Inference of Genetic Interactions in Large Scale Genetic Network

通过DNA微阵列、DNA芯片等最新的强大技术，可以同时测量基因组规模上成百上千个基因的表达谱。这些观测数据取决于其环境，通常以快照的形式获得，但也可以以密集的时间序列形式生成，以指示动态行为。实验观察到的时间进程数据应该包含关于体内遗传网络调控的大量信息。然而，由于这一信息完全是隐含的，它需要适当的分析和计算方法来检索和解释。利用实验观察到的时程数据对遗传网络进行推理的问题通常被称为逆问题，可以定义为对遗传网络合适的模型表示中所涉及的参数值的函数优化。解决这类反问题的关键是如何建立…的规范表示针对遗传网络更多的数学建模以及如何在巨大的搜索空间中挖掘和利用参数的取值问题，我们首次提出了一种新的遗传网络推理方法--S系统动态网络模型与基于实数编码遗传算法的参数估计计算技术相结合。利用S系统模型和结合单峰正态分布交叉和最小世代间隔的RCGAS，我们提出了从实验观察到的系统成分的时程数据推断遗传互作的有效方法。通过改进搜索算法和引入服务器-客户端系统，我们开发了一种新颖的推理系统，该系统可以找到大量可能的网络候选者，从而实现给定的实验观测时间进程数据。所有这些网络候选者都可以认识到相同的实验观察到的事实，但是遗传相互作用的结构是不同的。因此，我们提出了从众多网络候选中提取有用信息的分析方法。基因表达。在S系统模型中，相关系数的符号表示激活、抑制或不相关的相互作用。在相同的参数优化条件下，基于相同的实验观察时程数据推断出的所有基因表达网络候选之间的符号相同的相互作用定义了共同的核心相互作用。我们计算了网络候选中包含的每个交互作用的敏感度，并比较了共同核心交互作用和其他独特交互作用的敏感度。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