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Living systems are often described utilizing informational analogies. Animportant open question is whether information is merely a useful conceptualmetaphor, or intrinsic to the operation of biological systems. To address thisquestion, we provide a rigorous case study of the informational architecture oftwo representative biological networks: the Boolean network model for thecell-cycle regulatory network of the fission yeast S. pombe and that of thebudding yeast S. cerevisiae. We compare our results for these biologicalnetworks to the same analysis performed on ensembles of two different types ofrandom networks. We show that both biological networks share features in commonthat are not shared by either ensemble. In particular, the biological networksin our study, on average, process more information than the random networks.They also exhibit a scaling relation in information transferred between nodesthat distinguishes them from either ensemble: even when compared to theensemble of random networks that shares important topological properties, suchas a scale-free structure. We show that the most biologically distinct regimeof this scaling relation is associated with the dynamics and function of thebiological networks. Information processing in biological networks is thereforeinterpreted as an emergent property of topology (causal structure) and dynamics(function). These results demonstrate quantitatively how the informationalarchitecture of biologically evolved networks can distinguish them from otherclasses of network architecture that do not share the same informationalproperties.

New Scaling Relation for Information Transfer in Biological Networks