How to deal with parameters for whole-cell modelling

被引:60
作者
Babtie, Ann C. [1 ]
Stumpf, Michael P. H. [1 ]
机构
[1] Imperial Coll London, Dept Life Sci, London, England
基金
英国生物技术与生命科学研究理事会;
关键词
whole-cell models; statistical inference; parameter estimation; model selection; SYSTEMS BIOLOGY; NETWORK INFERENCE; GENE-EXPRESSION; IDENTIFIABILITY ANALYSIS; POSITIONAL INFORMATION; BAYESIAN-APPROACH; DYNAMICAL MODELS; GROWTH LAWS; SENSITIVITY; SELECTION;
D O I
10.1098/rsif.2017.0237
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Dynamical systems describing whole cells are on the verge of becoming a reality. But as models of reality, they are only useful if we have realistic parameters for the molecular reaction rates and cell physiological processes. There is currently no suitable framework to reliably estimate hundreds, let alone thousands, of reaction rate parameters. Here, we map out the relative weaknesses and promises of different approaches aimed at redressing this issue. While suitable procedures for estimation or inference of the whole (vast) set of parameters will, in all likelihood, remain elusive, some hope can be drawn from the fact that much of the cellular behaviour may be explained in terms of smaller sets of parameters. Identifying such parameter sets and assessing their behaviour is now becoming possible even for very large systems of equations, and we expect such methods to become central tools in the development and analysis of whole-cell models.
引用
收藏
页数:11
相关论文
共 107 条
[91]   A community-driven global reconstruction of human metabolism [J].
Thiele, Ines ;
Swainston, Neil ;
Fleming, Ronan M. T. ;
Hoppe, Andreas ;
Sahoo, Swagatika ;
Aurich, Maike K. ;
Haraldsdottir, Hulda ;
Mo, Monica L. ;
Rolfsson, Ottar ;
Stobbe, Miranda D. ;
Thorleifsson, Stefan G. ;
Agren, Rasmus ;
Boelling, Christian ;
Bordel, Sergio ;
Chavali, Arvind K. ;
Dobson, Paul ;
Dunn, Warwick B. ;
Endler, Lukas ;
Hala, David ;
Hucka, Michael ;
Hull, Duncan ;
Jameson, Daniel ;
Jamshidi, Neema ;
Jonsson, Jon J. ;
Juty, Nick ;
Keating, Sarah ;
Nookaew, Intawat ;
Le Novere, Nicolas ;
Malys, Naglis ;
Mazein, Alexander ;
Papin, Jason A. ;
Price, Nathan D. ;
Selkov, Evgeni, Sr. ;
Sigurdsson, Martin I. ;
Simeonidis, Evangelos ;
Sonnenschein, Nikolaus ;
Smallbone, Kieran ;
Sorokin, Anatoly ;
van Beek, Johannes H. G. M. ;
Weichart, Dieter ;
Goryanin, Igor ;
Nielsen, Jens ;
Westerhoff, Hans V. ;
Kell, Douglas B. ;
Mendes, Pedro ;
Palsson, Bernhard O. .
NATURE BIOTECHNOLOGY, 2013, 31 (05) :419-+
[92]   Genome-Scale Reconstruction of Escherichia coli's Transcriptional and Translational Machinery: A Knowledge Base, Its Mathematical Formulation, and Its Functional Characterization [J].
Thiele, Ines ;
Jamshidi, Neema ;
Fleming, Ronan M. T. ;
Palsson, Bernhard O. .
PLOS COMPUTATIONAL BIOLOGY, 2009, 5 (03)
[93]  
Thomas R, 1990, BIOL FEEDBACK
[94]   Cause and cure of sloppiness in ordinary differential equation models [J].
Toensing, Christian ;
Timmer, Jens ;
Kreutz, Clemens .
PHYSICAL REVIEW E, 2014, 90 (02)
[95]   Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems [J].
Toni, Tina ;
Welch, David ;
Strelkowa, Natalja ;
Ipsen, Andreas ;
Stumpf, Michael P. H. .
JOURNAL OF THE ROYAL SOCIETY INTERFACE, 2009, 6 (31) :187-202
[96]   Perspective: Sloppiness and emergent theories in physics, biology, and beyond [J].
Transtrum, Mark K. ;
Machta, Benjamin B. ;
Brown, Kevin S. ;
Daniels, Bryan C. ;
Myers, Christopher R. ;
Sethna, James P. .
JOURNAL OF CHEMICAL PHYSICS, 2015, 143 (01)
[98]   A Bayesian approach to targeted experiment design [J].
Vanlier, J. ;
Tiemann, C. A. ;
Hilbers, P. A. J. ;
van Riel, N. A. W. .
BIOINFORMATICS, 2012, 28 (08) :1136-1142
[99]  
Vernon I, 2016, BAYESIAN UNCERTAINTY
[100]   Structural Identifiability of Dynamic Systems Biology Models [J].
Villaverde, Alejandro F. ;
Barreiro, Antonio ;
Papachristodoulou, Antonis .
PLOS COMPUTATIONAL BIOLOGY, 2016, 12 (10)