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Employing a computational strategy to translate basic science to personalised application.

Cardiac electrophysiology and cellular signalling continues to be strength of the Oxford cardiovascular science programme. Over the last two decades, major technical breakthroughs in experimental physiology have paved the way for an explosion in the quality, and quantity, of data available for analysis. These developments have underpinned many of the post-genomic era’s advances, through the collection of individual data sets from many species including humans.

In parallel with these experimental developments, advances in computing and numerical techniques have accelerated our capacity to handle large volumes of data, making possible the integration of these data sets through the application of bio-physically based, computational models. These modelling developments are now in a position to potentially provide researchers with a set of tools uniquely capable of analyzing complex cause and effect relationships, and, in so doing, improve our understandings of physiological processes. The heart is now widely acknowledged as the most advanced exemplar of an integrated whole organ model.

An example of this approach that has translated into therapy is the development Ivabradine (Servier, FRANCE: EU approved). This drug has been developed as a consequence of the discovery of the I(f) channel (1980’s in Oxford) and modelling studies. These showed that a blocker of this channel would be a safe agent to slow cardiac rhythm, so proving therapeutically viable in angina by reducing energy demand on the heart.

Specific aims:

  • Linking of Data to Models
  • Creation of species-consistent models of the heart (mouse, rat, pig, human) which capture and couple function (eg intracellular signalling, activation, contraction, neural regulation, haemodynamics)
  • Human and Clinical Translation
  • Identify key parameters that are quantitatively critical for linking across different models in order to develop a generic model that explains common behaviour
  • Training of doctoral students to work at the cross-disciplinary interface, using both in-silico and wet lab tools for cardiovascular research
  • Embedding of post-doctoral researchers in both measurement and modelling laboratories to optimise research through the combined application of measurement and modelling

Our team