A Simulation Tool to Enable Identification of Critical Network Interactions Using

一种能够识别关键网络交互的仿真工具

• 批准号: 7482734
• 负责人: Jerry W Jenkins
• 金额: $ 9.96万
• 依托单位: CFD RESEARCH CORPORATION
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-10 至 2010-04-10
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7482734
• 关键词: Address; Advanced Development; Algorithms; Area; Bioinformatics; Biological; Biological Markers; Complex; Computer Analysis; Computer software; Coupled; Data; Data Analyses; Data Set; Databases; Disease; Future; Gene Silencing; Generations; Genes; Genomics; Kinetics; Lead; Libraries; Lipopolysaccharides; Machine Learning; Maps; Measurement; Methodology; Methods; Metric; Microarray Analysis; Modeling; Nature; Pathway Analysis; Pathway interactions; Phase; Process; Proteomics; Public Health; Research; Research Personnel; Software Tools; Statistical Data Interpretation; System; Systems Biology; Techniques; Title; Universities; Urination; Validation; Weight; base; chemical kinetics; commercialization; editorial; experience; graphical user interface; innovation; macrophage; mathematical model; metabolomics; network models; novel; novel therapeutics; simulation; therapeutic target; tool

DESCRIPTION (provided by applicant): One of the main challenges in the discovery of intracellular biomarkers and identification of therapeutic targets is the lack of a mechanistic understanding of the complex underlying pathways. The tremendous increase in both the quantity and diversity of cellular data represents a significant challenge to researchers seeking to construct biologically relevant interaction maps, and objectively extract specific actionable information. Machine learning based clustering algorithms serve as a preliminary statistical data analysis metric, but they fail to capture the data in the proper biological context. While chemical kinetics based models have proved to be effective in elucidating the pathway mechanisms, accurate estimates for the model parameters are severely lacking and are often impossible to obtain owing to the inherent difficulties involved in making dynamic measurements of specific intracellular phenomena. Additionally, methods for rational prioritization and selection of critical intracellular interactions (in the absence of kinetic information) are sorely lacking. Therefore, there is a clear need for innovative software tools that enable quantitative analysis of available microarray data in a biological pathway context, ultimately leading to the objective identification of critical biological interactions, providing a direction for more focused future efforts. We propose to address this challenge by developing an automated software platform that utilizes microarray data to select and merge relevant canonical biological pathway models thereby placing significantly expressed genes in their biological context. The analysis software will utilize a microarray expression-weighted metric to objectively rank the most critical interactions within the network model using a novel chemical kinetics-free Boolean dynamics algorithm. In the Phase I effort, we will develop a software tool composed of an R library that enables the automated generation of a pathway model from a given microarray dataset. Additionally, a methodology, and associated R library will be developed to objectively rank critical interactions in the pathway model, using a microarray data expression-weighted metric. Demonstration and validation of proposed algorithm will be carried out using a well characterized lipopolysaccharide (LPS) stimulated RAW 264.7 macrophage system. In Phase II, we will extend the scope of the algorithmic framework to include proteomic and metabolomic weighting in the objective ranking of critical interactions, and add workflow improvements through the addition of a graphical user interface (GUI). Experimental verification and validation of critical interactions identified in Phase I will be carried out using gene-silencing techniques. We also intend to establish collaborative partnerships with commercial entities. The proposing team has extensive experience in the areas of systems biology and bioinformatics (CFDRC) and microarray data analysis (Shawn Levy, University of Vanderbilt). CFDRC has a strong track record in the commercialization of software and hardware. PUBLIC HEALTH RELEVANCE: Recently, there has been a tremendous increase in both the amount and diversity of cellular data available to researchers, representing a clear need for the development of advanced computational analysis software to enable the discovery of biomarkers of disease states, and identification of new therapeutic targets. However, currently available analysis tools do not consider the data in a proper biological context. This research proposes to develop an automated software platform that utilizes available data to develop and analyze mathematical models of complex processes in an automated fashion, resulting in the identification of critical intracellular processes.

描述（由申请人提供）：发现细胞内生物标志物和识别治疗靶点的主要挑战之一是缺乏对复杂的潜在途径的机械理解。细胞数据的数量和多样性的巨大增加对寻求构建生物学相关的相互作用图并客观地提取特定的可操作信息的研究人员来说是一个重大挑战。基于机器学习的聚类算法可作为初步的统计数据分析指标，但它们无法在适当的生物学背景下捕获数据。虽然基于化学动力学的模型已被证明可以有效地阐明途径机制，但由于对特定细胞内现象进行动态测量所涉及的固有困难，模型参数的准确估计严重缺乏并且通常不可能获得。此外，严重缺乏合理优先排序和选择关键细胞内相互作用（在缺乏动力学信息的情况下）的方法。因此，显然需要创新的软件工具，能够在生物途径背景下对可用的微阵列数据进行定量分析，最终客观地识别关键的生物相互作用，为未来更有针对性的努力提供方向。我们建议通过开发一个自动化软件平台来应对这一挑战，该平台利用微阵列数据来选择和合并相关的规范生物途径模型，从而将显着表达的基因置于其生物背景中。该分析软件将利用微阵列表达加权指标，使用新颖的无化学动力学布尔动力学算法，客观地对网络模型内最关键的相互作用进行排名。在第一阶段的工作中，我们将开发一种由 R 库组成的软件工具，能够从给定的微阵列数据集自动生成通路模型。此外，还将开发一种方法和相关的 R 库，使用微阵列数据表达加权指标对通路模型中的关键相互作用进行客观排名。所提出的算法的演示和验证将使用经过充分表征的脂多糖 (LPS) 刺激的 RAW 264.7 巨噬细胞系统进行。在第二阶段，我们将扩展算法框架的范围，将蛋白质组学和代谢组学权重纳入关键相互作用的客观排名中，并通过添加图形用户界面（GUI）来改进工作流程。第一阶段确定的关键相互作用的实验验证和验证将使用基因沉默技术进行。我们还打算与商业实体建立合作伙伴关系。提案团队在系统生物学和生物信息学（CFDRC）以及微阵列数据分析（Shawn Levy，范德比尔特大学）领域拥有丰富的经验。 CFDRC 在软件和硬件商业化方面拥有良好的记录。公共健康相关性：最近，研究人员可用的细胞数据的数量和多样性都大幅增加，这表明明显需要开发先进的计算分析软件，以便能够发现疾病状态的生物标志物，并识别新的治疗靶点。然而，当前可用的分析工具并未在适当的生物学背景下考虑数据。这项研究建议开发一个自动化软件平台，利用可用数据以自动化方式开发和分析复杂过程的数学模型，从而识别关键的细胞内过程。