MOGAMUN

Two key elements of a systems-biology-based analysis are omics data and biological networks. Omics data give us a broad overview of a set of samples, whereas biological networks can either be generated from experimental data (for instance, mass-difference networks from metabolomics data) or they can be generic representations of biological knowledge (for instance, genome-scale metabolic networks or protein-protein interaction networks). Such heterogeneous biological networks can be organized as multi-layer networks, where every layer is an independent network, and common or related nodes from different layers can be linked by inter-layer edges. But the question is then how can we find groups of nodes (i.e., subnetworks) of interest that span over multiple layers? In this context, I developed MOGAMUN, a multi-objective genetic algorithm that analyzes multi-layer networks with transcriptomics data embedded, and finds the active modules, i.e., highly connected genes (nodes) with an overall deregulation. MOGAMUN is available as a Bioconductor package.