National Research Council, Inst. for High-Performance Computing and Networking, Naples, Italy.
Information Technology Services, University of Naples "L'Orientale", Naples, Italy.
Curr Med Chem. 2021;28(32):6619-6653. doi: 10.2174/0929867328666201217103148.
Systems biology and network modeling represent, nowadays, the hallmark approaches for the development of predictive and targeted-treatment based precision medicine. The study of health and disease as properties of the human body system allows the understanding of the genotype-phenotype relationship through the definition of molecular interactions and dependencies. In this scenario, metabolism plays a central role as its interactions are well characterized and it is considered an important indicator of the genotype- phenotype associations. In metabolic systems biology, the genome-scale metabolic models are the primary scaffolds to integrate multi-omics data as well as cell-, tissue-, condition- specific information. Modeling the metabolism has both investigative and predictive values. Several methods have been proposed to model systems, which involve steady-state or kinetic approaches, and to extract knowledge through machine and deep learning.
This review collects, analyzes, and compares the suitable data and computational approaches for the exploration of metabolic networks as tools for the development of precision medicine. To this extent, we organized it into three main sections: "Data and Databases", "Methods and Tools", and "Metabolic Networks for medicine". In the first one, we have collected the most used data and relative databases to build and annotate metabolic models. In the second section, we have reported the state-of-the-art methods and relative tools to reconstruct, simulate, and interpret metabolic systems. Finally, we have reported the most recent and innovative studies that exploited metabolic networks to study several pathological conditions, not only those directly related to metabolism.
We think that this review can be a guide to researchers of different disciplines, from computer science to biology and medicine, in exploring the power, challenges and future promises of the metabolism as predictor and target of the so-called P4 medicine (predictive, preventive, personalized and participatory).
系统生物学和网络建模代表了当前开发预测性和靶向治疗精准医学的标志性方法。将健康和疾病作为人体系统的属性进行研究，可以通过定义分子相互作用和依赖关系来理解基因型-表型关系。在这种情况下，代谢起着核心作用，因为它的相互作用得到了很好的描述，并且被认为是基因型-表型关联的重要指标。在代谢系统生物学中，基因组规模的代谢模型是整合多组学数据以及细胞、组织、条件特异性信息的主要支架。代谢建模具有探索性和预测性价值。已经提出了几种方法来对系统进行建模，这些方法涉及稳态或动力学方法，并通过机器学习和深度学习提取知识。
本综述收集、分析和比较了适合探索代谢网络的方法和计算方法，将其作为开发精准医学的工具。为此，我们将其分为三个主要部分：“数据和数据库”、“方法和工具”和“代谢网络与医学”。在第一个部分中，我们收集了构建和注释代谢模型最常用的数据和相关数据库。在第二个部分中，我们报告了重建、模拟和解释代谢系统的最新方法和相关工具。最后，我们报告了最新和创新的研究，这些研究利用代谢网络研究了几种病理状况，不仅包括与代谢直接相关的状况。
我们认为，这篇综述可以为来自计算机科学、生物学和医学等不同学科的研究人员提供指导，帮助他们探索代谢作为所谓 P4 医学（预测性、预防性、个性化和参与性）的预测因子和治疗靶点的潜力、挑战和未来前景。
