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SUMMARY:Nonlocal methods for modelling the electrophysiology of the human 
 heart
DTSTART:20130529T161500
DTEND:20130529T180000
DTSTAMP:20260408T134504Z
UID:cd31284a23aa4c89a94137281fdac361ea32aa975709967b982cc0b4
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Kevin Burrage\, University of Oxford and Queensland Univ
 ersity of Technology\nBio: I am a Federation Fellow of the Australian Rese
 arch Council (2003-2008). Until the end of 2007 I was also Professor of Co
 mputational Mathematics at the University of Queensland and Director of th
 e Advanced  Computational Modelling Centre.  I was also founding CEO of 
 the Queensland Parallel Supercomputing  foundation (now QCIF). In Austral
 ia I am now Professor of Computational Mathematics at the Queensland Unive
 rsity of Technology and Adjunct Professor at the Institute for Molecular B
 ioscience at the University for Queensland.\nI joined Oxford University in
  early 2008 and am now Professor of Computational Systems Biology at the D
 epartment of Computer Science\, University of Oxford and the Oxford Centre
  for Integrative Systems Biology. I am also a supernumerary fellow of New 
 College at Oxford University.\nI share my time between Brisbane and Oxford
 . I am married to Pamela and we have two children at Queensland doing the 
 Medical program.\nThe bidomain model has been a widely used mathematical f
 ramework (based on reaction - diffusion equations) for modelling the propa
 gation of an electrical signal in cardiac tissue for nearly 40 years. It h
 as been proven to be successful in many situations yet it is based on cert
 ain assumptions that may not hold. In this presentation we discuss these i
 ssues and propose a novel non - standard approach to modelling this electr
 ical propagation based on nonlocal models. This model is less intuitive th
 an the standard approach\, but it seems to explain some aspects that are b
 eyond the standard approach. We also discuss issues on the nature of model
 s\, how models are validated\, and whether a good model has to be biophysi
 cally detailed or phenomenological in nature.
LOCATION:CH B3 31
STATUS:CONFIRMED
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