Network growth models and genetic regulatory networks.

Journal Article

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 counterintuitive trends as the parameters are varied, including the broadening of the in-degree distribution when the probability for retaining input links is decreased. We also find that both the scaling of transcription factors with genome size and the measured degree distributions for genes in yeast can be reproduced by the growth algorithm if and only if a special seed is used to initiate the process.

Full Text

Duke Authors

Cited Authors

  • Foster, DV; Kauffman, SA; Socolar, JES

Published Date

  • March 2006

Published In

Volume / Issue

  • 73 / 3 Pt 1

Start / End Page

  • 031912 -

PubMed ID

  • 16605563

International Standard Serial Number (ISSN)

  • 1539-3755

Digital Object Identifier (DOI)

  • 10.1103/PhysRevE.73.031912

Language

  • eng

Conference Location

  • United States