Mutual information in random Boolean models of regulatory networks

Andre S. Ribeiro, Stuart A. Kauffman, Jason Lloyd-Price, Björn Samuelsson, and Joshua E. S. Socolar
Phys. Rev. E 77, 011901 – Published 3 January 2008

Abstract

The amount of mutual information contained in the time series of two elements gives a measure of how well their activities are coordinated. In a large, complex network of interacting elements, such as a genetic regulatory network within a cell, the average of the mutual information over all pairs, I, is a global measure of how well the system can coordinate its internal dynamics. We study this average pairwise mutual information in random Boolean networks (RBNs) as a function of the distribution of Boolean rules implemented at each element, assuming that the links in the network are randomly placed. Efficient numerical methods for calculating I show that as the number of network nodes, N, approaches infinity, the quantity NI exhibits a discontinuity at parameter values corresponding to critical RBNs. For finite systems it peaks near the critical value, but slightly in the disordered regime for typical parameter variations. The source of high values of NI is the indirect correlations between pairs of elements from different long chains with a common starting point. The contribution from pairs that are directly linked approaches zero for critical networks and peaks deep in the disordered regime.

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  • Received 26 July 2007

DOI:https://doi.org/10.1103/PhysRevE.77.011901

©2008 American Physical Society

Authors & Affiliations

Andre S. Ribeiro1,*, Stuart A. Kauffman1,2, Jason Lloyd-Price1, Björn Samuelsson3, and Joshua E. S. Socolar3

  • 1Institute for Biocomplexity and Informatics, University of Calgary, Calgary, Alberta, Canada, T2N 1N4
  • 2Department of Physics and Astronomy, University of Calgary, Calgary, Alberta, Canada, T2N 1N4
  • 3Physics Department and Center for Nonlinear and Complex Systems, Duke University, Durham, North Carolina 27708, USA

  • *Present address: Institute of Signal Processing, Tampere University of Technology, P.O. Box 553, 33101 Tampere, Finland.

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Vol. 77, Iss. 1 — January 2008

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