Maximizing the Information Content of Experiments in Systems Biology

被引:123
作者
Liepe, Juliane [1 ]
Filippi, Sarah [1 ]
Komorowski, Michal [2 ]
Stumpf, Michael P. H. [1 ,3 ]
机构
[1] Univ London Imperial Coll Sci Technol & Med, Ctr Integrat Syst Biol & Bioinformat, London, England
[2] Polish Acad Sci, Inst Fundamental Technol Res, Warsaw, Poland
[3] Univ London Imperial Coll Sci Technol & Med, Inst Chem Biol, London, England
基金
英国生物技术与生命科学研究理事会; 英国惠康基金;
关键词
APPROXIMATE BAYESIAN COMPUTATION; EXPERIMENTAL-DESIGN; MODEL SELECTION; PARAMETER-ESTIMATION; UNCERTAINTY; NETWORK; CELL; INFERENCE; IDENTIFIABILITY; SENSITIVITY;
D O I
10.1371/journal.pcbi.1002888
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Our understanding of most biological systems is in its infancy. Learning their structure and intricacies is fraught with challenges, and often side-stepped in favour of studying the function of different gene products in isolation from their physiological context. Constructing and inferring global mathematical models from experimental data is, however, central to systems biology. Different experimental setups provide different insights into such systems. Here we show how we can combine concepts from Bayesian inference and information theory in order to identify experiments that maximize the information content of the resulting data. This approach allows us to incorporate preliminary information; it is global and not constrained to some local neighbourhood in parameter space and it readily yields information on parameter robustness and confidence. Here we develop the theoretical framework and apply it to a range of exemplary problems that highlight how we can improve experimental investigations into the structure and dynamics of biological systems and their behavior.
引用
收藏
页数:13
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