Bayesian robustness in the control of gene regulatory networks

The presence of noise and the availability of a limited number of samples prevent the transition probabilities of a gene regulatory network from being accurately estimated. Thus, it is important to study the effect of modeling errors on the final outcome of an intervention strategy and to design robust intervention strategies. Two major approaches applied to the design of robust policies in general are the min-max (worst case) approach and the Bayesian approach. The min-max control approach is at times conservative because it gives too much importance to the scenarios which hardly occur in practice. Consequently, in this paper, we focus on the Bayesian approach for the control of gene regulatory networks.