Computational approaches for modeling regulatory cellular networks

Abstract

Cellular components interact with each other to form networks that process information and evoke biological responses. A deep understanding of the behavior of these networks requires the development and analysis of mathematical models. In this article, different types of mathematical representations for modeling signaling networks are described, and the advantages and disadvantages of each type are discussed. Two experimentally well-studied signaling networks are then used as examples to illustrate the insight that could be gained through modeling. Finally, the modeling approach is expanded to describe how signaling networks might regulate cellular machines and evoke phenotypic behaviors.During the past two decades, substantial progress has been made in understanding the biochemical properties of cellular components, in addition to the organization of various molecular machines such as those involved in transcription and motility. From these studies of components and pathways, the design principles underlying cellular networks have emerged [1,2]. However, a substantial number of experiments is still needed to build up thè parts list' for a cell and to specify `parts function' in terms of cellular location, dynamics and regulatory organization. Because of the sheer number of components and interactions, the analysis of regulatory interactions is not easily achieved through intuition; even pathways and networks with few components are configured into systems that display complex behaviors. Hence, it is becoming increasingly clear that quantitative descriptions that lead to predictive models can be of great use in analyzing signaling pathways.Models of regulatory networks can be developed at various levels. Each level has its value. The simplest models of regulatory pathways and networks depict them as connections maps (http://stke.sciencemag.org), which are useful starting points for detailed analyses of signaling pathways. Although many signaling pathways have been identified by the study of binary reactions, connections are increasingly deduced from high-throughput experimental analyses of both protein-protein interactions [3][4][5][6] and protein location and expression patterns [7,8]. However, these connection maps are largely qualitative and, hence, only limited mathematical analysis can be undertaken. Such analyses often fall along the line of statistical correlations (`clustering'), which reveals co-regulation of each component [9], or an analysis of how the components are connected, which describes the statistical properties of the network as a whole [10][11][12]. An advantage of these models is that they can be developed for large numbers of components and interactions, and are useful in obtaining an To understand how extracellular signals evoke dynamic cellular responses, an analysis of the chemical reactions that constitute a biological system is needed. Typically, such models are built in three stages. First, a biochemical scheme that depicts the chemical reactions between the components in the...

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