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Friday, March 13, 2009

Aria Shahingohar, UWO – Parameter estimation of Bergman's minimal model of insulin sensitivity using Genetic Algorithm.

Abnormal insulin production can lead to serious problems. Goal is to enhance the estimation of insulin sensitivity. Glucose is injected into blood at time zero, insulin is injected shortly after. Bergman has a model that describes the curves produced in this experiment.

Equations given for:
Change in plasma glucose over time = ......
Rate of insulin removal....

There are 8 parameters in this model which vary from person to person. The model is a closed loop system, and requires the partitioning of the subsystems [?] Requires good signal to noise ratio.

Use a genetic algorithm to optimize the 8 parameters.

Tested different methods: Genetic algorithms and Simplex method. Also tested various methods of optimization using subsets of information.

Used a maximum of 1000 generations in Genetic Algorithm. Population size 20-40, depending on expt. Each method tested 50 times (stochastic) to measure error for each parameter separately.

Results: GA was always better, and partitioning subsystem works better than trying to estimate all parameters at once.

Conclusion: Genetic algorithm significantly lowers error, and parameters can be estimated with only glucose and insulin measurements.

[My Comments: This was an interesting project which clearly has real world impacts. Although much of it wasn't particularly well explained, leaving the audience to pick out out the meaning. Very nice presentation, and cool concept. It would be nice to see more information on other algorithms.... ]

An audience member has asked about saturation. That's another interesting topic that wasn't covered.

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