Journal
ARTIFICIAL LIFE
Volume 17, Issue 4, Pages 315-329Publisher
MIT PRESS
DOI: 10.1162/artl_a_00041
Keywords
Fisher information; Shannon information; random Boolean networks; phase transition; edge of chaos
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We study the order-chaos phase transition in random Boolean networks (RBNs), which have been used as models of gene regulatory networks. In particular, we seek to characterize the phase diagram in information-theoretic terms, focusing on the effect of the control parameters (activity level and connectivity). Fisher information, which measures how much system dynamics can reveal about the control parameters, offers a natural interpretation of the phase diagram in RBNs. We report that this measure is maximized near the order-chaos phase transitions in RBNs, since this is the region where the system is most sensitive to its parameters. Furthermore, we use this study of RBNs to clarify the relationship between Shannon and Fisher information measures.
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