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Improvement of estimation accuracy in parameter optimization by symbolic computation

We evaluate the ability of our parameter optimization method that was newly developed by using differential elimination, to estimate kinetic parameter values with a high degree of accuracy. For this purpose, we performed a simulation study by using the objective function with and without the new constraints by differential elimination: parameters in a model of linear equations, under the assumption that only one molecule in the model can be monitored with and without the noise, was estimated by using genetic algorithm (GA). In particular, the ability was tested for the simulation data with and without noise. As a result, the introduction of new constraints was dramatically effective: the GA with new constraints could estimate successfully parameter values in the simulated model against the noisy data, with high degree of accuracy, in comparison with the degree by conventional GA without the constraints.