PHYSIOLOGICAL MODEL FOR GENE REGULATION IN DROSOPHILA

描述：该提案旨在制定一项表征 许多基因之间的相互作用决定了一个 空间和时间中的生物体。 与传统方法相比， 分子生物学，它试图描述这些相互作用， 通过一次研究一个或最多几个基因， 建议侧重于综合互动网络。 的方法 建议有三个主要内容。 第一，电脑想象技术 将被采用和完善，以产生高质量的浓度 基因产物作为时间的函数的测量， 单个核的分辨率。 这涉及荧光测量 用对每种相关基因产物特异的抗体， 增强图像以锐化核轮廓并将其带入 一个共同的记录。 其次，将开发并行模拟退火算法来适应 数学模型来表达数据。 本案的进展 是针对提高速度的目标，这与传统的 模拟退火算法的特性是缓慢的。 为了达到适合 实际上，这种方法需要使用超级计算机。 第三，数学模型是传统神经网络的模拟， 网络模型 对各种基因的表达变量进行加权 并求和，并将该和通过非线性阈值装置， 产生给定基因产物的合成速率。 还有一个 比例损失项和涉及扩散的交换项 原子核。 任何基因产物的变化率都是由 这三个词的组合。 当这三个元素 成功组合，结果是一组权重，提供最佳的 适合数据。 然后，这些权重用于推断 网络中基因之间的相互作用。 该模型系统已被 选择来证明这一战略的可行性是网络， 黑腹果蝇中的体节基因。

DESCRIPTION: This proposal aims to develop a strategy for characterizing the interactions among the many genes that determine the properties of an organism in space and time. In contrast to the conventional approach of molecular biology, which attempts to characterize these interactions in molecular terms by the study of one or at most a few genes at a time, this proposal focuses on the integrated network of interactions. The approach proposed has three major elements. First, computer imagining techniques will be adopted and refined to generate high-quality concentration measurements of gene products as a function of time with a spatial resolution of a single nucleus. This involves fluorescence measurements with antibodies specific for each of the relevant gene products and computer enhancement of the images to sharpen the nuclear profile and bring them into a common register. Second, a parallel simulated annealing algorithm will be developed to fit a mathematical model to the expression data. The developments in this case are directed toward the goal of enhancing the speed, which with conventional simulated annealing algorithms is characteristically slow. To achieve fits in practical time with this approach requires the use of a super computer. Third, the mathematical model is an analog of the conventional neural network model. The expression variables for the various genes are weighted and summed, and the sum is passed through a nonlinear threshold device to produce the synthesis rate for a given gene product. There is also a proportional loss term and an exchange term involving diffusion between nuclei. The rate of change in any gene product is determined by the combination of these three terms. When these three elements are successfully combined, the result is a set of weights that provides the best fit to the data. These weights are then used to infer the pattern of interactions among the genes of the network. The model system that has been selected to demonstrate the feasibility of this strategy is the network of segmentation genes in Drosophila melanogaster.