Special homomorphisms between Probabilistic Gene Regulatory Networks

Mathematics – Dynamical Systems

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

Scientific paper

In this paper we study finite dynamical systems with $n$ functions acting on the same set $X$, and probabilities assigned to these functions, that it is called Probabilistic Regulatory Gene Networks (PRN. his concept is the same or a natural generalization of the concept Probabilistic Boolean Networks (PBN), introduced by I. Shmulevich, E. Dougherty, and W. Zhang, particularly the model PBN has been using to describe genetic networks and has therapeutic applications. In PRNs the most important question is to describe the steady states of the systems, so in this paper we pay attention to the idea of transforming a network to another without lost all the properties, in particular the probability distribution. Following this objective we develop the concepts of homomorphism and $\epsilon$-homomorphism of probabilistic regulatory networks, since these concepts bring the properties from one networks to another. Projections are special homomorphisms, and they always induce invariant subnetworks that contain all cycles and steady states in the network.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Special homomorphisms between Probabilistic Gene Regulatory Networks does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Special homomorphisms between Probabilistic Gene Regulatory Networks, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Special homomorphisms between Probabilistic Gene Regulatory Networks will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-69623

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.