Dr. Christopher Mullaley

Chris’s thesis examines the way in which mathematical and computational modeling can be used to advance the understanding of biophysical mechanisms in the mammalian inner ear. It shows how experimental results from a variety of sources can be integrated to describe electrical current flows within the cochlea. Chris developed a software framework which allows the construction of complex, multi variable models from descriptions of the components. By taking advantage of the geometry of the cochlea, these descriptions can be simplified and the number of free parameters in the models reduced. The software framework handles the parsing of these descriptions, manipulation of the parameters through a simple graphical user interface (GUI) and interfacing to the MatLab programming environment for numerical simulation.


         He developed static models where the cochlea is represented as a network of resistive elements and voltage sources. From these models, estimates of the space constants for the organ of Corti were deduced and the consequences of blocking gap junctions were studied. He predicted that, in terms of short term effects on endocochlear potential, selectively blocking the gap junctions in the stria vascularis is likely to have a much greater effect than blocking those in the organ of Corti.


         Chris also developed dynamic models, incorporating a biophysical description of the travelling wave within the cochlea with appropriate phase relationships. The simulations indicate how the interaction of out-of-phase current flows in adjacent regions of the cochlea may help to maintain active amplification at high frequencies.
Finally, Chris modeled the electrophysiology of the stria vascularis in greater detail as a multi-layered structure with different ion channels and pumps in each different layer. He compared the behaviour of this system with that of the simple resistance and voltage representation used in the other models.

Chris is currently part of the Development Team Manager at Experian QAS

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