DYNAMICS OF SEGMENTATION GENE EXPRESSION IN DROSOPHILA

生物技术的最新进展导致了大规模基因表达数据的获取和解释。如果没有处理和解释这些信息的有效方法，发育生物学的科学进步以及对基于遗传的疾病的更好理解是不可能的。本提案通过应用计算和数学方法来解决这些问题，以果蝇片段确定系统中的基因表达动力学。该项目的具体目标是利用计算方法将单个胚胎的定量数据合成为细胞分辨率的分段基因表达综合定量图谱，并根据分段基因表达设计精确的胚胎分期时钟。我们将开发一个图谱（可搜索数据库）的分割基因表达在细胞分辨率，将可通过互联网访问。数学方法将用于解释表达数据，最终目的是根据粗粒度化学动力学模型来理解果蝇中控制片段的基因的功能相互作用。具体而言，将应用统计分析方法和二维空间中分割过程的模拟。此外，我们将找到描述段确定过程的高度非线性耦合反应扩散方程的精确和渐近解。该项目是NIH拨款RR-07801支持的活动的自然延伸，将在理解分割系统的架构和功能方面取得重大进展。

Recent advances in biotechnology have led to the acquisition and interpretation of large scale gene expression data. Scientific progress in developmental biology as well as improved understanding of genetically based diseases are impossible without efficient methods for the processing and interpretation of this information. This proposal addresses these problems by the application of both computational and mathematical methods to the dynamics of gene expression in the Drosophila segment determination system. The specific aims of the project are to use computational methods to synthesize quantitative data from individual embryos into an integrated quantitative atlas of segmentation gene expression at cellular resolution and to design a precise clock for staging embryos based on segmentation gene expression. We will develop an atlas (searchable database) of segmentation gene expression at cellular resolution that will be accessible over the Internet. Mathematical methods will be used to interpret the expression data with the ultimate goal to understanding the functional interactions of genes controlling segmentation in Drosophila in terms of a coarse-grained chemical kinetic model. Specifically, statistical analysis methods and simulations of the segmentation process in 2D space will be applied. In addition we will find exact and asymptotic solutions of the highly nonlinear coupled reaction-diffusion equations that describe the segment determination process. The project is a natural extension of the activity supported by the NIH grant RR-07801 and will result in significant advances in understanding of the architecture and function of the segmentation system.