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Open Access Research article

Information theoretic approach to complex biological network reconstruction: application to cytokine release in RAW 264.7 macrophages

Farzaneh Farhangmehr12, Mano Ram Maurya13, Daniel M Tartakovsky2 and Shankar Subramaniam14*

  • * Corresponding author: Shankar Subramaniam shankar@ucsd.edu

  • † Equal contributors

Author Affiliations

1 Department of Bioengineering, University of California San Diego, 9500 Gilman Drive, 92093-0412 La Jolla, CA, USA

2 Department of Mechanical and Aerospace Engineering, University of California San Diego, 9500 Gilman Drive, 92093-0411 La Jolla, CA, USA

3 San Diego Supercomputer Center, University of California San Diego, 9500 Gilman Drive, 92093-0505 La Jolla, CA, USA

4 Departments of Chemistry & Biochemistry, Cellular and Molecular Medicine and Graduate Program in Bioinformatics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, USA

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BMC Systems Biology 2014, 8:77  doi:10.1186/1752-0509-8-77

Published: 25 June 2014

Abstract

Background

High-throughput methods for biological measurements generate vast amounts of quantitative data, which necessitate the development of advanced approaches to data analysis to help understand the underlying mechanisms and networks. Reconstruction of biological networks from measured data of different components is a significant challenge in systems biology.

Results

We use an information theoretic approach to reconstruct phosphoprotein-cytokine networks in RAW 264.7 macrophage cells. Cytokines are secreted upon activation of a wide range of regulatory signals transduced by the phosphoprotein network. Identifying these components can help identify regulatory modules responsible for the inflammatory phenotype. The information theoretic approach is based on estimation of mutual information of interactions by using kernel density estimators. Mutual information provides a measure of statistical dependencies between interacting components. Using the topology of the network derived, we develop a data-driven parsimonious input–output model of the phosphoprotein-cytokine network.

Conclusions

We demonstrate the applicability of our information theoretic approach to reconstruction of biological networks. For the phosphoprotein-cytokine network, this approach not only captures most of the known signaling components involved in cytokine release but also predicts new signaling components involved in the release of cytokines. The results of this study are important for gaining a clear understanding of macrophage activation during the inflammation process.

Keywords:
Bioinformatics; Data mining; Network inference; Data-driven network reconstruction; Information theory; Mutual information; Probabilistic algorithm; Statistical methods