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Items: 1 to 20 of 29

1.

Sampling methods for exploring between-subject variability in cardiac electrophysiology experiments.

Drovandi CC, Cusimano N, Psaltis S, Lawson BA, Pettitt AN, Burrage P, Burrage K.

J R Soc Interface. 2016 Aug;13(121). pii: 20160214. doi: 10.1098/rsif.2016.0214.

2.

Rabbit-specific computational modelling of ventricular cell electrophysiology: Using populations of models to explore variability in the response to ischemia.

Gemmell P, Burrage K, Rodríguez B, Quinn TA.

Prog Biophys Mol Biol. 2016 Jul;121(2):169-84. doi: 10.1016/j.pbiomolbio.2016.06.003. Epub 2016 Jun 16.

3.

Experimentally-Based Computational Investigation into Beat-To-Beat Variability in Ventricular Repolarization and Its Response to Ionic Current Inhibition.

Pueyo E, Dangerfield CE, Britton OJ, Virág L, Kistamás K, Szentandrássy N, Jost N, Varró A, Nánási PP, Burrage K, Rodríguez B.

PLoS One. 2016 Mar 28;11(3):e0151461. doi: 10.1371/journal.pone.0151461. eCollection 2016.

4.

Variability in cardiac electrophysiology: Using experimentally-calibrated populations of models to move beyond the single virtual physiological human paradigm.

Muszkiewicz A, Britton OJ, Gemmell P, Passini E, Sánchez C, Zhou X, Carusi A, Quinn TA, Burrage K, Bueno-Orovio A, Rodriguez B.

Prog Biophys Mol Biol. 2016 Jan;120(1-3):115-27. doi: 10.1016/j.pbiomolbio.2015.12.002. Epub 2015 Dec 14.

5.

On the Order of the Fractional Laplacian in Determining the Spatio-Temporal Evolution of a Space-Fractional Model of Cardiac Electrophysiology.

Cusimano N, Bueno-Orovio A, Turner I, Burrage K.

PLoS One. 2015 Dec 2;10(12):e0143938. doi: 10.1371/journal.pone.0143938. eCollection 2015.

6.

In Vivo and In Silico Investigation Into Mechanisms of Frequency Dependence of Repolarization Alternans in Human Ventricular Cardiomyocytes.

Zhou X, Bueno-Orovio A, Orini M, Hanson B, Hayward M, Taggart P, Lambiase PD, Burrage K, Rodriguez B.

Circ Res. 2016 Jan 22;118(2):266-78. doi: 10.1161/CIRCRESAHA.115.307836. Epub 2015 Nov 24.

7.

Stochastic simulation in systems biology.

Székely T Jr, Burrage K.

Comput Struct Biotechnol J. 2014 Oct 30;12(20-21):14-25. doi: 10.1016/j.csbj.2014.10.003. eCollection 2014 Nov. Review.

8.

Application of stochastic phenomenological modelling to cell-to-cell and beat-to-beat electrophysiological variability in cardiac tissue.

Walmsley J, Mirams GR, Pitt-Francis J, Rodriguez B, Burrage K.

J Theor Biol. 2015 Jan 21;365:325-36. doi: 10.1016/j.jtbi.2014.10.029. Epub 2014 Nov 4.

9.

Stochastic dynamics of interacting haematopoietic stem cell niche lineages.

Székely T Jr, Burrage K, Mangel M, Bonsall MB.

PLoS Comput Biol. 2014 Sep 4;10(9):e1003794. doi: 10.1371/journal.pcbi.1003794. eCollection 2014 Sep.

10.

Efficient simulation of stochastic chemical kinetics with the Stochastic Bulirsch-Stoer extrapolation method.

Székely T Jr, Burrage K, Zygalakis KC, Barrio M.

BMC Syst Biol. 2014 Jun 18;8:71. doi: 10.1186/1752-0509-8-71.

11.

Fractional diffusion models of cardiac electrical propagation: role of structural heterogeneity in dispersion of repolarization.

Bueno-Orovio A, Kay D, Grau V, Rodriguez B, Burrage K.

J R Soc Interface. 2014 Aug 6;11(97):20140352. doi: 10.1098/rsif.2014.0352.

12.

Quantitative study of the effect of tissue microstructure on contraction in a computational model of rat left ventricle.

Carapella V, Bordas R, Pathmanathan P, Lohezic M, Schneider JE, Kohl P, Burrage K, Grau V.

PLoS One. 2014 Apr 2;9(4):e92792. doi: 10.1371/journal.pone.0092792. eCollection 2014.

13.

Population of computational rabbit-specific ventricular action potential models for investigating sources of variability in cellular repolarisation.

Gemmell P, Burrage K, Rodriguez B, Quinn TA.

PLoS One. 2014 Feb 28;9(2):e90112. doi: 10.1371/journal.pone.0090112. eCollection 2014.

14.

mRNA expression levels in failing human hearts predict cellular electrophysiological remodeling: a population-based simulation study.

Walmsley J, Rodriguez JF, Mirams GR, Burrage K, Efimov IR, Rodriguez B.

PLoS One. 2013;8(2):e56359. doi: 10.1371/journal.pone.0056359. Epub 2013 Feb 20.

15.

Inferring diffusion in single live cells at the single-molecule level.

Robson A, Burrage K, Leake MC.

Philos Trans R Soc Lond B Biol Sci. 2012 Dec 24;368(1611):20120029. doi: 10.1098/rstb.2012.0029. Print 2013 Feb 5. Review.

16.

A higher-order numerical framework for stochastic simulation of chemical reaction systems.

Székely T Jr, Burrage K, Erban R, Zygalakis KC.

BMC Syst Biol. 2012 Jul 15;6:85. doi: 10.1186/1752-0509-6-85.

17.

A multiscale investigation of repolarization variability and its role in cardiac arrhythmogenesis.

Pueyo E, Corrias A, Virág L, Jost N, Szél T, Varró A, Szentandrássy N, Nánási PP, Burrage K, Rodríguez B.

Biophys J. 2011 Dec 21;101(12):2892-902. doi: 10.1016/j.bpj.2011.09.060. Epub 2011 Dec 20.

18.

Determination of somatic and cancer stem cell self-renewing symmetric division rate using sphere assays.

Deleyrolle LP, Ericksson G, Morrison BJ, Lopez JA, Burrage K, Burrage P, Vescovi A, Rietze RL, Reynolds BA.

PLoS One. 2011 Jan 5;6(1):e15844. doi: 10.1371/journal.pone.0015844.

19.

Simulation methods with extended stability for stiff biochemical Kinetics.

Rué P, Villà-Freixa J, Burrage K.

BMC Syst Biol. 2010 Aug 11;4:110. doi: 10.1186/1752-0509-4-110.

20.

Probability distributed time delays: integrating spatial effects into temporal models.

Marquez-Lago TT, Leier A, Burrage K.

BMC Syst Biol. 2010 Mar 4;4:19. doi: 10.1186/1752-0509-4-19.

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