The cell contains numerous networks for information processing. These networks are responsible for carrying out all cell functions including gene transcription, signal transconduction, and metabolic activities. Many of these networks process information similar to digital logic circuits and classical logic methods have been successfully used to analyze their behavior. The objective of this paper is to investigate the potential of circuit structural analysis techniques in analyzing the topologies of cellular networks arising in systems biology context. Rent's rule has been in particular a classical method that is used in analyzing the topologies of digital circuits. We investigate the applicability of Rent's rule to systems biology networks, and we outline the structural similarities and differences between circuit networks and systems biology networks. We compute Rent's rule parameters and classify systems biology networks according to their Rent's exponent. Interestingly, networks that process information in a logical fashion have Rent exponents that are similar to that of logic circuits. To provide a basis for our results we utilize recent advancements in graph theory to create random artificial networks with the same degree sequences as real networks and extend our experiments to those circuits as well. Our results open the door for other researchers to further investigate topological circuit analysis techniques for networks in systems biology.
['Sherief Reda']
Using circuit structural analysis techniques for networks in systems biology