No-linear systematic understanding of genome-protein dynamics

在本研究中，我们提出了遗传算法在基因网络推理问题中的应用。它是近年来生物信息学研究的热点之一。目的是从观察到的结果，即表达模式来预测相互作用基因的调节网络结构。该任务包括对规则建模和从观测数据推断网络结构。将遗传算法应用于用观测数据训练模型，以预测以影响矩阵表示的调控途径。我们在定量和线性生物学框架中实现了基于遗传算法的逆向工程方法。该方法的优点是可以应用于少量数据，同时优化大量参数，并且可以应用于非线性模型。遗传算法的实现包括多阶段进化和矩阵染色体。该方法已在模拟表达模式和实验中得到应用。在本研究中，我们运用遗传算法设计电路的知识。对于另一个重要课题，我们提出了一个动态微分贝叶斯网络（DDBNs）和非参数回归模型。该模型是传统动态贝叶斯网络（dbn）的扩展模型，可以自然地吸收时间信息，并直接处理从微阵列获取的实值数据，而无需进行任何转换。此外，它可以处理基因表达水平之间的差异信息，而不会失去传统的优势，即估计基因之间非线性关系的能力。我们利用ddbn分析模拟数据和真实数据，即酿酒酵母细胞周期基因表达数据。我们已经证实了我们的方法的有效性，因为一些边缘只被ddbn成功检测到，而不是被dbn成功检测到。近年来，通过以人类基因组计划为代表的尝试，碱基序列被越来越多地破译。从而加快了遗传网络的估计速度。然而，目前还没有确定的方法来绘制一个大的有效图。为了解决这些困难，我们提出了一种方法，该方法可以通过将基因放置在几层的平面上，然后将这些平面重叠来应对节点数量的增加。这种布局涉及到一个优化问题，要求最大化适应度函数。为了证明我们方法的有效性，我们展示了一些使用82个基因、552个基因的实际数据和从1000个基因的无标度网络建模的人工数据的图表。我们还描述了如何通过随机搜索，例如随机爬坡和模拟退火方法来布置节点。与简单的随机搜索相比，实验结果表明了随机搜索的优越性和实用性。少

In this research, we have presented an application of genetic algorithms to the gene network inference problem. It is one of the active topics in recent Bioinformatics. The objective is to predict a regulating network structure of the interacting genes from observed outcome, i.e., expression pattern. The task consists of modeling the rules of regulation and inferring the network structure from observed data. The GA is applied to train the model with observed data to predict the regulatory pathways, represented as influence matrix. We have implemented a reverse engineering method based on genetic algorithms in a quantitative and linear biological framework. The merit of this approach is that it can be applied with small amount of data, optimize large amount of parameters simultaneously and can be applied on nonlinear models. The GA implementation includes multiple stage evolution and matrix chromosomes. This method has been applied on simulated expression patterns and experimentally obs … More erved expression patterns. In this research, we used the knowledge of designing electric circuit by GA.As for another important topic, we have proposed a dynamic differential Bayesian networks (DDBNs) and nonparametric regression model. This model is an extended model of traditional dynamic Bayesian networks (DBNs), which can incorporate temporal information in a natural way and directly handle real-valued data obtained from microarrays without any transformation. In addition, it can cope with differential information between gene expression levels, without any loss to the traditional advantage, i.e., the capability of estimating non-linear relationships between genes. We have applied DDBNs to analyze simulated data and real data, i.e., Saccharomyces cerevisiae cell cycle gene expression data. We have confirmed the effectiveness of our approach in the sense that some edges have been successfully detected only by DDBNs, not by DBNs.In recent years, base sequences have been increasingly unscrambled through attempts represented by the human genome project. Accordingly, the estimation of the genetic network has been accelerated. However, no definitive method has become available for drawing a large effective graph. To solve these difficulties, we have proposed a method which allows for coping with an increase in the number of nodes by laying out genes on planes of several layers and then overlapping these planes. This layout involves an optimization problem which requires maximizing the fitness function. To demonstrate the effectiveness of our approach, we show some graphs using actual data on 82 genes, 552 genes, and artificial data modeled from a scale-free network of 1,000 genes. We also described how to lay out nodes by means of stochastic searches, e.g., stochastic hill-climbing and simulating annealing methods. The experimental results have shown the superiority and usefulness of stochastic searches in comparison with the simple random search. Less