Purpose:
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.
Method:
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 which exploited metabolic networks for the study of several
pathological conditions, not only those directly related to the 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).
Using the Topology of Metabolic Networks to Predict Viability of Mutant Strains
