The Mathematical Modeling of Electrochemical Blood Glucose SensorsPresented with Steve Blythe
Our work involves the modeling of a basic blood glucose sensor, a device whose function is the measurement of a patient's blood sugar levels. We show how the basic behavior of such systems can be modeled analytically with partial differential equations, and how this quickly requires numerical solutions when more complexity is introduced to species interactions and domain geometry, necessitating the use of numerical solvers like COMSOL Multiphysics. We further demonstrate how such models can be powerful tools in the subsequent development of device algorithms that attempt to maximize the glucose signal from the sensor whilst mitigating undesirable error from interferences in the blood sample. In particular we look at Uric acid as a case study, a species present in human blood, which can generate unwanted background noise in an electrochemical sensor.
Stephen graduated from the University of Edinburgh in 1998 with a BSc (Hons) degree in Mathematics. Stephen joined LifeScan Scotland in 2005 initially helping to build, validate and maintain the statistical analysis and reporting processes for the product stability program within the Quality Assurance department. He later moved into R&D in 2008 when he joined the Modeling & Simulation team as a Modeling Scientist.
Prior to LifeScan Stephen worked for 6 years in the videogames industry as an engine and lead gameplay programmer, developing physics, collision and particle effect engines for next generation game consoles. He helped create several commercially viable titles including a million unit selling hit on the Playstation 2 platform.
Within the Modeling & Simulation team Stephen has developed and maintained a suite of numerical models simulating the dynamics of blood glucose test strips. More recently he has spearheaded the conversion and expansion of these models onto the COMSOL multi-physics environment.