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Dr Gary Chaffey

Visiting Research Fellow

Academic and research departments

Department of Mathematics.

My publications


Chaffey Gary, Lloyd David, Skeldon Anne, Kirkby Norman (2014) The effect of the G1-S transition checkpoint on an age structured cell cycle model.,PLoS One9(1)e83477 Public Library of Science
Knowledge of how a population of cancerous cells progress through the cell cycle is vital if the population is to be treated effectively, as treatment outcome is dependent on the phase distributions of the population. Estimates on the phase distribution may be obtained experimentally however the errors present in these estimates may effect treatment efficacy and planning. If mathematical models are to be used to make accurate, quantitative predictions concerning treatments, whose efficacy is phase dependent, knowledge of the phase distribution is crucial. In this paper it is shown that two different transition rates at the G1-S checkpoint provide a good fit to a growth curve obtained experimentally. However, the different transition functions predict a different phase distribution for the population, but both lying within the bounds of experimental error. Since treatment outcome is effected by the phase distribution of the population this difference may be critical in treatment planning. Using an age-structured population balance approach the cell cycle is modelled with particular emphasis on the G1-S checkpoint. By considering the probability of cells transitioning at the G1-S checkpoint, different transition functions are obtained. A suitable finite difference scheme for the numerical simulation of the model is derived and shown to be stable. The model is then fitted using the different probability transition functions to experimental data and the effects of the different probability transition functions on the model's results are discussed.
Lyle Jane, Charlton Peter H., Bonet Luz Esther, Chaffey Gary, Christie Mark, Nandi Manasi, Aston Philip (2017) Beyond HRV: Analysis of ECG Signals Using Attractor Reconstruction,Computing in Cardiology 201744 Computing in Cardiology
Attractor reconstruction analysis has previously been applied to analyse arterial blood pressure and photoplethysmogram signals. This study extends this novel technique to ECG signals. We show that the method gives high accuracy in identifying gender from ECG signals, performing significantly better than the same classification by interval measures.