Network growth models and genetic regulatory networks
D V Foster1, S A Kauffman, J E S Socolar
1Physics Department and Center for Nonlinear and Complex Systems, Duke University, Durham, North Carolina 27514, USA.
Abstract:
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.
More Related Videos
Related Concept Videos
Population Growth
Exponential Equations for Modeling Growth
Modeling with Differential Equations
Growth Models with Integration: Problem Solving
Genetic Screens
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...
Global Regulatory Systems


