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）：此申请响应于NOT-OD-09-058，标题为“ NIH宣布竞争性修订申请的恢复ACT资金可用性”。该竞争性修订申请提出了针对原始R01项目的其他特定目标。我们项目的长期目标是定义诱导细胞分化为成熟唾液浆液腺细胞细胞的信号网络的发展。数以百万计的患者因sjogren综合征或放射治疗而导致唾液腺功能丧失。了解唾液细胞的分化是使患病或破坏的腮腺唾液组织恢复的必要步骤。我们对父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节途径。现有项目的目的是开发基于IPA的数学和统计模型，这些模型将识别引起腮腺腺泡细胞终末分化的基因网络。动力学数学模型将用于生成将重申测试的假设，并通过合并新数据来重复改进该模型。 Affymetrix微阵列和miRNA阵列都用于创建具有Ingenuity途径分析和基因弹簧程序的生物网络。这些网络用于创建耦合的普通微分方程（ODE）模型来描述分化过程。任何生物建模项目的困难是找到适当的网络。此竞争性修订的特定目标＃1R建议开发一种无偏的统计方法，从数组数据中得出遗传网络。该方法基于最近开发的遗传网络发现方法，基于对数学网络中统计上显着相互作用的代数分析，而无需精确拟合所有模型参数。这种新颖的方法是基于Co-Pi雷姆帕拉博士的两份最新出版物，在该出版物中，它被证明对错误分类非常强大，并能够从嘈杂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用于培训和验证的其他微阵列结果以及RT-PCR和转染研究来支持，以验证派生的网络。附加的统计和生物学结果将为原始项目增加一个新的维度，并将加快定义驱动腮腺腺泡细胞细胞二分化的分子途径方面的进步。此外，发现生物网络的无偏统计方法将适用于研究其他组织和病理。 公共卫生相关性：这项研究的总体目标是定义将细胞分化为分泌唾液腺泡细胞的分子机制。这解决了由于SJ“ Gren's综合征，放射疗法或静脉疾病，由于基本药物而导致的数百万美国人的需求。这项研究是开发基因转移疗法和生物学等新技术的必要基础，例如治疗或减轻Xerostomia的口服症状。