Crohn’s disease (CD) is associated with an ecological imbalance of the intestinal microbiota, consisting of hundreds of species. The underlying complexity as well as individual differences between patients contributes to the difficulty to define a standardized treatment. Computational modeling can systematically investigate metabolic interactions between gut microbes to unravel mechanistic insights. In this study, we integrated metagenomic data of CD patients and healthy controls with genome-scale metabolic models into personalized in silico microbiotas. We predicted short chain fatty acid (SFCA) levels for patients and controls, which were overall congruent with experimental findings. As an emergent property, low concentrations of SCFA were predicted for CD patients and the SCFA signatures were unique to each patient. Consequently, we suggest personalized dietary treatments that could improve each patient’s SCFA levels. The underlying modeling approach could aid clinical practice to find dietary treatment and guide recovery by rationally proposing food aliments.

克罗恩氏病（CD）与肠道菌群的生态失衡有关，肠道菌群由数百种物种组成。潜在的复杂性以及患者之间的个体差异加剧了定义标准化治疗的难度。计算模型可以系统地研究肠道微生物之间的代谢相互作用，以揭示机理的见解。在这项研究中，我们将CD患者和健康对照组的宏基因组学数据与基因组规模的代谢模型整合到个性化的计算机微生物群中。我们预测了患者和对照组的短链脂肪酸（SFCA）水平，这些水平总体上与实验结果一致。作为一种新兴特性，预计CD患者的SCFA浓度低，并且SCFA签名对于每个患者都是唯一的。所以，我们建议采用个性化饮食疗法，以改善每位患者的SCFA水平。基本的建模方法可以帮助临床实践找到饮食疗法，并通过合理地提出食物饮食来指导恢复。