Probabilistic Regulatory Networks: Modeling Genetic Networks

Abstract

We describe here the new concept of $\epsilon$-Homomorphisms of Probabilistic Regulatory Gene Networks(PRN). The $\epsilon$-homomorphisms are special mappings between two probabilistic networks, that consider the algebraic action of the iteration of functions and the probabilistic dynamic of the two networks. It is proved here that the class of PRN, together with the homomorphisms, form a category with products and coproducts. Projections are special homomorphisms, induced by invariant subnetworks. Here, it is proved that an $\epsilon$-homomorphism for 0 <$\epsilon$< 1 produces simultaneous Markov Chains in both networks, that permit to introduce the concepts of $\epsilon$-isomorphism of Markov Chains, and similar networks.