Model-based design of experiments for cellular processes

被引:28
作者
Chakrabarty, Ankush [1 ]
Buzzard, Gregery T. [2 ]
Rundell, Ann E. [3 ]
机构
[1] Purdue Univ, Sch Elect & Comp Engn, W Lafayette, IN 47907 USA
[2] Purdue Univ, Dept Math, W Lafayette, IN 47907 USA
[3] Purdue Univ, Weldon Sch Biomed Engn, W Lafayette, IN 47907 USA
基金
美国国家科学基金会;
关键词
GLOBAL-SENSITIVITY-ANALYSIS; SEQUENTIAL EXPERIMENTAL-DESIGN; ROBUST EXPERIMENTAL-DESIGN; FISHER INFORMATION MATRIX; SYSTEMS BIOLOGY; PARAMETER-ESTIMATION; IDENTIFIABILITY ANALYSIS; PRACTICAL IDENTIFIABILITY; OPTIMAL IDENTIFICATION; SIGNAL-TRANSDUCTION;
D O I
10.1002/wsbm.1204
中图分类号
R-3 [医学研究方法]; R3 [基础医学];
学科分类号
1001 ;
摘要
Model-based design of experiments (MBDOE) assists in the planning of highly effective and efficient experiments. Although the foundations of this field are well-established, the application of these techniques to understand cellular processes is a fertile and rapidly advancing area as the community seeks to understand ever more complex cellular processes and systems. This review discusses the MBDOE paradigm along with applications and challenges within the context of cellular processes and systems. It also provides a brief tutorial on Fisher information matrix (FIM)-based and Bayesian experiment design methods along with an overview of existing software packages and computational advances that support MBDOE application and adoption within the Systems Biology community. As cell-based products and biologics progress into the commercial sector, it is anticipated that MBDOE will become an essential practice for design, quality control, and production. WIREs Syst Biol Med 2013, 5:181203. doi: 10.1002/wsbm.1204 For further resources related to this article, please visit the WIREs website.
引用
收藏
页码:181 / 203
页数:23
相关论文
共 140 条
[1]  
[Anonymous], 1993, OPTIMAL DESIGN EXPT
[2]  
[Anonymous], 2012, MODEL ORIENTED DESIG, DOI DOI 10.1007/978-1-4612-0703-0
[3]   Stimulus design for model selection and validation in cell signaling [J].
Apgar, Joshua F. ;
Toettcher, Jared E. ;
Endy, Drew ;
White, Forest M. ;
Tidor, Bruce .
PLOS COMPUTATIONAL BIOLOGY, 2008, 4 (02)
[4]   Sloppy models, parameter uncertainty, and the role of experimental design [J].
Apgar, Joshua F. ;
Witmer, David K. ;
White, Forest M. ;
Tidor, Bruce .
MOLECULAR BIOSYSTEMS, 2010, 6 (10) :1890-1900
[5]   Designing robust optimal dynamic experiments [J].
Asprey, SP ;
Macchietto, S .
JOURNAL OF PROCESS CONTROL, 2002, 12 (04) :545-556
[6]   Statistical tools for optimal dynamic model building [J].
Asprey, SP ;
Macchietto, S .
COMPUTERS & CHEMICAL ENGINEERING, 2000, 24 (2-7) :1261-1267
[7]   One hundred years of the design of experiments on and off the pages of Biometrika [J].
Atkinson, AC ;
Bailey, RA .
BIOMETRIKA, 2001, 88 (01) :53-97
[8]   A New Computational Tool for Establishing Model Parameter Identifiability [J].
August, Elias ;
Papachristodoulou, Antonis .
JOURNAL OF COMPUTATIONAL BIOLOGY, 2009, 16 (06) :875-885
[9]   Computational procedures for optimal experimental design in biological systems [J].
Balsa-Canto, E. ;
Alonso, A. A. ;
Banga, J. R. .
IET SYSTEMS BIOLOGY, 2008, 2 (04) :163-172
[10]   An iterative identification procedure for dynamic modeling of biochemical networks [J].
Balsa-Canto, Eva ;
Alonso, Antonio A. ;
Banga, Julio R. .
BMC SYSTEMS BIOLOGY, 2010, 4