Mathematic descriptions - multifactorial gene expression

描述（由申请人提供）：社会行为是一种复杂的特征，社会互动是人类身心健康的关键决定因素。我们的长期目标是通过整合基因组研究，统计分析，生物信息学资源和外展来解释影响复杂特征（如社会行为）的因素之间的相互作用。六个具体目标支持这一目标。1)我们将生成蜜蜂大脑中7000多个基因的表达谱，作为其复杂的行为成熟模式的函数;每3-4天采集一次样本，因为它完成了蜂巢内的任务，然后转移到外面觅食。我们将这些配置文件的特点，在两个不同的种族已知不同的成熟速度。这些生物学发现将对理解蜜蜂、人类和其他物种的行为、神经和心理过程、健康和福祉产生重大影响。上位性，多效性和多基因背景效应将进行研究，同时考虑系统噪声，包括阵列，染料和转录水平的影响。2)我们将应用新的线性和非线性混合效应纵向模型，无缝集成强度归一化和分析。3)我们将使用分层模型来识别种族之间的差异。这些模型解释了变异的技术来源，以及来自同一家族或编码同一途径中蛋白质的基因之间可能出现的表达相关性。这个建议填补了一个重要的需要，适合复杂的基因表达数据集的一般模型。4)将开发直观方法来检查模型是否适当。5)所提出的方法将结合生物信息学资源，在一套公开的例程，方便解释结果，同时分配基因功能。6)调查结果将被用作数学和科学概念的应用实例，以探究为基础，开放式，教育材料;虚拟和有形的介绍高中学生，教师和公众将开发。我们的发现构成了理解和进一步研究人类神经，精神，沟通和衰老过程和疾病的垫脚石，从而产生治疗和治愈。

DESCRIPTION (provided by applicant): Social behavior is a complex trait and social interactions are critical determinants of human mental and physical health. Our long-term objective is to explain the interplay among factors influencing complex traits like social behavior by the integration of genomic research, statistical analysis, bioinformatic resources and outreach. Six specific aims support this objective. 1) We will generate expression profiles for more than 7000 genes in the brain of the honey bee as a function of its complex pattern of behavioral maturation; samples will be taken every 3-4 days, as it moves through its in-hive tasks and then shifts to foraging outside. We will characterize these profiles in two distinct races known to differ in the rate of maturation. These biological findings will have a major impact on understanding behavior, neurological and mental processes, health and well-being in bees, humans and other species. Epistatic, pleiotropic, and polygenic background effects will be studied while accounting for systematic noise including array, dye and transcript level effects. 2) We will apply novel linear and nonlinear mixed-effects longitudinal models that seamlessly integrate intensity normalization and analyses. 3) We will use hierarchical models to identify between race variation. These models account for technical sources of variation and for correlation in expression that might arise among genes that are from the same family or code for proteins in the same pathway. This proposal fills an important need for general models suitable for complex sets of gene expression data. 4) Visual approaches will be developed to check for model adequacy. 5) The proposed methodology will be combined with bioinformatic resources in a publicly available suite of routines that facilitate the interpretation of results while assigning gene function. 6) The findings will be used as applied examples of mathematical and science concepts in inquiry-based, open-ended, educational materials; virtual and tangible presentations for high school students, teachers, and the general public will be developed. Our findings constitute stepping-stones for understanding and further studies of human neurological, mental, communication, and aging processes and disorders, giving rise to treatments and cures.