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This article is part of the supplement: The International Conference on Intelligent Biology and Medicine (ICIBM): Systems Biology

Open Access Research

A strategy to study pathway cross-talks of cells under repetitive exposure to stimuli

Yan Fu12, Xiaoshan Jiang1, Hang Zhang12 and Jianhua Xing1*

  • * Corresponding author: Jianhua Xing jxing@vt.edu

  • † Equal contributors

Author affiliations

1 Department of Biological Sciences, Virginia Tech, Blacksburg, VA 24060, USA

2 Interdisciplinary PhD Program of Genetics, Bioinformatics and Computational Biology, Virginia Tech, Blacksburg, VA 24060, USA

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Citation and License

BMC Systems Biology 2012, 6(Suppl 3):S6  doi:10.1186/1752-0509-6-S3-S6

Published: 17 December 2012

Abstract

Background

Cells are subject to fluctuating and multiple stimuli in their natural environment. The signaling pathways often crosstalk to each other and give rise to complex nonlinear dynamics. Specifically repetitive exposure of a cell to a same stimulus sometime leads to augmented cellular responses. Examples are amplified proinflammatory responses of innate immune cells pretreated with a sub-threshold then a high dose of endotoxin or cytokine stimulation. This phenomenon, called priming effect in the literature, has important pathological and clinical significances.

Results

In a previous study, we enumerated possible mechanisms for priming using a three-node network model. The analysis uncovered three mechanisms. Based on the results, in this work we developed a straightforward procedure to identify molecular candidates contributing to the priming effect and the corresponding mechanisms. The procedure involves time course measurements, e.g., gene expression levels, or protein activities under low, high, and low + high dose of stimulant, then computational analysis of the dynamics patterns, and identification of functional roles in the context of the regulatory network. We applied the procedure to a set of published microarray data on interferon-γ-mediated priming effect of human macrophages. The analysis identified a number of network motifs possibly contributing to Interferon-γ priming. A further detailed mathematical model analysis further reveals how combination of different mechanisms leads to the priming effect.

Conclusions

One may perform systematic screening using the proposed procedure combining with high throughput measurements, at both transcriptome and proteome levels. It is applicable to various priming phenomena.