Selection Policy-Induced Reduction Mappings for Boolean Networks

Developing computational models paves the way to understanding, predicting, and influencing the long-term behavior of genomic regulatory systems. However, several major challenges have to be addressed before such models are successfully applied in practice. Their inherent high complexity requires strategies for complexity reduction. Reducing the complexity of the model by removing genes and interpreting them as latent variables leads to the problem of selecting which states and their corresponding transitions best account for the presence of such latent variables. We use the Boolean network (BN) model to develop the general framework for selection and reduction of the model's complexity via designating some of the model's variables as latent ones. We also study the effects of the selection policies on the steady-state distribution and the controllability of the model.