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Highly designable phenotypes and mutational buffers emerge from a systematic mapping between network topology and dynamic output.


ABSTRACT: Deciphering the design principles for regulatory networks is fundamental to an understanding of biological systems. We have explored the mapping from the space of network topologies to the space of dynamical phenotypes for small networks. Using exhaustive enumeration of a simple model of three- and four-node networks, we demonstrate that certain dynamical phenotypes can be generated by an atypically broad spectrum of network topologies. Such dynamical outputs are highly designable, much like certain protein structures can be designed by an unusually broad spectrum of sequences. The network topologies that encode a highly designable dynamical phenotype possess two classes of connections: a fully conserved core of dedicated connections that encodes the stable dynamical phenotype and a partially conserved set of variable connections that controls the transient dynamical flow. By comparing the topologies and dynamics of the three- and four-node network ensembles, we observe a large number of instances of the phenomenon of "mutational buffering," whereby addition of a fourth node suppresses phenotypic variation amongst a set of three-node networks.

SUBMITTER: Nochomovitz YD 

PROVIDER: S-EPMC1449667 | biostudies-literature | 2006 Mar

REPOSITORIES: biostudies-literature

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Highly designable phenotypes and mutational buffers emerge from a systematic mapping between network topology and dynamic output.

Nochomovitz Yigal D YD   Li Hao H  

Proceedings of the National Academy of Sciences of the United States of America 20060303 11


Deciphering the design principles for regulatory networks is fundamental to an understanding of biological systems. We have explored the mapping from the space of network topologies to the space of dynamical phenotypes for small networks. Using exhaustive enumeration of a simple model of three- and four-node networks, we demonstrate that certain dynamical phenotypes can be generated by an atypically broad spectrum of network topologies. Such dynamical outputs are highly designable, much like cer  ...[more]

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