Computational Methods for Functional Genomic Discovery from Gene Knockout Studies

基因敲除研究中功能基因组发现的计算方法

• 批准号: 7475489
• 负责人: Kenneth L Drake
• 金额: $ 15.85万
• 依托单位: SERALOGIX
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-04 至 2010-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7475489
• 关键词: Address; Algorithms; Animals; Area; Biological; Biological Models; Biology of Aging; Biomedical Research; Cell Cycle; Collection; Communities; Complex; Computational Technique; Computing Methodologies; Congenital Abnormality; Data; Data Set; Databases; Developmental Biology; Diagnostic; Disease; Elements; Embryo; Embryonic Heart; Evaluation; Folate; Game Theory; Gene Expression; Gene Proteins; Generations; Genes; Genetic; Genetic Programming; Genomics; Goals; Health; Hereditary Disease; Institutes; Intervention; Investigation; Knock-out; Knockout Mice; Knowledge; Lead; Learning; Literature; Medical; Medicine; Messenger RNA; Methodology; Methods; Metric; Modeling; Molecular; Molecular Target; Mus; Names; Ontology; Pathogenesis; Pathway interactions; Pattern Recognition; Phase; Probability; Protein Analysis; Proteins; Proteomics; Public Health; Publishing; Receptor Signaling; Recurrence; Regulation; Research; Research Personnel; Resources; Signal Pathway; Signal Transduction; Software Tools; Source; Statistical Methods; Structure; System; Testing; Texas; Time; Tissues; Today; Training; Validation; base; biological research; comparative; computer based statistical methods; computerized tools; concept; data modeling; design; drug development; embryonic stem cell; folate-binding protein; functional genomics; gene discovery; gene interaction; improved; innovation; knockout animal; knockout gene; man; mathematical model; mouse genome; null mutation; protein expression; prototype; research study; response; sound; stem; tool

DESCRIPTION (provided by applicant): The overall Phase I objective is to prototype a new computational tool to assess the feasibility of identifying and predicting gene functional and interactive relationships from gene expression data obtained from multi- comparative gene knockout studies. NIH's Knockout Mouse Project is an initiative to generate a public resource of mouse embryonic stem (ES) cells containing a null mutation in every gene in the mouse genome, important for the study of diseases and for deciphering the complexity of biological systems of mice and ultimately in man. It is anticipated that a new generation of comparative knockout studies with a biological will emerge in all areas of biomedical research. Having new computational methods for identifying and deciphering genetically regulated response (e.g. signaling cascades) will significantly aid in advancing our understanding of the molecular targets and mechanisms of many diseases and will be extremely important for making medical breakthroughs, especially for the safe and effective development of drugs and diagnostics. Today, researchers are hindered by the tremendous volumes of data generated from knockout investigations. Seralogix plans to build upon the capabilities of our existing Biosystem Analysis Framework (BAF) that is comprised of a suite of integrated analysis and mathematical modeling tools for gene and protein analysis. Our core tools are based on the statistical power of Dynamic Bayesian Networks (DBNs) that is built on sound statistical methods that allow us to combine prior knowledge with empirical time-course data for modeling, pattern recognition and genetic network inference. Our innovation, proposed herein, is to integrate the }multi- perturbation Shapley Value Analysis (MSA) method with our DBNs for improved genetic network discovery. MSA is based on concepts from game theory that we will utilized to identify the relationships/importance of each element (genes) in a system with respect to all other elements. We hypothesize that MSA integrated with our DBN inference engine in conjunction with time-course gene expression data from multiple gene KO experiments will result in improved genetic interactive discovery leading to a more robust mathematical/functional model system. If successful, the inclusion of MSA into Seralogix's suite of analysis and modeling tools will provide an important new tool for genetic functional and interactive relationship discovery. Seralogix believes that such a new tool will have significant commercial potential and will make a major contribution to the scientific community at large. The Phase I goals for our proposed tool will be to: 1) design and implement our DBN/MSA multi-conditional KO comparative methodology for discovery of underlying genetic networks; and 2) demonstrate proof-of-feasibility of the DBN/MSA methodology on mice gene KO expression data for folate receptor and related signaling pathways provided to us from the Texas Institute of Genomic Medicine (TIGM) as part of their ongoing birth defect research. It is one of the ultimate goals for modern biological research to fully elucidate the intricate interplays and the regulations of the molecular determinants that control health and disease, to name a few, cell cycling, developmental biology, aging, and the progressive and recurrent pathogenesis of complex diseases. Having new computational methods (software tools) for identifying and deciphering genetically regulated response (e.g. signaling cascades) will significantly aid in advancing our understanding of the molecular targets and mechanisms of many diseases of high public health concern. The discovery of underlying genetic function and relationships will be extremely important for making medical breakthroughs, especially for the safe and effective development of drugs and diagnostics. Today, researchers are hindered by the tremendous volumes of gene/protein expression data generated from knockout investigations. Computational tools that transform these volumes of raw genomic/proteomic data to actionable knowledge via mathematical modeling will help guide and accelerate researchers' investigations of genetic disorder and identifying targets of intervention and treatment.

描述（由申请人提供）：整体I期目标是原型制作一种新的计算工具，以评估从从多比较基因敲除研究获得的基因表达数据中识别和预测基因功能和交互关系的可行性。 NIH的基因敲除小鼠项目是一项主动性，旨在产生小鼠胚胎茎（ES）细胞的公共资源，其中包含小鼠基因组中每个基因的无效突变，对于研究疾病的研究和破译小鼠生物系统的复杂性和人类最终在人类中的复杂性很重要。可以预计，在生物医学研究的所有领域，新一代具有生物学的比较敲除研究将出现。拥有新的计算方法来识别和解密遗传调节的反应（例如信号级联）将大大有助于促进我们对许多疾病的分子靶标和机制的理解，并且对于做出医疗突破非常重要，尤其是对于药物和诊断的安全有效发育。如今，研究人员受到淘汰调查产生的大量数据的阻碍。 Seralogix计划基于我们现有的生物系统分析框架（BAF）的功能，该框架由一套集成分析和用于基因和蛋白质分析的数学建模工具组成。我们的核心工具基于动态贝叶斯网络（DBN）的统计能力，该网络（DBN）建立在合理的统计方法上，使我们能够将先验知识与经验性的时间课程数据相结合，用于建模，模式识别和遗传网络推断。本文提出的我们的创新是将多扰动Shapley值分析（MSA）方法与我们的DBN集成，以改善遗传网络发现。 MSA基于游戏理论的概念，我们将利用与所有其他元素相对于系统中每个元素（基因）的关系/重要性。我们假设MSA与我们的DBN推理引擎结合了来自多个基因KO实验的时间表基因表达数据的结合，将导致改进的遗传互动发现，从而导致更强大的数学/功能模型系统。如果成功，将MSA纳入Seralogix的分析和建模工具套件将为遗传功能和互动关系发现提供重要的新工具。 Seralogix认为，这种新工具将具有巨大的商业潜力，并将为整个科学界做出重大贡献。我们提出的工具的I阶段目标将是：1）设计和实施我们的DBN/MSA多条件KO比较方法，以发现潜在的遗传网络； 2）证明了DBN/MSA方法在小鼠基因KO表达数据上的叶酸受体和相关信号传导途径的可行性证明，这是他们正在进行的出生缺陷研究的一部分。它是现代生物学研究的最终目标之一，可以完全阐明控制健康和疾病的分子决定因素的复杂相互作用和法规，仅举几例，包括细胞循环，发育生物学，衰老以及复杂疾病的渐进性和反复发病。拥有新的计算方法（软件工具）来识别和解密遗传调节的反应（例如信号级联）将大大有助于促进我们对许多高公共健康疾病的分子靶标和机制的理解。发现潜在的遗传功能和关系对于做出医疗突破非常重要，尤其是对于药物和诊断的安全开发而言。如今，研究人员受到敲除研究产生的基因/蛋白质表达数据的巨大影响。通过数学建模将这些原始基因组/蛋白质组学数据转换为可行知识的计算工具将有助于指导和加速研究人员对遗传疾病的研究，并确定干预和治疗的目标。