Gene network analysis: from heart development to cardiac therapy

Fulvia Ferrazzi; Riccardo Bellazzi; Felix B. Engel

doi:10.1160/TH14-06-0483

Thrombosis and Haemostasis, Table of Contents

Thromb Haemost 2015; 113(03): 521-531
DOI: 10.1160/TH14-06-0483

Theme Issue Article

Schattauer GmbH

Gene network analysis: from heart development to cardiac therapy

Fulvia Ferrazzi

¹Institute of Human Genetics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany

,

Riccardo Bellazzi

²Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy

,

Felix B. Engel

³Experimental Renal and Cardiovascular Research, Department of Nephropathology, Institute of Pathology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany

› Author Affiliations

Abstract

Summary

Networks offer a flexible framework to represent and analyse the complex interactions between components of cellular systems. In particular gene networks inferred from expression data can support the identification of novel hypotheses on regulatory processes. In this review we focus on the use of gene network analysis in the study of heart development. Understanding heart development will promote the elucidation of the aetiology of congenital heart disease and thus possibly improve diagnostics. Moreover, it will help to establish cardiac therapies. For example, understanding cardiac differentiation during development will help to guide stem cell differentiation required for cardiac tissue engineering or to enhance endogenous repair mechanisms. We introduce different methodological frameworks to infer networks from expression data such as Boolean and Bayesian networks. Then we present currently available temporal expression data in heart development and discuss the use of network-based approaches in published studies. Collectively, our literature-based analysis indicates that gene network analysis constitutes a promising opportunity to infer therapy-relevant regulatory processes in heart development. However, the use of network-based approaches has so far been limited by the small amount of samples in available datasets. Thus, we propose to acquire high-resolution temporal expression data to improve the mathematical descriptions of regulatory processes obtained with gene network inference methodologies. Especially probabilistic methods that accommodate the intrinsic variability of biological systems have the potential to contribute to a deeper understanding of heart development.

Keywords

Gene networks - temporal expression data - reverse engineering - heart development

Full Text

References

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