STUDY DESIGNS Studying and modelling dynamic biological processes using time-series gene expression data

被引：347

作者：

Bar-Joseph, Ziv ^{[1
,2
]}

Gitter, Anthony ^{[3
]}

Simon, Itamar ^{[4
]}

机构：

[1] Carnegie Mellon Univ, Sch Comp Sci, Lane Ctr Computat Biol, Pittsburgh, PA 15213 USA

[2] Carnegie Mellon Univ, Sch Comp Sci, Machine Learning Dept, Pittsburgh, PA 15213 USA

[3] Carnegie Mellon Univ, Sch Comp Sci, Dept Comp Sci, Pittsburgh, PA 15213 USA

[4] Hebrew Univ Jerusalem, Hadassah Med Sch, Dept Microbiol & Mol Genet, IMRIC, IL-91010 Jerusalem, Israel

来源：

NATURE REVIEWS GENETICS | 2012年 / 13卷 / 08期

基金：

美国国家卫生研究院; 以色列科学基金会; 欧洲研究理事会;

关键词：

NETWORK COMPONENT ANALYSIS; HUMAN CELL-CYCLE; RNA-SEQ; TRANSCRIPTIONAL CONTROL; REGULATORY NETWORKS; MICROARRAY ANALYSIS; BAYESIAN-ESTIMATION; CLUSTER-ANALYSIS; REVEALS; RESPONSES;

D O I：

10.1038/nrg3244

中图分类号：

Q3 [遗传学];

学科分类号：

071007 ; 090102 ;

摘要：

Biological processes are often dynamic, thus researchers must monitor their activity at multiple time points. The most abundant source of information regarding such dynamic activity is time-series gene expression data. These data are used to identify the complete set of activated genes in a biological process, to infer their rates of change, their order and their causal effects and to model dynamic systems in the cell. In this Review we discuss the basic patterns that have been observed in time-series experiments, how these patterns are combined to form expression programs, and the computational analysis, visualization and integration of these data to infer models of dynamic biological systems.

引用

页码：552 / 564

页数：13

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