Global reconstruction of the human metabolic network based on genomic and bibliomic data

TLDR

Metabolism is vital, and its dysfunction contributes to disease; because metabolic networks are highly interconnected, systems‑level computational approaches are required to link genotype to phenotype. The study aims to describe the reconstruction of a global human metabolic network and demonstrate its use for discovering missing information, building in‑silico models, and analyzing high‑throughput data. The network was manually reconstructed from Build 35 genome annotation and more than 50 years of bibliomic data, and the process was detailed to enable discovery, modeling, and data analysis. The reconstruction revealed numerous knowledge gaps, clarified the impact of compartmentalization and correlated reaction sets for drug target identification, enabled global assessment of metabolic states from high‑throughput data, and highlighted several applications, marking a key advance toward genome‑scale human systems biology.

Abstract

Metabolism is a vital cellular process, and its malfunction is a major contributor to human disease. Metabolic networks are complex and highly interconnected, and thus systems-level computational approaches are required to elucidate and understand metabolic genotype–phenotype relationships. We have manually reconstructed the global human metabolic network based on Build 35 of the genome annotation and a comprehensive evaluation of >50 years of legacy data (i.e., bibliomic data). Herein we describe the reconstruction process and demonstrate how the resulting genome-scale (or global) network can be used ( i ) for the discovery of missing information, ( ii ) for the formulation of an in silico model, and ( iii ) as a structured context for analyzing high-throughput biological data sets. Our comprehensive evaluation of the literature revealed many gaps in the current understanding of human metabolism that require future experimental investigation. Mathematical analysis of network structure elucidated the implications of intracellular compartmentalization and the potential use of correlated reaction sets for alternative drug target identification. Integrated analysis of high-throughput data sets within the context of the reconstruction enabled a global assessment of functional metabolic states. These results highlight some of the applications enabled by the reconstructed human metabolic network. The establishment of this network represents an important step toward genome-scale human systems biology.