Inference of Genetic Interactions in Large Scale Genetic Network

近年来，DNA微阵列、DNA芯片等技术的发展，使基因组规模上的成百上千个基因的表达谱可以同时测定。这些观察到的数据取决于它的环境，通常是作为快照获得的，但可以生成密集的时间序列，指示动态行为。实验观察到的时间过程数据应该包含大量的信息，在体内的遗传网络的调节。然而，由于这一信息是完全隐含的，它需要适当的分析和计算方法的检索和解释。遗传网络的这种利用实验观察到的时间过程数据的推理问题通常被称为“逆问题”，并且可以被定义为遗传网络的合适模型表示中所涉及的参数值的函数优化。求解这类反问题的关键是如何建立正则表达式， ...更多信息 为了解决遗传网络的数学建模问题以及如何在巨大的搜索空间中探索和利用参数值的问题，首次提出了一种将S-系统动态网络模型与基于实数编码遗传算法（RCGAs）的参数估计计算技术相结合的遗传网络推理方法。使用S-系统建模和RCGAs与UNDX（单峰正态分布交叉）和MGG（最小代沟）的组合，我们提出了有效的程序推理的遗传相互作用的实验观察到的时间过程数据的系统组件（mRNA）。通过改进搜索算法和引入服务器-客户端系统，我们开发了一个新的推理系统，它可以找到大量的可能的网络候选人，可以实现给定的实验观察到的时间过程数据。所有这些网络候选者都能实现相同的实验观察事实，然而，遗传相互作用的结构彼此不同。因此，我们提出了一种分析方法，用于从许多网络候选者中提取有用的信息。基因表达。在S系统模型中，相关系数的符号表示相互作用的类型，如激活、抑制或不相关。共同核心相互作用定义为在相同的参数优化条件下，基于相同的实验观察的时间过程数据推断的基因表达的所有网络候选者之间具有相同符号的相互作用。我们计算了候选网络中每个交互的敏感度，并将共同核心交互的敏感度与其他独特交互的敏感度进行了比较。少

The expression profiles of hundreds and thousands of genes on a genomic scale can be measured simultaneously by recent powerful technologies such as DNA microarrays, DNA chips and so forth. These observed data depending on its environment are usually obtained as snapshots, but can be generated as dense time series that indicate the dynamic behavior. The experimentally observed time-course data should contain enormous information about the regulation of genetic networks in vivo. However, since this information is entirely implicit, it requires adequate analytical and computational methods of retrieval and interpretation. This inference problem of genetic networks by using the experimentally observed time-course data is generally referred to as "inverse problem" and can be defined as function optimization of the values of parameters involved in a suitable model representation of genetic network. The key points to solve such an inverse problem are how to set up canonical representation of … More mathematical modeling of genetic network and how to explore and exploit the values of parameters within immense huge searching space, we had first proposed a novel inferring method of genetic network by combining a dynamic network model called S-system with a computational technique of parameter estimation based on real-coded genetic algorithms (RCGAs). Using S-system modeling and RCGAs with the combination of the UNDX (unimodal normal distribution crossover) and MGG (minmal generation gap), we proposed efficient procedures for the inference of genetic interactions from the experimentally observed time-course data of system components (mRNA). By improving the searching algorithm and by introducing server-client system, we have developed the novel inferring system which can be finding a lots of possibly network candidates that can realize the given experimentally observed time-course data. All of these network candidates can realize the same experimentally observed facts, however, the structures of genetic interactions are different each other. Therefore, we have proposed the analytical method for extracting useful information from many network candidates of. gene expression. In S-system model, the sign of interrelated coefficient shows the kind of interactions such as activation, inhibition, or no relation. The common core interactions are defined by the interactions with sign of which are same among all network candidates of gene expression which inferred based on the same experimentally observed time-course data under the same parameter optimizing conditions. We calculated sensitivity for each interaction included in the network candidates, and compared sensitivity of common core interactions with that of other unique interactions. Less