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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.