Hostname: page-component-586b7cd67f-dsjbd Total loading time: 0 Render date: 2024-11-24T11:27:55.591Z Has data issue: false hasContentIssue false
  • English
  • Français

MATHEMATICAL MODELS FOR QUANTIFYING COLLECTIVE CELL BEHAVIOUR

Part of: Parametric inference Equations of mathematical physics and other areas of application Markov processes

Published online by Cambridge University Press:  20 February 2018

STUART T. JOHNSTON
STUART T. JOHNSTON*
Affiliation:
School of Engineering, University of Melbourne, Parkville, Victoria 3010, Australia email [email protected]
Rights & Permissions [Opens in a new window]

Abstract

An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'

Keywords

approximate Bayesian computationcollective cell behaviourmathematical modelcell migrationcell proliferationrandom walkwound healingscratch assay

MSC classification

Primary: 60J70: Applications of Brownian motions and diffusion theory (population genetics, absorption problems, etc.)
Secondary: 35Q92: PDEs in connection with biology and other natural sciences 62F15: Bayesian inference
Type
Abstracts of Australasian PhD Theses
Information
Bulletin of the Australian Mathematical Society , Volume 97 , Issue 2 , April 2018 , pp. 349 - 352
Copyright
© 2018 Australian Mathematical Publishing Association Inc. 

References

Johnston, S. T., Baker, R. E., Sean McElwain, D. L. and Simpson, M. J., ‘Co-operation, competition and crowding: a discrete framework linking Allee kinetics, nonlinear diffusion, shocks and sharp-fronted travelling waves’, Sci. Rep. 7 (2017), 42134.CrossRefGoogle ScholarPubMed
Johnston, S. T., Baker, R. E. and Simpson, M. J., ‘Filling the gaps: a robust description of adhesive birth–death–movement processes’, Phys. Rev. E 93(4) (2016), 042413.CrossRefGoogle ScholarPubMed
Johnston, S. T., Baker, R. E. and Simpson, M. J., ‘A new and accurate continuum description of moving fronts’, New J. Phys. 19(3) (2017), 033010.CrossRefGoogle Scholar
Johnston, S. T., Ross, J. V., Binder, B. J., Sean McElwain, D. L., Haridas, P. and Simpson, M. J., ‘Quantifying the effect of experimental design choices for in vitro scratch assays’, J. Theoret. Biol. 400 (2016), 19–31.CrossRefGoogle ScholarPubMed
Johnston, S. T., Shah, E. T., Chopin, L. K., Sean McElwain, D. L and Simpson, M. J., ‘Estimating cell diffusivity and cell proliferation rate by interpreting IncuCyte ZOOMTM assay data using the Fisher–Kolmogorov model’, BMC Syst. Biol. 9(38) (2015), 12 pages.CrossRefGoogle Scholar
Johnston, S. T., Simpson, M. J. and Baker, R. E., ‘Modelling the movement of interacting cell populations: a moment dynamics approach’, J. Theoret. Biol. 370 (2015), 81–92.CrossRefGoogle ScholarPubMed
Johnston, S. T., Simpson, M. J. and Sean McElwain, D. L., ‘How much information can be obtained from tracking the position of the leading edge in a scratch assay?’, J. R. Soc. Interface 11(97) (2014), 20140325.CrossRefGoogle Scholar
Johnston, S. T., Simpson, M. J., Sean McElwain, D. L., Binder, B. J. and Ross, J. V., ‘Interpreting scratch assays using pair density dynamics and approximate Bayesian computation’, Open Biol. 4(9) (2014), 140097.CrossRefGoogle ScholarPubMed