Effect of coarse-scale modeling on control outcome of genetic regulatory networks

Fine-scale models represented by stochastic master equations can provide a very accurate description of the real genetic regulatory system but inadequate time series data and technological limitations on cell specific measurements in cancer related experiments prevent the accurate inference of the parameters of such a fine-scale model. Furthermore, the computational complexity involved in the design of optimal intervention strategies to favorably effect system dynamics for such detailed models is enormous. Thus, it is imperative to study the effect of intervention policies designed using coarse-scale models when applied to the fine-scale models. In this paper, we map a fine-scale model represented by a Stochastic Master Equation to a coarse-scale model represented by a Probabilistic Boolean Network and derive bounds on the performance of the intervention strategy designed using the coarse scale model when applied to the fine-scale model.