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: 7999392
• 负责人: Kenneth L Drake
• 金额: $ 53.8万
• 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 2012-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7999392
• 关键词: Adopted; Algorithms; Area; Biological; Biology of Aging; Biomedical Research; Cell Cycle; Collection; Communities; Complex; Computational Technique; Computing Methodologies; Congenital Abnormality; Coupled; Data; Databases; Development; Developmental Biology; Diagnostic; Disease; Embryo; Evaluation; Future; Gene Expression; Gene Proteins; Generations; Genes; Genetic; Genomics; Genotype; Goals; Health; Hereditary Disease; Imagery; In Vitro; Information Resources; Institutes; Intervention; Investigation; Knock-out; Knockout Mice; Knowledge; Knowledge Extraction; Learning; Literature; Medical; Medicine; Methods; Modeling; Molecular; Molecular Target; Mus; Names; Neural tube; Pathogenesis; Pattern; Pattern Recognition; Performance; Phase; Phenotype; Planning Techniques; Process; Proteomics; Provider; Public Health; Publishing; Recurrence; Regulation; Regulator Genes; Research; Research Personnel; Resources; Sampling; Scheme; Scientist; Signal Transduction; Software Tools; Statistical Methods; Structure; System; Systems Biology; Techniques; Testing; Texas; Time; Validation; base; biological research; biological systems; comparative; computer based statistical methods; computerized tools; data modeling; drug development; embryonic stem cell; functional genomics; gene discovery; improved; innovation; insight; knockout gene; malformation; man; mathematical model; method development; mouse genome; network models; novel; null mutation; protein expression; public health relevance; research study; response; scale up; simulation; software development; stem; tool

DESCRIPTION (provided by applicant): The overall Phase II objective is to continue the development/validation of new system biology computational tools for inferencing gene regulatory relationships from gene expression data obtained from multi-perturbation gene knockout experiments. NIH's Knockout Mouse Project (KOMP) 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 deciphering the complexity of biological systems of mice and ultimately man. It is anticipated that a new generation of multi-perturbation/KO studies with a biological system perspective will emerge in all areas of biomedical research. New computational tools for deciphering genetically regulated responses (genotype-to- phenotype signaling cascades) will significantly aid in advancing our understanding of the molecular targets and mechanisms of many diseases. Today, researchers need new tools to deal with and decipher the tremendous volumes of gene/protein expression data generated from multi-perturbation investigations. Seralogix's Phase II efforts focus on improving and creating new functionality for learning larger scale (biological system level) gene regulatory networks and integrating this network learning functionality into our existing Biosystem Analysis Framework (BAF). Our BAF is comprised of a suite of integrated mathematical analysis and modeling tools and databases. The BAF core tools are based on Dynamic Bayesian Networks (DBNs). DBNs allow us to systematically integrate prior knowledge with empirical time-course expression data for modeling, pattern recognition and eventually biological system genetic network learning as proposed herein. Our algorithmic innovation, proven feasible in Phase I, is the incorporation of biological prior knowledge and multi-perturbation data with our DBNs for enabling a genetic network learning approach. This approach is based on well established Bayesian statistical methods that we adopt in a sampling scheme enhanced with biological prior knowledge to overcome the intrinsic difficulty of structure learning from sparse and noisy gene expression data. We show in Phase I that prior-knowledge, coupled with Bayesian network learning methods and multi-perturbation/KO experimental data, resulted in reliable gene regulatory relationship identification. We believe this approach can be scaled up, leading to a more robust mathematical/functional system level model. Further, we believe that integrating genetic network learning into Seralogix's BAF will provide an important new tool for identifying novel gene regulatory relations and insights into disease processes and have significant commercial potential for Seralogix. We will be collaborating with the Texas Institute of Genomic Medicine as a provider of mouse gene expression KO data who are studying the genomic causes of birth defects. Our Phase II aims include: 1) scaling our approach to support biological system level network learning; 2) statistical assessment and biological validation of our learned networks; 3) developing new tools/techniques to interrogate the resulting system network models so biologist can extract important knowledge. PUBLIC HEALTH RELEVANCE: 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）：II阶段的总体目标是继续开发/验证新系统生物学计算工具，以从从多扰动基因敲除实验获得的基因表达数据中推导基因调节关系。 NIH的基因敲除小鼠项目（KOMP）是一项主动性，旨在生成在小鼠基因组中每个基因中含有无效突变的小鼠胚胎茎（ES）细胞的公共资源 - 对于破译小鼠生物系统的复杂性和最终人物的复杂性很重要。可以预料，在生物医学研究的所有领域中，都会出现新一代具有生物系统视角的多扰动/KO研究。用于解密遗传调节反应的新计算工具（基因型到表型信号传导级联）将大大有助于促进我们对许多疾病分子靶标和机制的理解。如今，研究人员需要新工具来处理和破译由多扰动研究产生的大量基因/蛋白质表达数据。 Seralogix的II期努力着重于改善和创建新的功能，以学习大规模（生物系统级别）基因调节网络，并将该网络学习功能集成到我们现有的生物系统分析框架（BAF）中。我们的BAF由一套集成的数学分析以及建模工具和数据库组成。 BAF核心工具基于动态贝叶斯网络（DBN）。 DBN使我们能够系统地将先验知识与经验的时间课程表达数据相结合，以建模，模式识别以及最终如本文所建议的生物系统遗传网络学习。我们在第一阶段可行的算法创新是将生物学先验知识和多扰动数据与我们的DBN合并在一起，以实现遗传网络学习方法。这种方法基于我们在采样方案中采用的良好确定的贝叶斯统计方法，并通过生物学先验知识增强了采样方案，以克服从稀疏和嘈杂的基因表达数据中学习结构的内在难度。我们在第一阶段显示，先前的知识，再加上贝叶斯网络学习方法和多扰动/KO实验数据，从而实现了可靠的基因调节关系鉴定。我们认为可以扩大这种方法，从而导致更强大的数学/功能系统级别模型。此外，我们认为将遗传网络学习整合到Seralogix的BAF中将为识别新型基因调节关系和对疾病过程的见解提供重要的新工具，并具有Seralogix的巨大商业潜力。我们将与德克萨斯基因组医学研究所合作，是研究出生缺陷的基因组原因的小鼠基因表达数据的提供者。我们的第二阶段目标包括：1）扩展我们支持生物系统水平网络学习的方法； 2）对我们学习的网络的统计评估和生物验证； 3）开发新的工具/技术来询问所得的系统网络模型，以便生物学家可以提取重要知识。 公共卫生相关性：这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，进行性疾病的进行性和重复的复杂疾病的发病机制。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。