Computational Methods for Functional Genomic Discovery from Gene Knockout Studies

描述（由申请人提供）：第一阶段的总体目标是建立一个新的计算工具的原型，以评估从多比较基因敲除研究中获得的基因表达数据中识别和预测基因功能和相互关系的可行性。美国国立卫生研究院的敲除小鼠项目是一项创举，旨在产生一种公共资源的小鼠胚胎干（ES）细胞，其中包含小鼠基因组中每个基因的零突变，这对于研究疾病和破译小鼠生物系统的复杂性以及最终在人类中都很重要。预计在生物医学研究的所有领域都将出现新一代的生物比较敲除研究。拥有新的计算方法来识别和破译遗传调控反应（例如信号级联）将极大地有助于推进我们对许多疾病的分子靶点和机制的理解，并将对取得医学突破，特别是对安全有效地开发药物和诊断非常重要。如今，研究人员受到基因敲除研究产生的大量数据的阻碍。Seralogix计划建立在我们现有的生物系统分析框架（BAF）的基础上，该框架由一套用于基因和蛋白质分析的集成分析和数学建模工具组成。我们的核心工具是基于动态贝叶斯网络（dbn）的统计能力，它建立在健全的统计方法上，使我们能够将先验知识与经验时间过程数据相结合，用于建模，模式识别和遗传网络推断。本文提出的创新是将多摄动Shapley值分析（MSA）方法与我们的dbn相结合，以改进遗传网络的发现。MSA是基于博弈论的概念，我们将利用它来确定系统中每个元素（基因）相对于所有其他元素的关系/重要性。我们假设MSA与我们的DBN推理引擎结合多基因KO实验的时间过程基因表达数据将导致改进的遗传交互发现，从而导致更健壮的数学/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交互关系发现提供重要的新工具。Seralogix相信，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。我们提出的工具的第一阶段目标将是：1)设计和实施我们的DBN/MSA多条件KO比较方法，用于发现潜在的遗传网络；2)证明DBN/MSA方法在小鼠叶酸受体基因KO表达数据和相关信号通路上的可行性，这些数据是德克萨斯基因组医学研究所（TIGM）提供给我们的，作为他们正在进行的出生缺陷研究的一部分。现代生物学研究的最终目标之一是充分阐明控制健康和疾病的分子决定因素的复杂相互作用和调控，例如细胞循环、发育生物学、衰老以及复杂疾病的进展和复发发病机制。拥有新的计算方法（软件工具）来识别和破译遗传调控反应（例如信号级联）将大大有助于推进我们对许多高度公共卫生关注的疾病的分子靶点和机制的理解。发现潜在的遗传功能和关系对于取得医学突破，特别是对于安全有效地开发药物和诊断方法，将是极其重要的。如今，研究人员受到敲除研究产生的大量基因/蛋白质表达数据的阻碍。通过数学建模将这些原始基因组/蛋白质组数据转化为可操作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调查，并确定干预和治疗的目标。

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.