A probabilistic regulatory network for the human immune system

TL;DR

This paper reviews probabilistic regulatory networks and introduces a homomorphism concept, applying the model to the immune system to analyze its equilibrium states using Markov Chains.

Contribution

It introduces the concept of homomorphisms in probabilistic regulatory networks and applies a Probabilistic Boolean Network model to the human immune system.

Findings

Homomorphisms of PRN are defined and exemplified.

The PRN model is applied to the immune system.

Equilibrium states are analyzed using Markov Chains.

Abstract

In this paper we made a review of some papers about probabilistic regulatory networks (PRN), in particular we introduce our concept of homomorphisms of PRN with an example of projection of a regulatory network to a smaller one. We apply the model PRN (or Probabilistic Boolean Network) to the immune system, the PRN works with two functions. The model called ""The B/T-cells interaction"" is Boolean, so we are really working with a Probabilistic Boolean Network. Using Markov Chains we determine the state of equilibrium of the immune response.