Mathematical Model of Parotid Acinar Differentiation

腮腺腺泡分化的数学模型

• 批准号: 7813880
• 负责人: DOUGLAS S DARLING
• 金额: $ 38.82万
• 依托单位: UNIVERSITY OF LOUISVILLE
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-21 至 2011-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7813880
• 关键词: Acinar Cell; Address; Algorithms; American; Biological; Biological Models; Biological Response Modifier Therapy; Calibration; Cell Differentiation process; Computer Simulation; Coupled; Data; Development; Dimensions; Equation; Foundations; Functional disorder; Funding; Gene Transfer; Genes; Genetic; Goals; Hybrids; Methods; MicroRNAs; Modeling; Molecular; Muscle; Natural regeneration; Network-based; Neurons; Oral; Parotid Gland; Pathology; Pathway Analysis; Pathway interactions; Patients; Pharmaceutical Preparations; Process; Publications; Radiation therapy; Recovery; Regulation; Regulatory Pathway; Research; Reverse Transcriptase Polymerase Chain Reaction; Salivary; Salivary Glands; Serous; Signal Pathway; Signal Transduction; Sjogren' s Syndrome; Speed; Statistical Models; Symptoms; Syndrome; Testing; Tissues; Training; Transfection; United States National Institutes of Health; Validation; Xerostomia; base; cell type; computer based statistical methods; genome-wide; mathematical model; network models; new technology; novel; parent grant; parent project; parotid cell; programs; public health relevance; research study; response; restoration; salivary acinar cell; salivary cell; tool

DESCRIPTION (provided by applicant): This application is in response to NOT-OD-09-058, titled "NIH Announces the Availability of Recovery Act Funds for Competitive Revision Applications." This Competitive Revision application proposes an additional Specific Aim for the original R01 project. The long term goal of our project is to define the development of signaling networks that induce differentiation of cells into mature salivary serous acinar cells. Millions of patients suffer loss of salivary gland function due to Sjogren's syndrome or radiation therapy. Understanding the differentiation of salivary cells is a necessary step to enable the restoration of diseased or destroyed parotid salivary tissue. Our overall hypothesis for the parent project is that a mathematical model can identify key regulatory pathways that control parotid acinar cell differentiation. The goal of the existing project is to develop IPA-based mathematical and statistical models that will identify gene networks which cause terminal differentiation of parotid acinar cells. The dynamical mathematical models will serve to generate hypotheses which will be reiteratively tested, and the model will be repeatedly refined by the incorporation of new data. Both Affymetrix microarrays and miRNA arrays are being used to create biological networks with Ingenuity Pathway Analysis, and Genespring programs. These networks are used to create coupled Ordinary Differential Equation (ODE) models to describe the process of differentiation. A difficulty in any biological modeling project is finding the appropriate networks. Specific Aim #1R for this Competitive Revision proposes to develop an unbiased statistical approach to derive genetic networks from array data. The approach is based on recently developed methods for genetic network discovery based on algebraic analysis of the statistically significant interactions in the mathematical network without the need of precisely fitting all the model parameters. This novel method is based on two recent publications by the Co-PI, Dr. Rempala where it is shown to be highly robust against misclassification and capable of correctly identify the network from noisy and incomplete data. The new modeling component will be supported by generating additional microarray results to use for training and validation, as well as RT-PCR and transfection studies to validate the derived network. The additional statistical and biological results will add a new dimension to the original project, and will speed progress in defining the molecular pathways that drive cytodifferentiation of parotid acinar cells. In addition, the unbiased statistical approach to discovering biological networks will be applicable to study of other tissues and pathologies. PUBLIC HEALTH RELEVANCE: The overall goal of this research is to define the molecular mechanisms which control differentiation of cells into secretory salivary acinar cells. This addresses the needs of millions of Americans who suffer from salivary gland dysfunction due to Sj"gren's Syndrome, radiation therapy, or xerostomia due to essential medications. This research is a necessary foundation for developing new technologies such as gene transfer therapy and biologics for treating or alleviating the oral symptoms of xerostomia.

描述（由申请人提供）：本申请是对NOT-OD-09-058的回应，标题为“NIH宣布恢复法案资金可用于竞争性修订申请”。“这个竞争性修订申请为原始R 01项目提出了一个额外的具体目标。我们项目的长期目标是确定诱导细胞分化为成熟唾液腺浆液性腺泡细胞的信号网络的发展。数以百万计的患者由于干燥综合征或放射治疗而丧失唾液腺功能。了解唾液腺细胞的分化是使病变或破坏的腮腺唾液组织恢复的必要步骤。我们对母项目的总体假设是，数学模型可以识别控制腮腺腺泡细胞分化的关键调控途径。现有项目的目标是开发基于IPA的数学和统计模型，以确定导致腮腺腺泡细胞终末分化的基因网络。动态数学模型将用于产生假设，这些假设将被反复检验，模型将通过纳入新数据而反复完善。Affyphase微阵列和miRNA阵列都被用于通过Inhibitity Pathway Analysis和Genespring程序创建生物网络。这些网络用于创建耦合的常微分方程（ODE）模型来描述微分过程。任何生物建模项目的一个困难是找到合适的网络。本竞争修订版的具体目标#1R提出开发一种无偏统计方法，从阵列数据中推导出遗传网络。该方法是基于最近开发的遗传网络发现方法的基础上，代数分析的数学网络中的统计上显着的相互作用，而不需要精确地拟合所有的模型参数。这种新方法是基于Co-PI，Dr. Rempala最近的两篇出版物，其中它被证明对错误分类具有高度鲁棒性，并且能够从噪声和不完整的数据中正确识别网络。新的建模组件将通过生成额外的微阵列结果来支持，以用于训练和验证，以及RT-PCR和转染研究来验证衍生网络。额外的统计学和生物学结果将为原始项目增加一个新的维度，并将加快确定驱动腮腺腺泡细胞细胞分化的分子途径的进展。此外，发现生物网络的无偏统计方法将适用于其他组织和病理的研究。 公共卫生相关性：本研究的总体目标是确定控制细胞分化为分泌性唾液腺泡细胞的分子机制。这解决了数百万美国人的需要，他们因干燥综合征、放射治疗或因基本药物引起的口腔干燥症而患有唾液腺功能障碍。该研究为开发基因转移治疗和生物制剂等新技术治疗或缓解口腔干燥症的口腔症状奠定了必要的基础。