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Items: 1 to 50 of 96

1.

Mathematical modelling indicates that lower activity of the haemostatic system in neonates is primarily due to lower prothrombin concentration.

Siekmann I, Bjelosevic S, Landman K, Monagle P, Ignjatovic V, Crampin EJ.

Sci Rep. 2019 Mar 8;9(1):3936. doi: 10.1038/s41598-019-40435-7.

2.

Insights on the impact of mitochondrial organisation on bioenergetics in high-resolution computational models of cardiac cell architecture.

Ghosh S, Tran K, Delbridge LMD, Hickey AJR, Hanssen E, Crampin EJ, Rajagopal V.

PLoS Comput Biol. 2018 Dec 5;14(12):e1006640. doi: 10.1371/journal.pcbi.1006640. eCollection 2018 Dec.

3.

Bond Graph Representation of Chemical Reaction Networks.

Gawthrop P, Crampin EJ.

IEEE Trans Nanobioscience. 2018 Oct;17(4):449-455. doi: 10.1109/TNB.2018.2876391. Epub 2018 Oct 16.

PMID:
30334803
4.

A thermodynamic framework for modelling membrane transporters.

Pan M, Gawthrop PJ, Tran K, Cursons J, Crampin EJ.

J Theor Biol. 2018 Sep 28. pii: S0022-5193(18)30470-3. doi: 10.1016/j.jtbi.2018.09.034. [Epub ahead of print]

PMID:
30273576
5.

Minimum information reporting in bio-nano experimental literature.

Faria M, Björnmalm M, Thurecht KJ, Kent SJ, Parton RG, Kavallaris M, Johnston APR, Gooding JJ, Corrie SR, Boyd BJ, Thordarson P, Whittaker AK, Stevens MM, Prestidge CA, Porter CJH, Parak WJ, Davis TP, Crampin EJ, Caruso F.

Nat Nanotechnol. 2018 Sep;13(9):777-785. doi: 10.1038/s41565-018-0246-4. Epub 2018 Sep 6. Review.

6.

An analytical approach for quantifying the influence of nanoparticle polydispersity on cellular delivered dose.

Johnston ST, Faria M, Crampin EJ.

J R Soc Interface. 2018 Jul;15(144). pii: 20180364. doi: 10.1098/rsif.2018.0364.

7.

Combinatorial Targeting by MicroRNAs Co-ordinates Post-transcriptional Control of EMT.

Cursons J, Pillman KA, Scheer KG, Gregory PA, Foroutan M, Hediyeh-Zadeh S, Toubia J, Crampin EJ, Goodall GJ, Bracken CP, Davis MJ.

Cell Syst. 2018 Jul 25;7(1):77-91.e7. doi: 10.1016/j.cels.2018.05.019. Epub 2018 Jul 11.

8.

Bond graph modelling of the cardiac action potential: implications for drift and non-unique steady states.

Pan M, Gawthrop PJ, Tran K, Cursons J, Crampin EJ.

Proc Math Phys Eng Sci. 2018 Jun;474(2214):20180106. doi: 10.1098/rspa.2018.0106. Epub 2018 Jun 27.

PMID:
29977132
9.

DiSNE Movie Visualization and Assessment of Clonal Kinetics Reveal Multiple Trajectories of Dendritic Cell Development.

Lin DS, Kan A, Gao J, Crampin EJ, Hodgkin PD, Naik SH.

Cell Rep. 2018 Mar 6;22(10):2557-2566. doi: 10.1016/j.celrep.2018.02.046.

10.

A computational study of the role of mitochondrial organization on cardiac bioenergetics.

Ghosh S, Crampin EJ, Hanssen E, Rajagopal V.

Conf Proc IEEE Eng Med Biol Soc. 2017 Jul;2017:2696-2699. doi: 10.1109/EMBC.2017.8037413.

PMID:
29060455
11.

Experimental and modelling evidence of shortening heat in cardiac muscle.

Tran K, Han JC, Crampin EJ, Taberner AJ, Loiselle DS.

J Physiol. 2017 Oct 1;595(19):6313-6326. doi: 10.1113/JP274680. Epub 2017 Aug 22.

12.

Energy-based analysis of biomolecular pathways.

Gawthrop PJ, Crampin EJ.

Proc Math Phys Eng Sci. 2017 Jun;473(2202):20160825. doi: 10.1098/rspa.2016.0825. Epub 2017 Jun 21.

13.

Changes in mitochondrial morphology and organization can enhance energy supply from mitochondrial oxidative phosphorylation in diabetic cardiomyopathy.

Jarosz J, Ghosh S, Delbridge LM, Petzer A, Hickey AJ, Crampin EJ, Hanssen E, Rajagopal V.

Am J Physiol Cell Physiol. 2017 Feb 1;312(2):C190-C197. doi: 10.1152/ajpcell.00298.2016. Epub 2016 Nov 30.

14.

Systems analysis identifies miR-29b regulation of invasiveness in melanoma.

Andrews MC, Cursons J, Hurley DG, Anaka M, Cebon JS, Behren A, Crampin EJ.

Mol Cancer. 2016 Nov 16;15(1):72.

15.
16.

Modular bond-graph modelling and analysis of biomolecular systems.

Gawthrop PJ, Crampin EJ.

IET Syst Biol. 2016 Oct;10(5):187-201. doi: 10.1049/iet-syb.2015.0083.

PMID:
27762233
17.

Modelling modal gating of ion channels with hierarchical Markov models.

Siekmann I, Fackrell M, Crampin EJ, Taylor P.

Proc Math Phys Eng Sci. 2016 Aug;472(2192):20160122.

18.

Information theoretic approaches for inference of biological networks from continuous-valued data.

Budden DM, Crampin EJ.

BMC Syst Biol. 2016 Sep 6;10(1):89. doi: 10.1186/s12918-016-0331-y.

19.

Myocardial energetics is not compromised during compensated hypertrophy in the Dahl salt-sensitive rat model of hypertension.

Tran K, Han JC, Taberner AJ, Barrett CJ, Crampin EJ, Loiselle DS.

Am J Physiol Heart Circ Physiol. 2016 Sep 1;311(3):H563-71. doi: 10.1152/ajpheart.00396.2016. Epub 2016 Jul 8.

20.

A Framework to Account for Sedimentation and Diffusion in Particle-Cell Interactions.

Cui J, Faria M, Björnmalm M, Ju Y, Suma T, Gunawan ST, Richardson JJ, Heidari H, Bals S, Crampin EJ, Caruso F.

Langmuir. 2016 Nov 29;32(47):12394-12402. Epub 2016 Jul 6.

PMID:
27384770
21.

Semantics-Based Composition of Integrated Cardiomyocyte Models Motivated by Real-World Use Cases.

Neal ML, Carlson BE, Thompson CT, James RC, Kim KG, Tran K, Crampin EJ, Cook DL, Gennari JH.

PLoS One. 2015 Dec 30;10(12):e0145621. doi: 10.1371/journal.pone.0145621. eCollection 2015.

22.

Spatially transformed fluorescence image data for ERK-MAPK and selected proteins within human epidermis.

Cursons J, Angel CE, Hurley DG, Print CG, Dunbar PR, Jacobs MD, Crampin EJ.

Gigascience. 2015 Dec 14;4:63. doi: 10.1186/s13742-015-0102-5. eCollection 2015.

23.

Network analysis of an in vitro model of androgen-resistance in prostate cancer.

Detchokul S, Elangovan A, Crampin EJ, Davis MJ, Frauman AG.

BMC Cancer. 2015 Nov 10;15:883. doi: 10.1186/s12885-015-1884-7.

24.

Examination of the Effects of Heterogeneous Organization of RyR Clusters, Myofibrils and Mitochondria on Ca2+ Release Patterns in Cardiomyocytes.

Rajagopal V, Bass G, Walker CG, Crossman DJ, Petzer A, Hickey A, Siekmann I, Hoshijima M, Ellisman MH, Crampin EJ, Soeller C.

PLoS Comput Biol. 2015 Sep 3;11(9):e1004417. doi: 10.1371/journal.pcbi.1004417. eCollection 2015 Sep.

25.

Regulation of cardiac cellular bioenergetics: mechanisms and consequences.

Tran K, Loiselle DS, Crampin EJ.

Physiol Rep. 2015 Jul;3(7). pii: e12464. doi: 10.14814/phy2.12464.

26.

Regulation of ERK-MAPK signaling in human epidermis.

Cursons J, Gao J, Hurley DG, Print CG, Dunbar PR, Jacobs MD, Crampin EJ.

BMC Syst Biol. 2015 Jul 25;9:41. doi: 10.1186/s12918-015-0187-6.

27.

Modelling the conditional regulatory activity of methylated and bivalent promoters.

Budden DM, Hurley DG, Crampin EJ.

Epigenetics Chromatin. 2015 Jun 19;8:21. doi: 10.1186/s13072-015-0013-9. eCollection 2015.

28.

Stimulus-dependent differences in signalling regulate epithelial-mesenchymal plasticity and change the effects of drugs in breast cancer cell lines.

Cursons J, Leuchowius KJ, Waltham M, Tomaskovic-Crook E, Foroutan M, Bracken CP, Redfern A, Crampin EJ, Street I, Davis MJ, Thompson EW.

Cell Commun Signal. 2015 May 15;13:26. doi: 10.1186/s12964-015-0106-x.

29.

NAIL, a software toolset for inferring, analyzing and visualizing regulatory networks.

Hurley DG, Cursons J, Kan Wang Y, Budden DM, Print CG, Crampin EJ.

Bioinformatics. 2015 Jul 1;31(13):2015-240. doi: 10.1093/bioinformatics/btv095. Epub 2015 May 5. No abstract available.

PMID:
25948714
30.

Predicting expression: the complementary power of histone modification and transcription factor binding data.

Budden DM, Hurley DG, Cursons J, Markham JF, Davis MJ, Crampin EJ.

Epigenetics Chromatin. 2014 Nov 24;7(1):36. doi: 10.1186/1756-8935-7-36. eCollection 2014.

31.

Virtual Reference Environments: a simple way to make research reproducible.

Hurley DG, Budden DM, Crampin EJ.

Brief Bioinform. 2015 Sep;16(5):901-3. doi: 10.1093/bib/bbu043. Epub 2014 Nov 28.

32.

Energy-based analysis of biochemical cycles using bond graphs.

Gawthrop PJ, Crampin EJ.

Proc Math Phys Eng Sci. 2014 Nov 8;470(2171):20140459.

33.

NAIL, a software toolset for inferring, analyzing and visualizing regulatory networks.

Hurley DG, Cursons J, Wang YK, Budden DM, Print CG, Crampin EJ.

Bioinformatics. 2015 Jan 15;31(2):277-8. doi: 10.1093/bioinformatics/btu612. Epub 2014 Sep 21. Erratum in: Bioinformatics. 2015 Jul 1;31(13):2015-240.

PMID:
25246431
34.

Predictive modelling of gene expression from transcriptional regulatory elements.

Budden DM, Hurley DG, Crampin EJ.

Brief Bioinform. 2015 Jul;16(4):616-28. doi: 10.1093/bib/bbu034. Epub 2014 Sep 16.

PMID:
25231769
35.

Reply to response to 'What do aquaporin knockout studies tell us about fluid transport in epithelia?' Maclaren OJ, Sneyd J, Crampin EJ (2013) J Membr Biol 246:297-305.

Maclaren OJ, Sneyd J, Crampin EJ.

J Membr Biol. 2014 Mar;247(3):289-90. doi: 10.1007/s00232-013-9628-6. Epub 2014 Jan 9. No abstract available.

PMID:
24402243
36.

Integration of steady-state and temporal gene expression data for the inference of gene regulatory networks.

Wang YK, Hurley DG, Schnell S, Print CG, Crampin EJ.

PLoS One. 2013 Aug 14;8(8):e72103. doi: 10.1371/journal.pone.0072103. eCollection 2013.

37.

What do aquaporin knockout studies tell us about fluid transport in epithelia?

Maclaren OJ, Sneyd J, Crampin EJ.

J Membr Biol. 2013 Apr;246(4):297-305. doi: 10.1007/s00232-013-9530-2. Epub 2013 Feb 22.

38.

Biclustering reveals breast cancer tumour subgroups with common clinical features and improves prediction of disease recurrence.

Wang YK, Print CG, Crampin EJ.

BMC Genomics. 2013 Feb 13;14:102. doi: 10.1186/1471-2164-14-102.

39.

MCMC can detect nonidentifiable models.

Siekmann I, Sneyd J, Crampin EJ.

Biophys J. 2012 Dec 5;103(11):2275-86. doi: 10.1016/j.bpj.2012.10.024.

40.

Multilayer perceptron classification of unknown volatile chemicals from the firing rates of insect olfactory sensory neurons and its application to biosensor design.

Bachtiar LR, Unsworth CP, Newcomb RD, Crampin EJ.

Neural Comput. 2013 Jan;25(1):259-87. doi: 10.1162/NECO_a_00386. Epub 2012 Sep 28.

PMID:
23020109
41.

A kinetic model for type I and II IP3R accounting for mode changes.

Siekmann I, Wagner LE 2nd, Yule D, Crampin EJ, Sneyd J.

Biophys J. 2012 Aug 22;103(4):658-68. doi: 10.1016/j.bpj.2012.07.016.

42.

A quantitative analysis of electrolyte exchange in the salivary duct.

Patterson K, Catalán MA, Melvin JE, Yule DI, Crampin EJ, Sneyd J.

Am J Physiol Gastrointest Liver Physiol. 2012 Nov 15;303(10):G1153-63. doi: 10.1152/ajpgi.00364.2011. Epub 2012 Aug 16.

43.

Comparison of the Gibbs and Suga formulations of cardiac energetics: the demise of "isoefficiency".

Han JC, Taberner AJ, Tran K, Goo S, Nickerson DP, Nash MP, Nielsen PM, Crampin EJ, Loiselle DS.

J Appl Physiol (1985). 2012 Oct;113(7):996-1003. doi: 10.1152/japplphysiol.00693.2011. Epub 2012 Aug 9. Review.

44.

Relating components of pressure-volume area in Suga's formulation of cardiac energetics to components of the stress-time integral.

Han JC, Taberner AJ, Tran K, Nickerson DP, Nash MP, Nielsen PM, Crampin EJ, Loiselle DS.

J Appl Physiol (1985). 2012 Oct;113(7):988-95. doi: 10.1152/japplphysiol.00438.2012. Epub 2012 Jul 26. Review.

45.

Myocardial twitch duration and the dependence of oxygen consumption on pressure-volume area: experiments and modelling.

Han JC, Tran K, Taberner AJ, Nickerson DP, Kirton RS, Nielsen PM, Ward ML, Nash MP, Crampin EJ, Loiselle DS.

J Physiol. 2012 Sep 15;590(18):4603-22. doi: 10.1113/jphysiol.2012.228965. Epub 2012 May 8.

46.

Cell cycle gene networks are associated with melanoma prognosis.

Wang L, Hurley DG, Watkins W, Araki H, Tamada Y, Muthukaruppan A, Ranjard L, Derkac E, Imoto S, Miyano S, Crampin EJ, Print CG.

PLoS One. 2012;7(4):e34247. doi: 10.1371/journal.pone.0034247. Epub 2012 Apr 20.

47.

Modelling the effects of calcium waves and oscillations on saliva secretion.

Palk L, Sneyd J, Patterson K, Shuttleworth TJ, Yule DI, Maclaren O, Crampin EJ.

J Theor Biol. 2012 Jul 21;305:45-53. doi: 10.1016/j.jtbi.2012.04.009. Epub 2012 Apr 14.

48.

Efficiency of primary saliva secretion: an analysis of parameter dependence in dynamic single-cell and acinus models, with application to aquaporin knockout studies.

Maclaren OJ, Sneyd J, Crampin EJ.

J Membr Biol. 2012 Jan;245(1):29-50. doi: 10.1007/s00232-011-9413-3. Epub 2012 Jan 19.

49.

Predicting odorant chemical class from odorant descriptor values with an assembly of multi-layer perceptrons.

Bachtiar LR, Unsworth CP, Newcomb RD, Crampin EJ.

Conf Proc IEEE Eng Med Biol Soc. 2011;2011:2756-9. doi: 10.1109/IEMBS.2011.6090755.

PMID:
22254912
50.

Using artificial neural networks to classify unknown volatile chemicals from the firings of insect olfactory sensory neurons.

Bachtiar LR, Unsworth CP, Newcomb RD, Crampin EJ.

Conf Proc IEEE Eng Med Biol Soc. 2011;2011:2752-5. doi: 10.1109/IEMBS.2011.6090754.

PMID:
22254911

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