Dynamic Flux Balance Analysis of Hepatic Lipid Metabolism: Cell and Organ Studies

肝脏脂质代谢的动态通量平衡分析：细胞和器官研究

• 批准号: 1067323
• 负责人: Howard Matthew
• 金额: $ 54.99万
• 依托单位: Wayne State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1067323&HistoricalAwards=false
• 关键词: Dynamic; Flux; Balance; Analysis; Hepatic

Therapy development for many metabolic diseases and disturbances in hampered by an incomplete understanding of the detailed mechanisms and system objectives. Metabolic diseases of the liver are particularly challenging due to the intrinsic complexity of this organ and its role in maintaining blood levels of nutrients for the entire body. In particular, hepatic steatosis, or fatty liver, is a condition estimated to affect 15 to 20% of the US population. Its key feature is an accumulation of fat droplets within hepatocytes, followed by a degradation of hepatocyte function. Interest in the causal mechanisms of fatty liver has been driven by the realization that fat accumulation in hepatocytes is often a precursor to more serious liver problems. In addition, the risk of more serious problems is proportional to the level of fat accumulation. Unfortunately, identification of the causal mechanisms is hampered by the complexity of the liver metabolic network, and the variety of metabolic disturbances that may lead to steatosis. Thus, a quantitative understanding of the lipid metabolism network in liver is clearly necessary for identification of the causes and developing therapeutic approaches for steatosis.The long-term goals of this research are to develop a mathematical model of liver metabolism and to employ it for analysis and computational optimization of interventions for treating steatosis. The objective of this project is to obtain experimental data for training and testing a system-scale mathematical model of hepatocyte fat metabolism. This model is expected to enable reasonably accurate prediction of the responses of hepatocytes cells to a variety of disturbances. The central hypotheses of the proposal are: (A) the cellular control of hepatic fat metabolism behaves as an optimal feedback- controller and, (B) a mathematical model based on the optimal control of metabolism can predict the responses of cultured hepatocytes to stimuli. The proposed model can be used either for identification of pathological mechanisms leading to fat accumulation, or for testing of alternate hypotheses regarding methods for manipulating specific metabolic targets to reverse steatosis and re-establish homeostasis. The Specific Aims are: (1) To collect metabolite data for cultured hepatocytes exposed to lipid-disturbing stimuli; and (2) To model the mechanisms of lipid metabolic control in cultured hepatocyte and ex vivo livers. The output of the proposed work will be a liver metabolism model suitable for analysis and prediction of steatosis outcomes, with its accuracy and capabilities assessed. In addition, the modeling framework developed is likely to be applicable to other tissues and metabolic diseases involving multiple pathways.Broader ImpactsThe proposed work seeks to extend optimization-based mathematical modeling techniques to applications in mammalian physiology. As such, it is expected to stimulate application of these sophisticated modeling principles to other complex tissues and diseases. In the longer term, successful deployment of these predictive models will contribute powerful tools to the field of personalized medicine using a systems biology approach. The results of the research will be incorporated into lectures on metabolic modeling and cell culture bioreactors, in graduate courses currently taught by the Investigators. Specifically, ?Tissue Engineering? and ?Advanced Mathematics? will benefit from research-derived, learning modules and project activities. A new senior/graduate course entitled ?Systems Biology for Biomedical Interventions? is also planned, and will involve at least one graduate student in teaching the modeling approaches. Two graduate students and two undergraduate students (including one underrepresented minority student per year will be trained in the research methods. In the third year, one graduate student will be trained in animal surgery and organ perfusion methods by our collaborators at Harvard Medical School. Additional impact will be achieved through the involvement of two underrepresented minority high school students in summer research. The research results will be disseminated to the scientific community by presentations at annual conferences such as those sponsored by AICHE, BMES and ACS as well as publication of manuscripts in high impact journals.

许多代谢性疾病和紊乱的治疗开发受到对详细机制和系统目标的不完全理解的阻碍。肝脏的代谢疾病特别具有挑战性，因为这个器官的内在复杂性及其在维持整个身体的血液营养水平方面的作用。特别是，肝性脂肪变性或脂肪肝是一种估计影响15%至20%的美国人口的病症。其主要特征是肝细胞内脂肪滴的积聚，随后是肝细胞功能的降解。对脂肪肝的因果机制的兴趣是由于认识到肝细胞中的脂肪积累通常是更严重肝脏问题的前兆。此外，更严重问题的风险与脂肪积累的水平成正比。不幸的是，因果机制的识别受到肝脏代谢网络的复杂性和可能导致脂肪变性的各种代谢紊乱的阻碍。因此，在肝脏中的脂质代谢网络的定量的了解，显然是必要的原因和开发治疗方法的识别脂肪变性。本研究的长期目标是开发一个数学模型的肝脏代谢，并采用它的分析和计算优化的干预措施，治疗脂肪变性。本研究的目的是获得实验数据，用于训练和测试肝细胞脂肪代谢的系统级数学模型。该模型有望合理准确地预测肝细胞对各种干扰的反应。该建议的中心假设是：（A）肝脂肪代谢的细胞控制表现为最佳反馈控制器，（B）基于代谢最佳控制的数学模型可以预测培养的肝细胞对刺激的反应。所提出的模型可用于鉴定导致脂肪积累的病理机制，或用于测试关于操纵特定代谢靶点以逆转脂肪变性和重建稳态的方法的替代假设。具体目标是：（1）收集暴露于脂质干扰刺激的培养肝细胞的代谢物数据;和（2）模拟培养肝细胞和离体肝脏中脂质代谢控制的机制。拟议工作的结果将是一个适合分析和预测脂肪变性结果的肝脏代谢模型，并对其准确性和能力进行评估。此外，开发的建模框架可能适用于其他组织和代谢疾病，涉及多个pathways.Broader ImpactsThe建议的工作旨在扩展优化为基础的数学建模技术在哺乳动物生理学中的应用。因此，预计将刺激这些复杂的建模原理应用于其他复杂组织和疾病。从长远来看，这些预测模型的成功部署将为使用系统生物学方法的个性化医疗领域提供强大的工具。研究结果将被纳入代谢建模和细胞培养生物反应器的讲座中，目前由研究人员教授的研究生课程。具体来说，？组织工程学？然后呢？高等数学？将受益于研究衍生的学习模块和项目活动。一个新的高级/研究生课程题为？生物医学干预的系统生物学也计划，并将涉及至少一名研究生在教学建模方法。每年将有两名研究生和两名本科生（包括一名代表性不足的少数民族学生）接受研究方法培训。在第三年，一名研究生将在哈佛医学院接受我们的合作者在动物外科和器官灌注方法方面的培训。通过让两名代表性不足的少数民族高中学生参与暑期研究，将产生更多的影响。研究结果将通过在年度会议上的介绍传播给科学界，如由AICHE，BMES和ACS赞助的会议，以及在高影响力期刊上发表手稿。