A Multi-Objective Genetic Algorithm to Find Active Modules in Multiplex Biological Networks

Abstract

The identification of subnetworks of interest -or active modules -by integrating biological networks with molecular profiles is a key resource to inform on the processes perturbed in different cellular conditions. We here propose MOGAMUN, a Multi-Objective Genetic Algorithm to identify active modules in multiplex biological networks. MOGAMUN optimizes both the density of interactions and the scores of the nodes (e.g., their differential expression).We compare MOGAMUN with state-of-the-art methods, representative of different algorithms dedicated to the identification of active modules in single networks. MOGAMUN identifies dense and highscoring modules that are also easier to interpret. In addition, to our knowledge, MOGAMUN is the first method able to use multiplex networks. Multiplex networks are composed of different layers of physical and functional relationships between genes and proteins. Each layer is associated to its own meaning, topology, and biases; the multiplex framework allows exploiting this diversity of biological networks.We applied MOGAMUN to identify cellular processes perturbed in Facio-Scapulo-Humeral muscular Dystrophy, by integrating RNA-seq expression data with a multiplex biological network. We identified different active modules of interest, thereby providing new angles for investigating the pathomechanisms of this disease. Availability: MOGAMUN is available at https://github.com/elvanov/MOGAMUN.

Pathomechanisms and biomarkers in facioscapulohumeral muscular dystrophy: roles of DUX4 and PAX7

“…Limitations of poor concordance in differentially expressed genes have been overcome by application of sophisticated, bioinformatic, network‐based tools. These have identified consistent signals in pathway disruption in FSHD muscle biopsies, including TNFα signalling, HIF1α over‐activation, Wnt/β‐catenin signalling and mitochondrial function via OGDHL (Banerji et al , 2015 ; preprint: Novoa‐del‐Toro et al , 2020 ).…”

“…The MOGAMUN algorithm identified highly connected sub‐networks of differentially expressed genes in the FSHD muscle transcriptomic data of Yao et al , 2014 , including perturbation of a gene module involved in mitochondrial function centred around 2‐oxoglutarate dehydrogenase‐like, mitochondrial (OGDHL). Although no active modules contained DUX4 target genes (possibly due to poor curation of relationships between these genes), 23 active modules involving PAX7 target genes were found (preprint: Novoa‐del‐Toro et al , 2020 ).…”

AKT Signaling Modifies the Balance between Cell Proliferation and Migration in Neural Crest Cells from Patients Affected with Bosma Arhinia and Microphthalmia Syndrome

“…As network-based approaches have been highly efficient for identification of biological processes perturbed in diseases, we performed in-depth analysis of our RNA-Seq data using MOGAMUN, a novel algorithm designed to identify active modules by integrating expression data with one or more networks of biological interactions [42]. By comparing the different active modules found in the different conditions using DEGs as seeds, we identified genes and pathways that appeared highly meaningful with regard to neural crest cell differentiation and BAMS pathophysiology.…”

Toward modeling metabolic state from single-cell transcriptomics

“…For example, the activity of the whole pathway depends on the activity of all enzymes in the chain with downregulation of intermediate enzymes potentially resulting in metabolic bottlenecks that lead to lower pathway activity even when other enzymes are highly active. Methods that incorporate pathway topology information or enable data-driven pathway discovery had been developed [ [61] , [62] , [63] , [64] , [65] , [66] ]. For example, to pinpoint critical genes and metabolites scMetNet [ 39 ] constructs a metabolic network based on KEGG and then finds submodules with significant differential expression across cell populations.…”