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A neural nanonetwork model based on cell signaling molecules

A neural nanonetwork model based on cell signaling molecules,10.1109/INFCOMW.2011.5928862,Aron Szabo,Gabor Vattay,Daniel Kondor

A neural nanonetwork model based on cell signaling molecules  
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All living entities have to adapt to their environment. This is particularly true for unicellular organisms. In this case, en- vironmental signals are quite simple, eg. temperature changes or changes in the chemical composition of the environment. Their so-called signaling network which consists of thousands of proteins reacts to such external stimuli. Its role is similar to the neural network of higher organisms. The high degree of interconnectedness and complexity of the protein network makes it very similar to a neural network. The nodes of this network are the proteins that travel in the cell's inner volume by diffusion. Proteins in the signaling network can be regarded as in- formation processing units: they produce molecules according to their molecular input. These proteins have two states: an activated and a deactivated. A protein in its activated state is also called phosphorylated. Hence this two-state system can be regarded as a binary bit based information storage system and passage of the activated state from protein to protein can be regarded as information propagation in the network. Such properties of the cellular signaling network enable us to use this system for computations and also as a nanoscale communication network. Recently, it has been shown (1) that cell based molecular nano-communication networks can be modeled in terms of information theory. The receptor of the cell membrane (ligand) can be regarded as a sender and the cellular nucleus can be regarded as a receiver of the intra-cell communication. The response of the nucleus for the incoming cellular signals can be the initiation of the transcription of a specific gene. The tran- scription factors are the output nodes of the cell signaling net-
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