Probabilistic Modeling Of Multi-Level Genetic Regulatory Logic

We propose a new class of models for genetic regulatory interactions that extends prior work by Bulashevska and Eils. Our model uses a probabilistic multi-level logic semantic framework to account for noisy biological processes and observed data. In particular, the expression levels of genes take more than two values and the evolution of the genetic regulatory network follows a hidden Markov model. We illustrate our model building procedure using an 'OR' function.