Identification and control of gene networks in living organisms via supervised and unsupervised learning

被引：4
|
作者：
Driscoll, ME
Gardner, TS [1 ]
机构：
[1] Boston Univ, Dept Biomed Engn, Boston, MA 02215 USA
[2] Boston Univ, Bioinformat Program, Ctr Biodynam, Boston, MA 02215 USA
来源：
JOURNAL OF PROCESS CONTROL | 2006年 / 16卷 / 03期
关键词：
statistical inference; learning algorithms; biotechnology;
D O I：
10.1016/j.jprocont.2005.06.010
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Cells efficiently carry out organic synthesis, energy transduction, and signal processing across a range of environmental conditions and at nanometer scales-rivaling any engineered system. In the cell, these processes are orchestrated by gene networks, which we define broadly as networks of interacting genes, proteins, and metabolites. Understanding how the dynamics of gene networks give rise to cellular functions is a principal challenge in biology, and identifying their structure is the first step towards their control. This knowledge has applications ranging from the improvement of antibiotics, the engineering of microbes for environmental remediation, and the creation of biologically-derived energy sources. In this review, we discuss several methods for the identification of gene networks. (c) 2005 Elsevier Ltd. All rights reserved.
引用
收藏
页码：303 / 311
页数：9
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