Statistical mechanics of organization, information, and emergence in protein networks (Q863881)

The author presents a theory of biochemical networks applying basic information theory concepts to organization, information and emergence in protein networks. Based on the concept of mutual information of integration I(X:Y)N, he introduces three types of organization and integration of biochemical networks. For zero values of I(X:Y)N the networks does not exhibit collective properties and has no information, for positive values of I(X:Y)N the system possess few collective properties while for negative values it is complex or emergent, exhibiting many collective properties. It is shown that emergence of information and collective properties in protein networks might appear as the consequence of competition phenomena or conformation changes in proteins. It is also shown that the notion of generalized microscopic reversibility is the condition to derive expression of the mutual integration of a network in steady state. The results reported in this review paper are based on the similarity between a communication system composed of a source with a given alphabet, the channel with its encoder and the receptor and the translation of the DNA message into protein language. However, it is stressed that the main difference between the communication channel and a protein network is the possible lack of subadditivity between entropies. The author concludes that, due to their biochemical networks, living cells probably contain much more information than could be expected from analyzing only the sequences of their genomes.