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WIREs Syst Biol Med
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Systems biology approaches to toll‐like receptor signaling

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Abstract Toll‐like receptor (TLR) signaling pathways constitute an evolutionarily conserved host defense system that protects against a broad range of infectious agents. Modeling of TLR signaling has been carried out at several levels. Structural models of TLRs and their adaptors, which utilize a small number of structural domains to recognize a diverse range of pathogens, provide a starting point for understanding how pathogens are recognized and signaling events initiated. Various experimental and computational techniques have been used to construct models of downstream signal transduction networks from the measurements of gene expression and chromatin structure under resting and perturbed conditions along with predicted regulatory sequence motifs. Although a complete and accurate mathematical model of all TLR signaling pathways has yet to be derived, many important modules have been identified and investigated, enhancing our understanding of innate immune responses. Extensions of these models based on emerging experimental techniques are discussed. WIREs Syst Biol Med 2012 doi: 10.1002/wsbm.1178 This article is categorized under: Models of Systems Properties and Processes > Cellular Models Biological Mechanisms > Cell Signaling Analytical and Computational Methods > Computational Methods Models of Systems Properties and Processes > Mechanistic Models

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Multi‐scale modeling of toll‐like receptor (TLR) pathways. (a) The MyD88‐ and TRIF‐dependent pathways are illustrated. Reprinted with permission from Ref 2 Copyright 2010 Elsevier. (b) The X‐ray structure of TLR3 leucine rich repeat (LRR) domains bound to double‐stranded RNA. (c) A mathematical model of a hypothetical signaling network between components A–F. (d) A heatmap of gene expression values at 10 time points.

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Schematic picture of mathematical modeling approaches to toll‐like receptor (TLR) signal transduction networks. In order to construct mathematical models, several kinds of data, such as network topology and biochemical parameters, are required. Those data must be collected or inferred from literature, databases, or experiments. Mathematical models allow us to predict system behavior under different conditions based on assumed rules (or laws). Essential features of the system, such as oscillatory behavior caused by the strong negative feedback loops of IkBa or input–output relationships among TLR receptors and transcriptional output, can be extracted through such models. Predictions can be validated using further experiments, thus enhancing our knowledge of the system.

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Structural domains of toll‐like receptors (TLRs) and their adaptors. The canonical domains of TLRs and their adaptors are shown as 1D bar graphs with the domains drawn to scale. Darker colors indicate experimentally determined structures while lighter colors indicate domains that can be modeled by homology. The cartoon representations of several representative structures are drawn to scale: TLR4 leucine rich repeat (LRR) dimer,15 TLR10 TIR (interleukin‐1) receptordimer,16 TIRAP TIR dimer,17 MyD88 TIR,18 and myddosome complex.9 In the cartoon representations, dark/light shades are used to distinguish individual chains in dimers.

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Models of Systems Properties and Processes > Cellular Models
Models of Systems Properties and Processes > Mechanistic Models
Biological Mechanisms > Cell Signaling
Analytical and Computational Methods > Computational Methods

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