SGER: Dynamical Models of Gene Networks in Development.

O‘DELL 9732702这个项目的目标有三个：开发一个数学框架和计算机软件来表示和求解基因网络的微分方程式模型，组装一个果蝇片段极性基因网络的最小模型，并根据文献和实验室实验的结果测试和改进该模型，根据需要向模型中添加组件。分子遗传学已经产生了关于一系列重要功能基因的日益丰富的数据。对于试图解释发育机制的生物学家来说，这些数据既是一个机遇，也是一个严峻的挑战。大多数，也许是所有的发育重要基因都在动态网络中起作用，其基本行为由其组成部分之间复杂的相互作用网络决定。由于它们的动态性质，这些基本行为不能从生物学家通常用来总结他们实验的简单箭头图中直接推断出来。如果不使用计算机工具，即使是在非常小的网络中同时发生的实际动态交互也不可能解析成对整个网络行为的准确预测。因此，计算机模型为分析这种复杂的动态网络的行为提供了一种强大而独特的方法。这项建议的目标是开发和说明一种通用的计算机建模方法，该方法通常可以用于在许多不同的背景下研究基因网络。这种方法的基础是建立描述每个网络成分的浓度如何作为其在网络中的相互作用的函数在时间上连续变化的方程，然后使用它们来探索网络的行为。果蝇的片段极性基因网络将被用作一个模型，它建立了跨越细胞胚层的有模式的细胞命运。

O'Dell 9732702 The goals of this project are three-fold: To develop a mathematical framework and computer software for representing and solving differential equation models of gene networks, to assemble a minimal model of the segment polarity gene network of Drosophila melanogaster and to test and refine that model against results in the literature and laboratory experiments, adding components to the model as necessary. Molecular genetics has produced a growing wealth of data on a host of functionally important genes. These data present both an opportunity and a serious challenge to biologists seeking to explain developmental mechanisms. Most, and maybe all, developmentally important genes act within dynamic networks whose essential behaviors are determined by complex webs of interaction among their constituents. Because of their dynamic nature, these essential behaviors cannot be directly inferred from the simple arrow diagrams that biologists typically use to summarize their experiments. The resolution of the actual dynamic interactions occurring simultaneously within even very small networks into accurate predictions of overall network behavior is not possible without the use of computer tools. Thus, computer models offer a powerful and unique approach to analyzing the behavior of such complex dynamic networks. The goal of this proposal is to develop and illustrate a general computer modeling approach that can be generally used to study gene networks in many different contexts. The basis of this approach is to formulate equations describing how the concentration of each network constituent varies continuously in time as a function of its interactions within the network, and then use them to explore the network's behavior. The segment polarity gene network in Drosophila which establishes patterned cell fates across the cellular blastoderm will be used as a model.