Synergistic control of oscillations in the NF-κB signalling pathway

被引:41
作者
Ihekwaba, AEC
Broomhead, DS
Grimley, R
Benson, N
White, MRH
Kell, DB
机构
[1] Univ Manchester, Sch Chem, Manchester M60 1QD, Lancs, England
[2] Univ Manchester, Sch Math, Manchester M60 1QD, Lancs, England
[3] Pfizer Global Res & Dev, Sandwich CT13 9NJ, Kent, England
[4] Sch Biol Sci, Ctr Cell Imaging, Liverpool L69 7ZB, Merseyside, England
来源
IEE PROCEEDINGS SYSTEMS BIOLOGY | 2005年 / 152卷 / 03期
关键词
D O I
10.1049/ip-syb:20050050
中图分类号
Q2 [细胞生物学];
学科分类号
071009 ; 090102 ;
摘要
In previous work, we studied the behaviour of a model of part of the NF-kappa B signalling pathway. The model displayed oscillations that varied both in number, amplitude and frequency when its parameters were varied. Sensitivity analysis showed that just nine of the 64 reaction parameters were mainly responsible for the control of the oscillations when these parameters were varied individually. However, the control of the properties of any complex system is distributed, and, as many of these reactions are highly non-linear, we expect that their interactions will be too. Pairwise modulation of these nine parameters gives a search space some 50 times smaller (81 against 4096) than that required for the pairwise modulation of all 64 reactions, and this permitted their study (which would otherwise have been effectively intractable). Strikingly synergistic effects were observed, in which the effect of one of the parameters was strongly (and even qualitatively) dependent on the values of another parameter. Regions of parameter space could be found in which the amplitude, but not the frequency (timing), of oscillations varied, and vice versa. Such modelling will permit the design and performance of experiments aimed at disentangling the role of the dynamics of oscillations, rather than simply their amplitude, in determining cell fate. Overall, the analyses reveal a level of complexity in these dynamic models that is not apparent from study of their individual parameters alone and point to the value of manipulating multiple elements of complex networks to achieve desired physiological effects.
引用
收藏
页码:153 / 160
页数:8
相关论文
共 73 条
[1]  
Lauffenburger D.A., Cell signaling pathways as control modules: Complexity for simplicity?, Proc. Natl. Acad. Sci. USA, 97, pp. 5031-5033, (2000)
[2]  
Gardner T.S., di Bernardo D., Lorenz D., Collins J.J., Inferring genetic networks and identifying compound mode of action via expression profiling, Science, 301, pp. 102-105, (2003)
[3]  
Sontag E., Kiyatkin A., Kholodenko B.N., Inferring dynamic architecture of cellular networks using time series of gene expression, protein and metabolite data, Bioinformatics, 20, pp. 1877-1886, (2004)
[4]  
Alm E., Arkin A.P., Biological networks, Curr. Opin. Struct. Biol., 13, pp. 193-202, (2003)
[5]  
Papin J.A., Hunter T., Palsson B.O., Subramaniam S., Reconstruction of cellular signalling networks and analysis of their properties, Nat. Rev. Mol. Cell. Biol., 6, pp. 99-111, (2005)
[6]  
Barabasi A.-L., Oltvai Z.N., Network biology: Understanding the cell's functional organization, Nat. Rev. Genet., 5, pp. 101-113, (2004)
[7]  
Santo H.M., Kholodenko B.N., Quantitative analysis of signaling networks, Prog. Biophys. Mol. Biol., 86, pp. 5-43, (2004)
[8]  
Sachs K., Perez O., Pe'er D., Lauffenburger D.A., Nolan G.P., Causal protein-signaling networks derived from multiparameter single-cell data, Science, 308, pp. 523-529, (2005)
[9]  
Tyson J.J., Chen K.C., Novak B., Sniffers, buzzers, toggles and blinkers: Dynamics of regulatory and signaling pathways in the cell, Curr. Opin. Cell. Biol., 15, pp. 221-231, (2003)
[10]  
Csete M.E., Doyle J.C., Reverse engineering of biological complexity, Science, 295, pp. 1664-1669, (2002)