PHYSIOLOGICAL MODEL FOR GENE REGULATION IN DROSOPHILA

点击翻译按钮获取中文摘要

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.