Radulescu2008 - NF-κB hierarchy ℳ(16,34,46)

Radulescu2008 - NF-κB hierarchy ℳ(16,34,46)
This is a model of NF-κB pathway functioning from hierarchy of models of decreasing complexity, created to demonstrate application of model reduction methods proposed in the associated publication.
The name of the model M(x,y,z) should be deciphered as following:
x: number of species
y: number of reactions
z: number of parameters

Simulation protocol: the model can be simulated in CellDesigner directly, or in any simulator supporting events. The simulation period should be set up in 40 hours (t=144000 sec). The 'signal' event applies signal to the pathway at the moment t=20 hours=72000 sec.

This model is described in the article:
Robust simplifications of multiscale biochemical networks.

Radulescu O, Gorban AN, Zinovyev A, Lilienbaum A.
BMC Syst Biol 2008 Oct; 2(1):86
Abstract:
BACKGROUND: Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed.
RESULTS: We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as "pseudo-monomolecular" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway.
CONCLUSION: Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in "middle-out" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models.

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