Inferring Interaction Networks From Multi-Omics Data - Hawe, Johann S.; Theis, Fabian J.; Heinig, Matthias (Frontiers in Genetics, 2019)
A major goal in systems biology is a comprehensive description the entirety of all complex interactions between different types of biomolecules - also referred to as the interactome - and how these interactions give rise to higher, cellular and organism level functions or diseases. Numerous efforts have been undertaken to define such interactomes experimentally, for example yeast-tow-hybrid based protein-protein interaction networks or ChIP-seq based protein-DNA interactions for individual proteins. To complement these direct measurements, genome-scale quantitative multi-omics data (proteomics, transcriptomics, etc.) enable researchers to predict novel functional interactions between molecular species. Moreover, these data allow to distinguish relevant functional from non-functional interactions in a specific biological context. However, integration of multi-omics data is not straight forward due to their heterogeneity. So far, interaction networks have mostly been inferred from homogeneous functional data (e.g. gene co-expression networks) and novel methods are essential for inferring comprehensive regulatory networks across omics data types. Here we review state-of-the-art techniques for inferring the topology of interaction networks from functional multi-omics data, encompassing graphical models with multiple node types and quantitative-trait-loci (QTL) based approaches. In addition, we will discuss Bayesian aspects of network inference, which allow for leveraging already established biological information, such as known protein-protein or protein-DNA interactions, to guide the inference process.