Analysis of Large-Scale Biochemical Systems

大型生化系统分析

• 批准号: 7103431
• 负责人: DANIEL A BEARD
• 金额: $ 28.13万
• 依托单位: MEDICAL COLLEGE OF WISCONSIN
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-08-01 至 2008-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7103431
• 关键词: acetaminophen; animal data; cell population study; computational biology; computer data analysis; computer program /software; computer system design /evaluation; disease /disorder model; drug interactions; drug metabolism; enzyme activity; ethanol; heart cell; human data; information dissemination; liver cells; mathematical model; metabolomics; method development; model design /development; muscle cells; phenotype; thermodynamics

DESCRIPTION (provided by applicant): Modern technology in computational biology enables us to quantitatively model integrated physiological systems of ever increasing size and complexity. For example, constraint-based approaches to modeling biochemical systems provide powerful computational tools for predicting metabolic capabilities and mapping gene product network interactions in whole-genome cell systems. One key to increasing the amount of information available from computer models and improving the accuracy of constraint-based modeling approaches is the identification of further physicochemical constraints under which biological systems operate. Toward that aim, we have introduced thermodynamic-based constraints on biochemical fluxes and concentration to augment the flux-balance analysis (FBA) approach. Our methodology allows us to make predictions not available from FBA alonc namely, predictions of reactant concentrations, reaction potentials, and enzyme activities. These new capabilities will allow us to introduce a wide range of new applications of constraint-based modeling, including: making predictions that are more physically realistic and biologically meaningful than are provided by FBA-based methods; making direct comparisons of computation predictions with experimental results; and predicting the regulatory and control mechanisms operating in cells. Our efforts in the development and evaluation of these computational tools will target energy metabolism in cardiac and skeletal muscle and in hepatocytes. A major goal of this proposed research program are to build and validate metabolic models of health and disease. Those models will be used for a number of applications including: (1) computational profiling of metabolic function in healthy and diseased tissues; (2) identification and testing of putative regulatory mechanisms; (3) determination of the mechanism of action of certain therapeutic agents; and (4) target identification and lead optimization. In addition, the computational tools that are developed by this research program will be disseminated through a partnership with a software company.

描述（由申请人提供）：计算生物学中的现代技术使我们能够对规模和复杂性不断增加的集成生理系统进行定量建模。例如，基于约束的生化系统建模方法为预测代谢能力和绘制全基因组细胞系统中基因产物网络相互作用提供了强大的计算工具。增加计算机模型可用的信息量和提高基于约束的建模方法的准确性的关键之一是识别生物系统运行的进一步物理化学约束。为了实现这一目标，我们引入了基于热力学的生化通量约束和 浓度以增强通量平衡分析（FBA）方法。我们的方法使我们能够做出仅 FBA 无法提供的预测，即预测反应物浓度、反应电位和酶活性。这些新功能将使我们能够引入基于约束的建模的广泛新应用，包括：做出比基于 FBA 的方法更符合物理现实和生物学意义的预测；将计算预测与实验结果进行直接比较；并预测细胞中运作的调节和控制机制。我们在开发和评估这些计算工具方面的努力将针对心脏和骨骼肌以及肝细胞中的能量代谢。该拟议研究计划的主要目标是建立和验证健康和疾病的代谢模型。这些模型将用于许多应用，包括：（1）健康和患病组织代谢功能的计算分析； (2) 识别和测试假定的监管机制； (3)确定某些治疗剂的作用机制； (4)靶标识别和先导化合物优化。此外，该研究项目开发的计算工具将通过与软件公司的合作进行传播。