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 high-scoring 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.

中文翻译：

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在多重生物网络中寻找主动模块的多目标遗传算法

通过将生物网络与分子图谱整合在一起，识别感兴趣的子网络（或活动模块）是告知在不同细胞条件下受到干扰的过程的关键资源。我们在此提出MOGAMUN，这是一种用于识别多重生物网络中活动模块的多目标遗传算法。MOGAMUN优化了交互的密度和节点的分数（例如，它们的差异表达）。我们将MOGAMUN与最先进的方法进行比较，它们代表了专用于识别单个网络中活动模块的不同算法。MOGAMUN识别密集且高分的模块，这些模块也易于解释。另外，据我们所知，MOGAMUN是第一种能够使用多路复用网络的方法。多重网络由基因和蛋白质之间的物理和功能关系的不同层组成。每层都有自己的含义，拓扑和偏见；多重框架允许利用这种多样性的生物网络。我们通过将RNA-seq表达数据与一个多重生物网络整合，将MOGAMUN应用到了Facio-Scapulo-肱骨肌营养不良症中受干扰的细胞过程中。我们确定了感兴趣的不同活性模块，从而为研究这种疾病的致病机理提供了新的视角。我们将MOGAMUN通过将RNA-seq表达数据与一个多重生物网络整合，来识别在Facio-肩cap骨-肱骨肌营养不良症中受扰的细胞过程。我们确定了感兴趣的不同活性模块，从而为研究这种疾病的致病机理提供了新的视角。我们将MOGAMUN通过将RNA-seq表达数据与一个多重生物网络整合，来识别在Facio-肩cap骨-肱骨肌营养不良症中受扰的细胞过程。我们确定了感兴趣的不同活性模块，从而为研究这种疾病的发病机理提供了新的视角。