Optimal control of gene regulatory networks with uncertain intervention effects

Optimal control policies for Markovian gene regulatory networks assume that external intervention is 100% specific to control genes. In practice, however, this effect may be unpredictable in the sense that intervention may also target alternative genes. Our goal is to find an optimal control policy that performs well in such cases. We model this by an uncertainty class of controlled networks corresponding to different affected genes, governed by a probability distribution representing our confidence in the intervention specificity to each gene, and optimize relative to this uncertainty class.