SteatoNet: The First Integrated Human Metabolic Model with Multi-layered Regulation to Investigate Liver-Associated Pathologies

Current state-of-the-art mathematical models to investigate complex biological processes, in particular liver-associated pathologies, have limited expansiveness, flexibility, representation of integrated regulation and rely on the availability of detailed kinetic data. We generated the SteatoNet, a multi-pathway, multi-tissue model and in silico platform to investigate hepatic metabolism and its associated deregulations. SteatoNet is based on object-oriented modelling, an approach most commonly applied in automotive and process industries, whereby individual objects correspond to functional entities. Objects were compiled to feature two novel hepatic modelling aspects: the interaction of hepatic metabolic pathways with extra-hepatic tissues and the inclusion of transcriptional and post-transcriptional regulation. SteatoNet identification at normalised steady state circumvents the need for constraining kinetic parameters. Validation and identification of flux disturbances that have been proven experimentally in liver patients and animal models highlights the ability of SteatoNet to effectively describe biological behaviour. SteatoNet identifies crucial pathway branches (transport of glucose, lipids and ketone bodies) where changes in flux distribution drive the healthy liver towards hepatic steatosis, the primary stage of non-alcoholic fatty liver disease. Cholesterol metabolism and its transcription regulators are highlighted as novel steatosis factors. SteatoNet thus serves as an intuitive in silico platform to identify systemic changes associated with complex hepatic metabolic disorders.

当前用于研究复杂生物过程（尤其是肝脏相关病理）的顶尖数学模型，在扩展性、灵活性以及整合调控的表征能力上存在局限，且依赖详细动力学数据的可获取性。本研究构建了SteatoNet——一款多通路、多组织的模型及硅基模拟平台（in silico platform），用于探究肝脏代谢及其相关调控异常。SteatoNet基于面向对象建模（object-oriented modelling）方法，该方法最常用于汽车与流程工业，其中单个对象对应功能实体。该模型经整合各类对象后，具备两项新颖的肝脏建模特征：肝脏代谢通路与肝外组织的相互作用，以及转录和转录后调控的纳入。在归一化稳态条件下开展SteatoNet的参数识别，可免去对动力学参数进行约束的需求。对已在肝脏疾病患者及动物模型中经实验证实的通量扰动进行验证与识别，证实了SteatoNet能够有效刻画生物学行为的能力。SteatoNet可识别关键通路分支（葡萄糖、脂质与酮体的转运过程），这些分支的通量分布变化会促使健康肝脏向肝脂肪变性发展——即非酒精性脂肪性肝病的初始阶段。胆固醇代谢及其转录调控因子被鉴定为新型脂肪变性致病相关因子。因此，SteatoNet可作为一款直观的硅基模拟平台，用于识别与复杂肝脏代谢紊乱相关的系统性变化。