Mathematic descriptions - multifactorial gene expression

描述（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社会行为等复杂特征的因素之间的相互作用。支持这一目标的六个具体目标。1)我们将生成蜜蜂大脑中7000多个基因的表达谱，作为其复杂行为成熟模式的功能；每隔3-4天采集一次样本，因为它会完成蜂巢内的任务，然后转移到外面觅食。我们将在已知成熟率不同的两个不同种族中描述这些特征。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。将研究上位性、多效性和多基因背景效应，同时考虑系统噪声，包括阵列、染料和转录水平效应。2)我们将采用新颖的线性和非线性混合效应纵向模型，将强度归一化和分析无缝集成。3)我们将使用层次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，以及来自同一家族的基因或在同一途径中编码蛋白质的基因之间可能出现的表达相关性。这一建议填补了对适用于复杂基因表达数据集的通用模型的重要需求。将开发可视化方法来检查模型的充分性。5)提出的方法将与生物信息学资源相结合，在一套公开可用的常规程序中，促进结果的解释，同时分配基因功能。6)研究结果将被用作以探究为基础的开放式教育材料中数学和科学概念的应用实例；将为高中学生、教师和一般公众开发虚拟和有形的演示文稿。我们的发现为理解和进一步研究人类神经、精神、交流、衰老过程和疾病奠定了基础，并带来了治疗和治愈。

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.