Systems biology: experimental design

被引:178
作者
Kreutz, Clemens [1 ]
Timmer, Jens [1 ]
机构
[1] Univ Freiburg, Dept Phys, D-79104 Freiburg, Germany
关键词
confounding; experimental design; mathematical modeling; model discrimination; Monte Carlo method; parameter estimation; sampling; systems biology; SEQUENTIAL EXPERIMENTAL-DESIGN; MODEL DISCRIMINATION; PARAMETER-ESTIMATION; SAMPLE-SIZE; GLOBAL IDENTIFIABILITY; DYNAMIC EXPERIMENTS; QUANTITATIVE DATA; ROBUST DESIGNS; EFFICIENT; SELECTION;
D O I
10.1111/j.1742-4658.2008.06843.x
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Experimental design has a long tradition in statistics, engineering and life sciences, dating back to the beginning of the last century when optimal designs for industrial and agricultural trials were considered. In cell biology, the use of mathematical modeling approaches raises new demands on experimental planning. A maximum informative investigation of the dynamic behavior of cellular systems is achieved by an optimal combination of stimulations and observations over time. In this minireview, the existing approaches concerning this optimization for parameter estimation and model discrimination are summarized. Furthermore, the relevant classical aspects of experimental design, such as randomization, replication and confounding, are reviewed.
引用
收藏
页码:923 / 942
页数:20
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