Campuses:

cell polarization

Tuesday, May 29, 2018 - 10:00am - 10:50am
Ching-Shan Chou (The Ohio State University)
Mathematical models in systems biology often have many parameters, such as biochemical reaction rates, whose true values are unknown. When the number of parameters is large, it becomes computationally difficult to analyze their effects and to estimate parameter values from experimental data. This is especially challenging when the model is expensive to evaluate, as is the case for large spatial models. In this work, we introduce a methodology for using surrogate models to drastically reduce the cost of parameter analysis in such models.
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