Metabolic modeling enables the study of human metabolism in healthy and in diseased conditions, e.g., the prediction of new drug targets and biomarkers for metabolic diseases. To accurately describe blood and urine metabolite dynamics, the integration of multiple metabolically active tissues is necessary. We developed a dynamic multi-tissue model, which recapitulates key properties of human metabolism at the molecular and physiological level based on the integration of transcriptomics data. It enables the simulation of the dynamics of intra-cellular and extra-cellular metabolites at the genome scale. The predictive capacity of the model is shown through the accurate simulation of different healthy conditions (i.e., during fasting, while consuming meals or during exercise), and the prediction of biomarkers for a set of Inborn Errors of Metabolism with a precision of 83%. This novel approach is useful to prioritize new biomarkers for many metabolic diseases, as well as for the integration of various types of personal omics data, towards the personalized analysis of blood and urine metabolites.

代谢建模能够研究健康和患病条件下的人体代谢，例如预测代谢疾病的新药物靶点和生物标志物。为了准确描述血液和尿液代谢动力学，需要整合多个代谢活跃的组织。我们开发了一种动态多组织模型，该模型基于转录组数据的整合，在分子和生理水平上概括了人类新陈代谢的关键特性。它能够在基因组规模上模拟细胞内和细胞外代谢物的动态。该模型的预测能力通过准确模拟不同的健康状况（即禁食期间、进餐期间或运动期间）以及对一组先天性代谢错误的生物标志物的预测来显示，精确度为83%。这种新颖的方法可用于优先考虑许多代谢疾病的新生物标志物，以及整合各种类型的个人组学数据，以实现血液和尿液代谢物的个性化分析。