Complexity reduction of biochemical networks

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.