Robust simplifications of multiscale biochemical networks

Abstract : 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. Last but not least, model reduction is an appropriate tool to study biological robustness. 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 Gorban and Radulescu 08. 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 NFkB pathway. Conclusions: 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.
Type de document :
Article dans une revue
BMC Systems Biology, BioMed Central, 2008, 2:86
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Contributeur : Ovidiu Radulescu <>
Soumis le : mercredi 15 octobre 2008 - 16:59:52
Dernière modification le : samedi 24 mars 2018 - 01:51:05


  • HAL Id : inria-00331212, version 1


Ovidiu Radulescu, Alexander Gorban, Andrei Zinovyev, Alain Lilienbaum. Robust simplifications of multiscale biochemical networks. BMC Systems Biology, BioMed Central, 2008, 2:86. 〈inria-00331212〉



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