Complexity reduction of biochemical networks
Pages 1690 - 1697
Abstract
This paper discusses two broad approaches for reducing the complexity of large cellular network models. The first approach involves exploiting conservation and time-scale separation and allows the dimension of the model to be significantly reduced. The second approach involves identifying subnetworks that carry out well defined functions and replacing these with simpler representations. Examples include identification of functional subnetworks such as oscillators or bistable switches and replacing these with a simplified mathematical construct. This enables complex networks to be rationalized as a series of hierarchical modules and greatly simplifies our ability to understand the dynamics of complex networks.
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- Complexity reduction of biochemical networks
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Published In
December 2006
2429 pages
ISBN:1424405017
- Editors:
- L. Felipe Perrone,
- Barry G. Lawson,
- Jason Liu,
- Frederick P. Wieland,
- General Chair:
- David Nicol,
- Program Chair:
- Richard Fujimoto
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- IIE: Institute of Industrial Engineers
- ASA: American Statistical Association
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- NIST: National Institute of Standards and Technology
- (SCS): The Society for Modeling and Simulation International
- INFORMS-CS: Institute for Operations Research and the Management Sciences-College on Simulation
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Winter Simulation Conference
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Published: 03 December 2006
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