Learning from metabolic networks: current trends and future directions for precision medicine

Granata, Ilaria, Manzo, Mario, Kusumastuti, Ari and Guarracino, Mario R. (2020) Learning from metabolic networks: current trends and future directions for precision medicine. Current Medicinal Chemistry, 28 (32). pp. 6619-6653. ISSN 1875-533X

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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).

Item Type: Journal Article
Keywords: metabolic networks; biochemical databases; precision medicine; omics data; mathematical modeling; machine learning; deep learning
Subjects: 01 MATHEMATICAL SCIENCES > 0105 Mathematical Physics
Divisions: Faculty of Mathematics and Sciences > Department of Mathematics
Depositing User: Ari Kusumastuti
Date Deposited: 12 Apr 2023 08:38


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