Computational reconstruction of tissue‐specific metabolic models: application to human liver metabolism

TLDR

The advent of the first generic stoichiometric model of human metabolism has advanced computational studies of human metabolism. This study introduces a rapid algorithm for reconstructing tissue‑specific genome‑scale metabolic models, enabling the creation of such models for other tissues and organisms. The algorithm integrates literature, transcriptomic, proteomic, metabolomic, and phenotypic data to generate a tissue‑specific model, which was applied to build and cross‑validate the first genome‑scale hepatic model. The hepatic model’s flux predictions correlate with experimental measurements and outperform the generic model (0.67 vs 0.46 accuracy), also yielding higher accuracy for biomarker predictions in genetic metabolic disorders (0.67 vs 0.59).

Abstract

The computational study of human metabolism has been advanced with the advent of the first generic (non‐tissue specific) stoichiometric model of human metabolism. In this study, we present a new algorithm for rapid reconstruction of tissue‐specific genome‐scale models of human metabolism. The algorithm generates a tissue‐specific model from the generic human model by integrating a variety of tissue‐specific molecular data sources, including literature‐based knowledge, transcriptomic, proteomic, metabolomic and phenotypic data. Applying the algorithm, we constructed the first genome‐scale stoichiometric model of hepatic metabolism. The model is verified using standard cross‐validation procedures, and through its ability to carry out hepatic metabolic functions. The model's flux predictions correlate with flux measurements across a variety of hormonal and dietary conditions, and improve upon the predictive performance obtained using the original, generic human model (prediction accuracy of 0.67 versus 0.46). Finally, the model better predicts biomarker changes in genetic metabolic disorders than the generic human model (accuracy of 0.67 versus 0.59). The approach presented can be used to construct other human tissue‐specific models, and be applied to other organisms.