Control theory has seen recently impactful applications in network science, especially in connections with applications in network medicine. A key topic of research is that of finding minimal external interventions that offer control over the dynamics of a given network, a problem known as network controllability. We propose in this article a new solution for this problem based on genetic algorithms. We tailor our solution for applications in computational drug repurposing, seeking to maximise its use of FDA-approved drug targets in a given disease-specific protein-protein interaction network. We show how our algorithm identifies a number of potentially efficient drugs for breast, ovarian, and pancreatic cancer. We demonstrate our algorithm on several benchmark networks from cancer medicine, social networks, electronic circuits, and several random networks with their edges distributed according to the Erd\H{o}s-R\'{e}nyi, the small-world, and the scale-free properties. Overall, we show that our new algorithm is more efficient in identifying relevant drug targets in a disease network, advancing the computational solutions needed for new therapeutic and drug repurposing approaches.

中文翻译：

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使用遗传算法识别复杂交互网络的有效控制

控制理论最近在网络科学中看到了有影响力的应用，特别是在与网络医学应用的联系中。研究的一个关键主题是寻找最小的外部干预，以控制给定网络的动态，这个问题称为网络可控性。我们在本文中提出了一种基于遗传算法的针对该问题的新解决方案。我们为计算药物再利用的应用定制我们的解决方案，寻求在给定的疾病特异性蛋白质-蛋白质相互作用网络中最大限度地利用 FDA 批准的药物靶点。我们展示了我们的算法如何识别许多潜在有效的乳腺癌、卵巢癌和胰腺癌药物。我们在来自癌症医学、社交网络、电子电路、和几个随机网络，其边缘根据 Erd\H{o}sR\'{e}nyi、小世界和无标度属性分布。总的来说，我们表明我们的新算法在识别疾病网络中的相关药物靶点方面更有效，推进了新治疗和药物再利用方法所需的计算解决方案。