Optimal Control of Probabilistic Boolean Networks: An Information-Theoretic Approach

The primary challenge with biological sciences is to control gene regulatory networks (GRNs), thereby creating therapeutic intervention methods that alter network dynamics in the desired manner. The optimal control of GRNs with probabilistic Boolean control networks (PBCNs) as the underlying structure is a solution to this challenge. Owing to the exponential growth in network size with the increase in the number of genes, we need an optimal control approach that scales to large systems without imposing any limitations on network dynamics. Furthermore, we are encouraged to use the graphics processing unit (GPU) to reduce time complexity utilizing the easily available and enhanced computational resources. The optimal control of PBCNs in the Markovian framework is developed in this paper employing an information-theoretic approach which includes Kullback-Leibler (KL) divergence. We convert the nonlinear optimal control problem of PBCN to a linear problem by using the exponential transformation of the cost function, also known as the desirability function. The linear formulation enables us to compute an optimal control using the path integral (PI) method. Furthermore, we offer sampling-based methodologies for approximating PI and therefore optimizing PBCN control. The sampling-based method can be implemented in parallel, which solves the optimal control problem for large PBCNs.

Automated summary

The primary challenge in biological sciences is to control gene regulatory networks (GRNs) to create therapeutic intervention methods. Optimal control of GRNs using probabilistic Boolean control networks (PBCNs) addresses this challenge by scaling to large systems without limiting network dynamics. Utilizing GPU and information-theoretic approach, the study developed optimal control for PBCNs in the Markovian framework, with a transformation to a linear problem for computation of optimal control using the path integral (PI) method and sampling-based methodologies for approximation and optimization.