Title:Learning from Metabolic Networks: Current Trends and Future Directions for Precision Medicine
Volume: 28
Issue: 32
关键词:
代谢网络、生化数据库、精准医学、组学数据、数学建模、机器学习、深度学习。
摘要:
Background: 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.
Methods: 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.
Conclusion: 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).
