Suppr超能文献

从基因表达时间序列推断无标度网络。

Inference of scale-free networks from gene expression time series.

作者信息

Daisuke Tominaga, Horton Paul

机构信息

Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, Aomi 2-42, Koto, Tokyo 135-0064, Japan.

出版信息

J Bioinform Comput Biol. 2006 Apr;4(2):503-14. doi: 10.1142/s0219720006001886.

Abstract

Quantitative time-series observation of gene expression is becoming possible, for example by cell array technology. However, there are no practical methods with which to infer network structures using only observed time-series data. As most computational models of biological networks for continuous time-series data have a high degree of freedom, it is almost impossible to infer the correct structures. On the other hand, it has been reported that some kinds of biological networks, such as gene networks and metabolic pathways, may have scale-free properties. We hypothesize that the architecture of inferred biological network models can be restricted to scale-free networks. We developed an inference algorithm for biological networks using only time-series data by introducing such a restriction. We adopt the S-system as the network model, and a distributed genetic algorithm to optimize models to fit its simulated results to observed time series data. We have tested our algorithm on a case study (simulated data). We compared optimization under no restriction, which allows for a fully connected network, and under the restriction that the total number of links must equal that expected from a scale free network. The restriction reduced both false positive and false negative estimation of the links and also the differences between model simulation and the given time-series data.

摘要

例如,通过细胞阵列技术,基因表达的定量时间序列观测正变得可行。然而,目前还没有仅使用观测到的时间序列数据来推断网络结构的实用方法。由于大多数用于连续时间序列数据的生物网络计算模型具有高度的自由度,几乎不可能推断出正确的结构。另一方面,据报道,某些类型的生物网络,如基因网络和代谢途径,可能具有无标度特性。我们假设推断出的生物网络模型的架构可以限制为无标度网络。通过引入这样的限制,我们开发了一种仅使用时间序列数据来推断生物网络的算法。我们采用S-系统作为网络模型,并使用分布式遗传算法来优化模型,使其模拟结果与观测到的时间序列数据相匹配。我们在一个案例研究(模拟数据)中测试了我们的算法。我们比较了在无限制(允许全连接网络)和在链接总数必须等于无标度网络预期数量的限制下的优化情况。该限制减少了链接的假阳性和假阴性估计,以及模型模拟与给定时间序列数据之间的差异。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验