Gene regulatory network inference: Data integration in dynamic models—A review |
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Authors: | Michael Hecker Sandro Lambeck Susanne Toepfer Eugene van Someren Reinhard Guthke |
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Affiliation: | 1. Leibniz Institute for Natural Product Research and Infection Biology - Hans Knoell Institute, Beutenbergstr. 11a, D-07745 Jena, Germany;2. BioControl Jena GmbH, Wildenbruchstr. 15, D-07745 Jena, Germany;3. Centre for Molecular and Biomolecular Informatics (CMBI) and Department of Applied Biology, Nijmegen Centre for Molecular Life Sciences, Radboud Universiteit Nijmegen, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands |
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Abstract: | Systems biology aims to develop mathematical models of biological systems by integrating experimental and theoretical techniques. During the last decade, many systems biological approaches that base on genome-wide data have been developed to unravel the complexity of gene regulation. This review deals with the reconstruction of gene regulatory networks (GRNs) from experimental data through computational methods. Standard GRN inference methods primarily use gene expression data derived from microarrays. However, the incorporation of additional information from heterogeneous data sources, e.g. genome sequence and protein–DNA interaction data, clearly supports the network inference process. This review focuses on promising modelling approaches that use such diverse types of molecular biological information. In particular, approaches are discussed that enable the modelling of the dynamics of gene regulatory systems. The review provides an overview of common modelling schemes and learning algorithms and outlines current challenges in GRN modelling. |
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Keywords: | Systems biology Reverse engineering Biological modelling Knowledge integration |
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