Comprehensive molecular‐level models of human metabolism have been generated on a cellular level. However, models of whole‐body metabolism have not been established as they require new methodological approaches to integrate molecular and physiological data. We developed a new metabolic network reconstruction approach that used organ‐specific information from literature and omics data to generate two sex‐specific whole‐body metabolic (WBM ) reconstructions. These reconstructions capture the metabolism of 26 organs and six blood cell types. Each WBM reconstruction represents whole‐body organ‐resolved metabolism with over 80,000 biochemical reactions in an anatomically and physiologically consistent manner. We parameterized the WBM reconstructions with physiological, dietary, and metabolomic data. The resulting WBM models could recapitulate known inter‐organ metabolic cycles and energy use. We also illustrate that the WBM models can predict known biomarkers of inherited metabolic diseases in different biofluids. Predictions of basal metabolic rates, by WBM models personalized with physiological data, outperformed current phenomenological models. Finally, integrating microbiome data allowed the exploration of host–microbiome co‐metabolism. Overall, the WBM reconstructions, and their derived computational models, represent an important step toward virtual physiological humans.

中文翻译：

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个性化的全身模型整合了新陈代谢，生理学和肠道微生物组。

人类新陈代谢的分子水平综合模型已在细胞水平上产生。但是，尚未建立全身代谢模型，因为它们需要新的方法论方法来整合分子和生理数据。我们开发了一种新的代谢网络重建方法，该方法使用来自文献和组学数据的器官特定信息来生成两个性别特定的全身代谢（WBM）重建。这些重建捕获了26种器官和6种血细胞类型的新陈代谢。每个WBM重建体均以解剖学和生理学上一致的方式代表着超过8万次生化反应的全身器官分解代谢。我们使用生理，饮食和代谢组学数据对WBM重建进行参数化。由此产生的WBM模型可以概括已知的器官间代谢周期和能量消耗。我们还说明，WBM模型可以预测不同生物流体中遗传性代谢疾病的已知生物标记。通过具有生理数据个性化的WBM模型对基础代谢率的预测优于目前的现象学模型。最后，整合微生物组数据可以探索宿主-微生物组的新陈代谢。总体而言，WBM重建及其派生的计算模型代表了迈向虚拟生理人类的重要一步。优于目前的现象学模型。最后，整合微生物组数据可以探索宿主-微生物组的新陈代谢。总体而言，WBM重建及其派生的计算模型代表了迈向虚拟生理人类的重要一步。优于目前的现象学模型。最后，整合微生物组数据可以探索宿主-微生物组的新陈代谢。总体而言，WBM重建及其派生的计算模型代表了迈向虚拟生理人类的重要一步。