Learning from Metabolic Networks: Current Trends and Future Directions for Precision Medicine

Abstract

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).

TumorMet: A repository of tumor metabolic networks derived from context-specific Genome-Scale Metabolic Models

“…According to this, the reconstruction of comprehensive networks through the integration of omics data into metabolic scaffolds is one of the tools preferred by the systems biology approach for investigating biological phenomena from a holistic point of view. The metabolic scaffolds are given by the Genome-Scale Metabolic Models (GSMs), built from multi-omics data integration, and carrying information concerning the genes/proteins with enzymatic activity, how they interact with bioactive compounds in the context of biochemical reactions, and how the metabolic interconnections change in different cells, tissues or specific conditions 4 . There is a great interest in exploiting these models to generate condition-specific graphs at the service of machine learning approaches.…”

“…Metabolic networks are complex and can involve different metabolic players (i.e., metabolites, enzymes, reactions). Machine and deep learning frameworks allow extracting knowledge from the metabolic networks while dealing with their structural and relational complexity 4 . In the context of findability, accessibility, interoperability, and reusability (FAIR) principles 8 , providing benchmark datasets for comparing novel approaches and for the general advancement of a specific research domain is extremely important.…”