Are Genetically Robust Regulatory Networks Dynamically Different from Random Ones?
Molecular Networks
2010-12-07 v2 Statistical Mechanics
Adaptation and Self-Organizing Systems
Quantitative Methods
Abstract
We study a genetic regulatory network model developed to demonstrate that genetic robustness can evolve through stabilizing selection for optimal phenotypes. We report preliminary results on whether such selection could result in a reorganization of the state space of the system. For the chosen parameters, the evolution moves the system slightly toward the more ordered part of the phase diagram. We also find that strong memory effects cause the Derrida annealed approximation to give erroneous predictions about the model's phase diagram.
Cite
@article{arxiv.q-bio/0703022,
title = {Are Genetically Robust Regulatory Networks Dynamically Different from Random Ones?},
author = {Volkan Sevim and Per Arne Rikvold},
journal= {arXiv preprint arXiv:q-bio/0703022},
year = {2010}
}
Comments
To be published in Computer Simulation Studies in Condensed-Matter Physics XX. Ed. by D.P. Landau, S. P. Lewis, H.-B. Schuttler (Springer-Verlag, Berlin Heidelberg New York)