Akutsu T, Miyano S, Kuhara S
Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan.
Bioinformatics. 2000 Aug;16(8):727-34. doi: 10.1093/bioinformatics/16.8.727.
Inferring genetic network architecture from time series data of gene expression patterns is an important topic in bioinformatics. Although inference algorithms based on the Boolean network were proposed, the Boolean network was not sufficient as a model of a genetic network.
First, a Boolean network model with noise is proposed, together with an inference algorithm for it. Next, a qualitative network model is proposed, in which regulation rules are represented as qualitative rules and embedded in the network structure. Algorithms are also presented for inferring qualitative relations from time series data. Then, an algorithm for inferring S-systems (synergistic and saturable systems) from time series data is presented, where S-systems are based on a particular kind of nonlinear differential equation and have been applied to the analysis of various biological systems. Theoretical results are shown for Boolean networks with noises and simple qualitative networks. Computational results are shown for Boolean networks with noises and S-systems, where real data are not used because the proposed models are still conceptual and the quantity and quality of currently available data are not enough for the application of the proposed methods.
从基因表达模式的时间序列数据推断遗传网络结构是生物信息学中的一个重要课题。尽管提出了基于布尔网络的推断算法，但布尔网络作为遗传网络的模型并不充分。
首先，提出了一个带有噪声的布尔网络模型及其推断算法。其次，提出了一个定性网络模型，其中调控规则表示为定性规则并嵌入网络结构中。还给出了从时间序列数据推断定性关系的算法。然后，给出了一个从时间序列数据推断S系统（协同和饱和系统）的算法，其中S系统基于一种特定类型的非线性微分方程，并已应用于各种生物系统的分析。给出了带有噪声的布尔网络和简单定性网络的理论结果。给出了带有噪声的布尔网络和S系统的计算结果，这里未使用实际数据，因为所提出的模型仍处于概念阶段，且当前可用数据的数量和质量不足以应用所提出的方法。
