Develop a Dynamic Model that Incoporates Text-mining to Reconstruct Networks

开发结合文本挖掘来重建网络的动态模型

• 批准号: 7348388
• 负责人: CHRISTINA CHAN
• 金额: $ 25.05万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-02-01 至 2011-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7348388
• 关键词: Algorithms; Animals; Cell Culture System; Cell physiology; Cells; Computer Simulation; Condition; Cultured Cells; Data; Diet; Disease; Drug Delivery Systems; Environment; Fatty acid glycerol esters; Feedback; Gene Expression; Gene Silencing; Genes; Inflammation; Knowledge; Liver; Measures; Metabolic; Modeling; Numbers; Ontology; Pathway interactions; Phenotype; RNA Interference; Rattus; Regulator Genes; Series; Structure; Text; Time; Transfection; Triglycerides; computer based statistical methods; cost effectiveness; desire; feeding; improved; in vivo; nonalcoholic steatohepatitis; novel strategies; reconstruction; research study; response; text searching; Algorithms; Animals; Cell Culture System; Cell physiology; Cells; Computer Simulation; Condition; Cultured Cells; Data; Diet; Disease; Drug Delivery Systems; Environment; Fatty acid glycerol esters; Feedback; Gene Expression; Gene Silencing; Genes; Inflammation; Knowledge; Liver; Measures; Metabolic; Modeling; Numbers; Ontology; Pathway interactions; Phenotype; RNA Interference; Rattus; Regulator Genes; Series; Structure; Text; Time; Transfection; Triglycerides; computer based statistical methods; cost effectiveness; desire; feeding; improved; in vivo; nonalcoholic steatohepatitis; novel strategies; reconstruction; research study; response; text searching

DESCRIPTION (provided by applicant): Improved understanding of disease mechanisms and drug target identification requires better understanding of how diseases alter cellular processes from the healthy state. Our group has developed an integrative pathway search algorithm that reconstructs networks of active pathways from gene expression and phenotypic profiles. Preliminary studies illustrate that this framework is able to reconstruct networks that include those pathways that should be altered, and how they should be altered, to obtain a desired phenotype. Nevertheless, the current framework may not fully capture transients, such as cycles and feedback loops. Furthermore, cells continuously reprogram gene regulatory networks as they sense changes in their environment. To understand how cells are regulated in response to environmental alterations, time series (i.e., dynamic) data are required. Correspondingly, a dynamic model is required to uncover the mechanisms from time series data. We hypothesize that incorporating domain knowledge and metabolic data into a dynamic model would enhance the accuracy of the genes chosen and in turn improve the prediction of the reconstructed networks. Unlike previous studies that have focused on using the gene ontology information, we propose to incorporate domain knowledge retrieved from the free text. This is significant because a large portion of the genes do not have gene ontological keywords. Additionally, it is often difficult to assess the accuracy of the network structures that have been inferred from experimental data because the underlying "true" regulatory network is unknown or unavailable a priori. Therefore, one needs to have a known network structure that can be used to optimize and evaluate the modeling frameworks. Once so optimized, the static and dynamic models will be applied to an experimental cell culture system, which has a perturbed (transfected or silenced) gene, and assessed as to how well each model predicts the resulting, measured phenotypic responses. The cost-effectiveness of the cell culture, in contrast to in vivo animal studies, allows us to establish, with experimental data, which model produces predictions of greater confidence. Having established which model is more predictive, we will apply that model to rats that are maintained on high fat diets, so as to identify the pathways that could be altered to reduce triglyceride storage (steatosis) and inflammation in the livers of these rats. The findings could have implications for identifying potential therapies for steatosis and, perhaps, even non-alcoholic steatohepatitis (NASH). The objectives will be achieved through the following aims: 1) Develop a novel approach that incorporates domain knowledge retrieved from the free text as well as gene expression data to predict cellular or phenotypic responses. 2) Develop an optimized dynamic Bayesian Network to infer gene regulatory networks from time series data. 3) Experimentally validate the model predictions for the cell culture system. 4) Characterize the livers from rats fed high fat vs. normal diets.

描述（由申请人提供）：提高对疾病机制和药物靶标识别的理解需要更好地理解疾病如何改变健康状态的细胞过程。我们的小组已经开发了一种集成途径搜索算法，该算法从基因表达和表型曲线中重建了活动途径的网络。初步研究表明，该框架能够重建包括应改变的途径以及应如何改变的网络，以获得所需的表型。然而，当前的框架可能无法完全捕获瞬态，例如循环和反馈循环。此外，细胞在感知环境变化时不断重新编程基因调节网络。为了了解如何根据环境改变来调节细胞，需要时间序列（即动态）数据。相应地，需要一个动态模型来发现时间序列数据中的机制。我们假设将域知识和代谢数据纳入动态模型将提高所选基因的准确性，进而改善重建网络的预测。与以前专注于使用基因本体信息的研究不同，我们建议合并从自由文本中检索的领域知识。这很重要，因为大部分基因没有基因本体论关键词。此外，通常很难评估从实验数据中推断出的网络结构的准确性，因为基本的“真”监管网络是未知或不可用的先验性。因此，需要具有可用于优化和评估建模框架的已知网络结构。一旦如此优化，静态和动态模型将应用于具有扰动（转染或沉默的）基因的实验细胞培养系统，并评估了每个模型如何预测所产生的，测量的表型响应。与体内动物研究相比，细胞培养的成本效益使我们能够通过实验数据建立模型，从而产生更大的置信度的预测。确定哪种模型更具预测性后，我们将将该模型应用于维持高脂肪饮食的大鼠，以确定可以改变的途径以减少这些大鼠肝脏中的甘油三酸酯储存（脂肪症）和炎症。这些发现可能对鉴定脂肪变性的潜在疗法有影响，甚至可能是非酒精性脂肪性肝炎（NASH）。目标将通过以下目的实现：1）开发一种新的方法，该方法结合了从自由文本中检索到的领域知识以及基因表达数据以预测细胞或表型响应。 2）开发一个优化的动态贝叶斯网络，从时间序列数据推断基因调节网络。 3）实验验证细胞培养系统的模型预测。 4）表征来自喂养高脂饮食的大鼠的肝脏。