyces cerevisiae under steady-state and highly dynamic conditions. Biotechnol Bioeng. 2008; 100(4):734–43.

Nikerel IE, Canelas AB, Jol SJ, Verheijen PJT, Heijnen JJ. Construction of kinetic models for metabolic reaction networks: Lessons learned in analysing short-term stimulus response data. Math Comput Model Dyn Syst. 2011; 17(3):243–60.

Llaneras F, Picó J. Stoichiometric modelling of cell metabolism. J Biosci Bioeng. 2008; 105(1):1–11.

Bro R. Multiway calibration. Multilinear PLS. J Chemom. 1998; 10(1):47–61.

Westerhuis JA, Hoefsloot HCJ, Smit S, Vis DJ, Smilde AK, Velzen EJJv, Duijnhoven JPMv, Dorsten FAv. Assessment of PLSDA cross validation. Metabolomics. 2008; 4(1):81–9.

Szymańska E, Saccenti E, Smilde AK, Westerhuis JA. Double-check: validation of diagnostic statistics for PLS-DA models in metabolomics studies. Metabolomics. 2012; 8(Suppl 1):3–16.

Rodrigues F, Ludovico P, Leão C. Sugar Metabolism in Yeasts: an Overview of Aerobic and Anaerobic Glucose Catabolism. In: Biodiversity and Ecophysiology of Yeasts. The Yeast Handbook. Berlin: Springer: 2006. p. 101–21.

Larsson K, Ansell R, Eriksson P, Adler L. A gene encoding sn-glycerol 3-phosphate dehydrogenase (NAD+) complements an osmosensitive mutant of Saccharomyces cerevisiae. Mol Microbiol. 1993; 10(5):1101–11.

Eriksson P, André L, Ansell R, Blomberg A, Adler L. Cloning and characterization of GPD2, a second gene encoding sn-glycerol 3-phosphate dehydrogenase (NAD+) in Saccharomyces cerevisiae, and its comparison with GPD1. Mol Microbiol. 1995; 17(1):95–107.

Norbeck J, Pâhlman AK, Akhtar N, Blomberg A, Adler L. Purification and characterization of two isoenzymes of DL-glycerol-3-phosphatase from Saccharomyces cerevisiae. Identification of the corresponding GPP1 and GPP2 genes and evidence for osmotic regulation of Gpp2p expression by the osmosensing mitogen-activated protein kinase signal transduction pathway. J Biol Chem. 1996; 271(23):13875–81.

[-]

recommendations

 

This item appears in the following Collection(s)

Show full item record