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的主要挑战和治疗靶标的识别是缺乏对复杂基础途径的机械理解。细胞数据的数量和多样性的巨大增加代表了寻求构建生物学相关相互作用图并客观提取特定可操作信息的研究人员的重大挑战。基于机器学习的聚类算法是初步的统计数据分析指标，但它们无法在适当的生物学环境中捕获数据。尽管已证明基于化学动力学的模型在阐明途径机制方面有效，但由于在对特定细胞内现象的动态测量中涉及的固有困难，对模型参数的准确估计严重缺乏，并且通常无法获得。另外，缺乏理性优先级和选择关键细胞内相互作用的方法（在没有动力学信息的情况下）。因此，明确需要创新的软件工具，可以在生物途径环境中对可用的微阵列数据进行定量分析，最终导致客观地识别关键的生物学相互作用，从而为更加集中的未来努力提供了方向。我们建议通过开发一个使用微阵列数据来选择和合并相关规范生物途径模型的自动化软件平台，从而解决这一挑战，从而在其生物学环境中放置显着表达的基因。分析软件将利用新型化学动力学布尔动力学算法利用微阵列表达式加权指标对网络模型中最关键的相互作用进行客观排名。在第一阶段的工作中，我们将开发一个由R库组成的软件工具，该工具可以从给定的微阵列数据集中自动生成途径模型。此外，将使用微阵列数据表达式加权度量的指标开发一种方法和相关的R库来客观地对路径模型中的关键相互作用进行对关键相互作用。将使用良好表征的脂多糖（LPS）刺激的RAW 264.7巨噬细胞系统进行演示和验证。在第二阶段，我们将扩展算法框架的范围，以在关键交互的目标排名中包括蛋白质组学和代谢组的加权，并通过添加图形用户界面（GUI）来添加工作流程改进。将使用基因沉默技术进行I阶段中确定的关键相互作用的实验验证和验证。我们还打算与商业实体建立合作伙伴关系。提议团队在系统生物学和生物信息学领域（CFDRC）和微阵列数据分析（Shawn Levy，范德比尔特大学）具有丰富的经验。 CFDRC在软件和硬件的商业化方面具有很强的记录。公共卫生相关性：最近，研究人员可用的细胞数据的数量和多样性都大大增加，这明确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，并确定新的治疗靶标。但是，当前可用的分析工具在适当的生物学环境中没有考虑数据。这项研究建议开发一个自动化的软件平台，该平台利用可用数据以自动化的方式开发和分析复杂过程的数学模型，从而确定关键细胞内过程。