The study of networks, in the form of mathematical graph theory, is one of the fundamental pillars of systems biology. A network is fundamentally a set of items, which we call vertices or nodes, with connections between them, called edges. An informative network model must account for the complexities of emergent biological behavior while still being simple enough to allow reasonable interpretation of the results. Recent years have witnessed a substantial new movement in biological network research, with the focus shifting away from the analysis of single small networks and the properties of individual nodes or edges within such networks toward the consideration of statistical properties of large-scale networks. This change of scale has also forced a corresponding change in our analytical approaches. The outline of this chapter is as follows. In Section 4.1, we describe empirical studies of biological networks. In Section 4.2, we describe some of the common properties important for the understanding of the functioning of networked systems. In Section 4.3, we provide a survey of module discovery approaches. In Section 4.4, we address different issues related to the task of network inference. In Section 4.5, we summarize metrics for quantification of the performance of network inference methods. In Section 4.6, we address the problem of comparative assessment of performance among network inference methods. In Section 4.7, we present a survey of integrative network inference approaches.
|Title of host publication||Computational Systems Biology|
|Subtitle of host publication||Inference and Modelling|
|Number of pages||19|
|Publication status||Published - 2016|
|Name||Computational Systems Biology|