Abstract
Interactions among genes, proteins, and metabolites generate most of the central functions of the living cell. These interactions take place in highly complex biochemical networks, often involving hundreds of components and reactions. Exposing the connection between the individual components, such as genes, and the overall behavior of the network requires a systems approach based on dynamic models of the network. In this article, the author illustrates how a simple linear systems analysis can be used to analyze the role of various components in generating complex dynamic behavior in biochemical networks. The approach is used to identify the most important proteins and mutual interactions involved in the cell cycle in frog eggs and sustained oscillations of glycolysis in yeast. The approach is applicable to large-scale network models that can be developed in the near future based on high throughput data on a genomic and proteomic scale.
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