Sensitivity analysis of parameters controlling oscillatory signalling in the NF-/sub K/Bpathway: the roles of IKK and I/sub K/B/sub alpha/

Analysis of cellular signalling interactions is expected to create an enormous informatics challenge, perhaps even greater than that of analysing the genome. A key step in the evolution towards a more quantitative understanding of signalling is to specify explicitly the kinetics of all chemical reaction steps in a pathway. We have reconstructed a model of the nuclear factor, kappaB (NF-kappaB) signalling pathway, containing 64 parameters and 26 variables, including steps in which the activation of the NF-kappaB transcription factor is intimately associated with the phosphorylation and ubiquitination of its inhibitor kappaB by a membrane-associated kinase, and its translocation from the cytoplasm to the nucleus. We apply sensitivity analysis to the model. This identifies those parameters in this (IkappaB)/NF-kappaB signalling system (containing only induced IkappaBalpha isoform) that most affect the oscillatory concentration of nuclear NF-kappaB (in terms of both period and amplitude). The intention is to provide guidance on which proteins are likely to be most significant as drug targets or should be exploited for further, more detailed experiments. The sensitivity coefficients were found to be strongly dependent upon the magnitude of the parameter change studied, indicating the highly non-linear nature of the system. Of the 64 parameters in the model, only eight to nine exerted a major control on nuclear NF-kappaB oscillations, and each of these involved as reaction participants either the IkappaB kinase (IKK) or IkappaBalpha, directly. This means that the dominant dynamics of the pathway can be reflected, in addition to that of nuclear NF-kappaB itself, by just two of the other pathway variables. This is conveniently observed in a phase-plane plot.

[1]  Douglas B. Kell,et al.  Metabolic control theory: its role in microbiology and biotechnology , 1986 .

[2]  D. Fell Metabolic control analysis: a survey of its theoretical and experimental development. , 1992, The Biochemical journal.

[3]  G P Nolan,et al.  The p65 subunit of NF-kappa B regulates I kappa B by two distinct mechanisms. , 1993, Genes & development.

[4]  H. Kacser,et al.  The control of flux. , 1995, Biochemical Society transactions.

[5]  D. Kell,et al.  What bio technologists knew all along...? , 1996, Journal of theoretical biology.

[6]  H. Suyang,et al.  Role of unphosphorylated, newly synthesized IκBβ in persistent activation of NF-κB , 1996 .

[7]  S. Ghosh,et al.  Role of unphosphorylated, newly synthesized I kappa B beta in persistent activation of NF-kappa B , 1996, Molecular and cellular biology.

[8]  R. Heinrich,et al.  The Regulation of Cellular Systems , 1996, Springer US.

[9]  D. Fell Understanding the Control of Metabolism , 1996 .

[10]  Matthias Reuss,et al.  Dynamic sensitivity analysis for metabolic systems , 1997 .

[11]  M J May,et al.  NF-kappa B and Rel proteins: evolutionarily conserved mediators of immune responses. , 1998, Annual review of immunology.

[12]  Douglas B. Kell,et al.  Non-linear optimization of biochemical pathways: applications to metabolic engineering and parameter estimation , 1998, Bioinform..

[13]  G. Ghosh,et al.  IκBα Functions through Direct Contacts with the Nuclear Localization Signals and the DNA Binding Sequences of NF-κB* , 1998, The Journal of Biological Chemistry.

[14]  Ebrahim Zandi,et al.  Direct Phosphorylation of IκB by IKKα and IKKβ: Discrimination Between Free and NF-κB-Bound Substrate , 1998 .

[15]  R. Heilker,et al.  The kinetics of association and phosphorylation of IkappaB isoforms by IkappaB kinase 2 correlate with their cellular regulation in human endothelial cells. , 1999, Biochemistry.

[16]  Felix Freuler,et al.  All three IκB isoforms and most Rel family members are stably associated with the IκB kinase 1/2 complex , 1999 .

[17]  M. Karin,et al.  Phosphorylation meets ubiquitination: the control of NF-[kappa]B activity. , 2000, Annual review of immunology.

[18]  P. Herrlich,et al.  UV-Induced Stabilization of c-fos and Other Short-Lived mRNAs , 2000, Molecular and Cellular Biology.

[19]  Inder M. Verma,et al.  Signal-dependent and -independent Degradation of Free and NF-κB-bound IκBα* , 2000, The Journal of Biological Chemistry.

[20]  Eva E. Qwarnstrom,et al.  Dynamic Shuttling of Nuclear Factor κB between the Nucleus and Cytoplasm as a Consequence of Inhibitor Dissociation* , 2000, The Journal of Biological Chemistry.

[21]  L. Acerenza Design of large metabolic responses. Constraints and sensitivity analysis. , 2000, Journal of theoretical biology.

[22]  Douglas B. Kell,et al.  MEG (Model Extender for Gepasi): a program for the modelling of complex, heterogeneous, cellular systems , 2001, Bioinform..

[23]  W. Tam,et al.  IκB Family Members Function by Different Mechanisms* , 2001, The Journal of Biological Chemistry.

[24]  T. Ideker,et al.  A new approach to decoding life: systems biology. , 2001, Annual review of genomics and human genetics.

[25]  Hong-shan Wang,et al.  BAFF-induced NEMO-independent processing of NF-κB2 in maturing B cells , 2002, Nature Immunology.

[26]  A. Hoffmann,et al.  The I (cid:1) B –NF-(cid:1) B Signaling Module: Temporal Control and Selective Gene Activation , 2022 .

[27]  Ravi Iyengar,et al.  Modeling Signaling Networks , 2002, Science.

[28]  Eduardo Sontag,et al.  Untangling the wires: A strategy to trace functional interactions in signaling and gene networks , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[29]  Hans V Westerhoff,et al.  Control analysis for autonomously oscillating biochemical networks. , 2002, Biophysical journal.

[30]  Bernd R. Binder,et al.  Signaling Molecules of the NF-κB Pathway Shuttle Constitutively between Cytoplasm and Nucleus* , 2002, The Journal of Biological Chemistry.

[31]  Masaru Tomita,et al.  Computational Challenges in Cell Simulation: A Software Engineering Approach , 2002, IEEE Intell. Syst..

[32]  P. Brazhnik,et al.  Linking the genes: inferring quantitative gene networks from microarray data. , 2002, Trends in genetics : TIG.

[33]  Kwang-Hyun Cho,et al.  Investigations Into the Analysis and Modeling of the TNFα-Mediated NF-κB-Signaling Pathway , 2003 .

[34]  R. Callard,et al.  From the top down: towards a predictive biology of signalling networks. , 2003, Trends in biotechnology.

[35]  Kwang-Hyun Cho,et al.  Experimental Design in Systems Biology, Based on Parameter Sensitivity Analysis Using a Monte Carlo Method: A Case Study for the TNFα-Mediated NF-κ B Signal Transduction Pathway , 2003, Simul..

[36]  O Wolkenhauer,et al.  Analysis and modelling of signal transduction pathways in systems biology. , 2003, Biochemical Society transactions.

[37]  Jason A. Papin,et al.  Metabolic pathways in the post-genome era. , 2003, Trends in biochemical sciences.

[38]  Herbert M Sauro,et al.  Sensitivity analysis of stoichiometric networks: an extension of metabolic control analysis to non-steady state trajectories. , 2003, Journal of theoretical biology.

[39]  Michael Karin,et al.  The IKK NF-κB system: a treasure trove for drug development , 2004, Nature Reviews Drug Discovery.

[40]  Uri Alon,et al.  Dynamics of the p53-Mdm2 feedback loop in individual cells , 2004, Nature Genetics.

[41]  D. Kell Metabolomics and systems biology: making sense of the soup. , 2004, Current opinion in microbiology.