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Recent development and biomedical applications of probabilistic Boolean networks

Panuwat Trairatphisan1*, Andrzej Mizera2, Jun Pang2, Alexandru Adrian Tantar24, Jochen Schneider35 and Thomas Sauter1

Author Affiliations

1 Life Sciences Research Unit, University of Luxembourg, Luxembourg

2 Computer Science and Communications Research Unit, University of Luxembourg, Luxembourg

3 Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Luxembourg

4 Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Luxembourg

5 Saarland University Medical Center, Department of Internal Medicine II, Homburg, Saarland, Germany

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Cell Communication and Signaling 2013, 11:46  doi:10.1186/1478-811X-11-46

Published: 1 July 2013


Probabilistic Boolean network (PBN) modelling is a semi-quantitative approach widely used for the study of the topology and dynamic aspects of biological systems. The combined use of rule-based representation and probability makes PBN appealing for large-scale modelling of biological networks where degrees of uncertainty need to be considered.

A considerable expansion of our knowledge in the field of theoretical research on PBN can be observed over the past few years, with a focus on network inference, network intervention and control. With respect to areas of applications, PBN is mainly used for the study of gene regulatory networks though with an increasing emergence in signal transduction, metabolic, and also physiological networks. At the same time, a number of computational tools, facilitating the modelling and analysis of PBNs, are continuously developed.

A concise yet comprehensive review of the state-of-the-art on PBN modelling is offered in this article, including a comparative discussion on PBN versus similar models with respect to concepts and biomedical applications. Due to their many advantages, we consider PBN to stand as a suitable modelling framework for the description and analysis of complex biological systems, ranging from molecular to physiological levels.

Probabilistic Boolean networks; Probabilistic graphical models; Qualitative modelling; Systems biology