Abstract
Recent developments in experimental methods, such as optogenetic induction and microfluidic culture devices, enable precise time-varying control of gene expression. These tools provide new opportunities for dynamic experiments. However the complexity of these experiments poses a challenge to traditional experimental design. In this work, we propose a method for optimal sample scheduling and use simulations to compare it with uniform sampling schedules. We show that optimal scheduling improves the informativeness of results toward parameter estimation and identifiability. In addition, we show that uniform sampling may impose obstacles for the application of experimental design methods for the selection of dynamic inputs.
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