Network growth models and genetic regulatory networks

Network growth models and genetic regulatory networks.

We study a class of growth algorithms for directed graphs that are candidate models for the evolution of genetic regulatory networks. The algorithms involve partial duplication of nodes and their links, together with the innovation of new links, allowing for the possibility that input and output links from a newly created node may have different probabilities of survival. We find some counterin...

Learning and Validating Bayesian Network Models of Genetic Regulatory Networks

We propose a framework for learning from data and validating Bayesian network models of genetic regulatory networks. The learning phase selects multiple locally optimal models of the data and reports the best of them. The validation phase assesses the confidence in the model reported by studying all the different locally optimal models obtained in the learning phase. We prove that our proposal ...

Modeling gene regulatory networks: Classical models, optimal perturbation for identification of network

Deep understanding of molecular biology has allowed emergence of new technologies like DNA decryption.  On the other hand, advancements of molecular biology have made manipulation of genetic systems simpler than ever; this promises extraordinary progress in biological, medical and biotechnological applications.  This is not an unrealistic goal since genes which are regulated by gene regulatory ...

Boolean models for genetic regulatory networks

Dynamical models of genetic regulatory networks

It is shown that a simplified model of genetic regulatory networks, aimed at the study of their generic properties, can shed light on some important biological phenomena. Two cases are analyzed, namely perturbations in gene expression induced in an organism by the knock-out of selected genes, and cell differentiation. The role of simplified models in biology is discussed.