Optimal intervention in semi-Markov-based asynchronous genetic regulatory networks

Probabilistic Boolean networks are a class of rule-based models for gene regulatory networks. This class of models is used to design optimal therapeutic intervention strategies. While synchronous probabilistic Boolean networks have been investigated in detail in the literature, no similar endeavor has been completed for asynchronous networks. This paper addresses this issue by introducing an asynchronous extension to probabilistic Boolean networks and by developing intervention methods based on this new model. The proposed framework introduces asynchronism at the level of aggregated genes status. The theory of semi-Markov decision processes is then used to devise effective intervention methods where the objective is to reduce the time duration that the system spends in undesirable states. The necessary timing information for the proposed model can be obtained from sequences of gene-activity profile measurements. This is one of the major advantages of the propose approach.