Model-based design of experiments for cellular processes

被引:27
|
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 50 条
  • [1] Safe model-based design of experiments using Gaussian processes
    Petsagkourakis, Panagiotis
    Galvanin, Federico
    [J]. COMPUTERS & CHEMICAL ENGINEERING, 2021, 151
  • [2] Model-based design of parallel experiments
    Galvanin, Federico
    Macchietto, Sandro
    Bezzo, Fabrizio
    [J]. INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2007, 46 (03) : 871 - 882
  • [3] Integrated Bayesian parameter estimation with model-based design of experiments for dynamic processes
    Cao, Xinyu
    Chen, Xi
    Biegler, Lorenz T.
    [J]. AICHE JOURNAL, 2024, 70 (07)
  • [4] Model-Based Design of Experiments based on Local Model Networks for Nonlinear Processes with Low Noise Levels
    Hartmann, Benjamin
    Ebert, Tobias
    Nelles, Oliver
    [J]. 2011 AMERICAN CONTROL CONFERENCE, 2011, : 5306 - 5311
  • [5] Efficient model-based design of neurophysiological experiments
    Lewi, Jeremy
    Butera, Robert
    Paninski, Liam
    [J]. 2006 28TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-15, 2006, : 2855 - +
  • [6] Model-based design of experiments under structural model uncertainty
    Quaglio, Marco
    Fraga, Eric S.
    Galvanin, Federico
    [J]. 27TH EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING, PT A, 2017, 40A : 145 - 150
  • [7] Comparison of Different Approaches for the Model-Based Design of Experiments
    Reichert, Ina
    Olney, Peter
    Lahmer, Tom
    Zabel, Volkmar
    [J]. MODEL VALIDATION AND UNCERTAINTY QUANTIFICATION, VOL 3, 2015, : 135 - 141
  • [8] Influence of the error description on model-based design of experiments
    Reichert, I.
    Olney, P.
    Lahmer, T.
    [J]. ENGINEERING STRUCTURES, 2019, 193 : 100 - 109
  • [9] Towards on-line model-based design of experiments
    Galvanin, Federico
    Barolo, Massimiliano
    Bezzo, Fabrizio
    [J]. 18TH EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING, 2008, 25 : 349 - 354
  • [10] Backoff-Based Model-Based Design of Experiments Under Model Mismatch
    Petsagkourakis, Panagiotis
    Galvanin, Federico
    [J]. 30TH EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING, PTS A-C, 2020, 48 : 1777 - 1782