Designing experiments for optimal reduction of uncertainty in gene regulatory networks

One of the main issues in systems biology is limited resources for conducting biological experiments. Therefore, a strategy for prioritizing the experiments seems to be inevitable. Experimental design is the process of planning experiments in such a way to make experiments as informative as possible. In this work, we propose a novel strategy for designing effective experiments that can optimally reduce the uncertainty in gene regulatory networks, based on the concept of mean objective cost of uncertainty (MOCU).