Capobianco Enrico
Center for Computational Science, University of Miami, Miami, FL 33146, USA.
J Clin Med. 2019 May 11;8(5):664. doi: 10.3390/jcm8050664.
Nowadays, networks are pervasively used as examples of models suitable to mathematically represent and visualize the complexity of systems associated with many diseases, including cancer. In the cancer context, the concept of network entropy has guided many studies focused on comparing equilibrium to disequilibrium (i.e., perturbed) conditions. Since these conditions reflect both structural and dynamic properties of network interaction maps, the derived topological characterizations offer precious support to conduct cancer inference. Recent innovative directions have emerged in network medicine addressing especially experimental omics approaches integrated with a variety of other data, from molecular to clinical and also electronic records, bioimaging etc. This work considers a few theoretically relevant concepts likely to impact the future of applications in personalized/precision/translational oncology. The focus goes to specific properties of networks that are still not commonly utilized or studied in the oncological domain, and they are: controllability, synchronization and symmetry. The examples here provided take inspiration from the consideration of metastatic processes, especially their progression through stages and their hallmark characteristics. Casting these processes into computational frameworks and identifying network states with specific modular configurations may be extremely useful to interpret or even understand dysregulation patterns underlying cancer, and associated events (onset, progression) and disease phenotypes.
如今，网络被广泛用作适合以数学方式表示和可视化与包括癌症在内的许多疾病相关的系统复杂性的模型示例。在癌症背景下，网络熵的概念指导了许多专注于比较平衡状态与不平衡（即受干扰）状态的研究。由于这些状态反映了网络相互作用图谱的结构和动态特性，所推导的拓扑特征为进行癌症推断提供了宝贵的支持。网络医学领域出现了一些新的创新方向，特别是将实验性组学方法与从分子到临床以及电子记录、生物成像等各种其他数据相结合的方法。这项工作考虑了一些在理论上可能影响个性化/精准/转化肿瘤学未来应用的相关概念。重点关注网络的特定属性，这些属性在肿瘤学领域仍未得到普遍利用或研究，它们是：可控性、同步性和对称性。这里提供的示例灵感来自对转移过程的思考，特别是它们通过各个阶段的进展及其标志性特征。将这些过程纳入计算框架并识别具有特定模块配置的网络状态，对于解释甚至理解癌症潜在的失调模式以及相关事件（发病、进展）和疾病表型可能极其有用。
