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Front Immunol. 2014 Apr 15;5:172. doi: 10.3389/fimmu.2014.00172. eCollection 2014.

An Interaction Library for the FcεRI Signaling Network.

Author information

1
Department of Chemistry and Chemical Biology, Cornell University , Ithaca, NY , USA ; Los Alamos National Laboratory, Theoretical Division, Center for Non-linear Studies , Los Alamos, NM , USA.
2
Department of Chemistry and Chemical Biology, Cornell University , Ithaca, NY , USA.
3
Los Alamos National Laboratory, Theoretical Division, Center for Non-linear Studies , Los Alamos, NM , USA.

Abstract

Antigen receptors play a central role in adaptive immune responses. Although the molecular networks associated with these receptors have been extensively studied, we currently lack a systems-level understanding of how combinations of non-covalent interactions and post-translational modifications are regulated during signaling to impact cellular decision-making. To fill this knowledge gap, it will be necessary to formalize and piece together information about individual molecular mechanisms to form large-scale computational models of signaling networks. To this end, we have developed an interaction library for signaling by the high-affinity IgE receptor, FcεRI. The library consists of executable rules for protein-protein and protein-lipid interactions. This library extends earlier models for FcεRI signaling and introduces new interactions that have not previously been considered in a model. Thus, this interaction library is a toolkit with which existing models can be expanded and from which new models can be built. As an example, we present models of branching pathways from the adaptor protein Lat, which influence production of the phospholipid PIP3 at the plasma membrane and the soluble second messenger IP3. We find that inclusion of a positive feedback loop gives rise to a bistable switch, which may ensure robust responses to stimulation above a threshold level. In addition, the library is visualized to facilitate understanding of network circuitry and identification of network motifs.

KEYWORDS:

IgE receptors (FcεRI); computational modeling; feed-forward loops; immunoreceptor signaling; knowledge engineering; mast cells; network motifs; visualization

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