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This article is part of the supplement: Selected articles from the 4th International Conference on Computational Systems Biology (ISB 2010)

Open Access Report

Modeling and analysis of the dynamic behavior of the XlnR regulon in Aspergillus niger

Jimmy Omony12*, Leo H de Graaff2, Gerrit van Straten1 and Anton J B van Boxtel1

Author Affiliations

1 Systems and Control group, Wageningen University, Wageningen, P.O. Box 17, 6700 AA, The Netherlands

2 Laboratory of Systems and Synthetic Biology, Wageningen University, Wageningen, Dreijenplein 10, 6703 HB, The Netherlands

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BMC Systems Biology 2011, 5(Suppl 1):S14  doi:10.1186/1752-0509-5-S1-S14

Published: 20 June 2011

Abstract

Background

In this paper the dynamics of the transcription-translation system for XlnR regulon in Aspergillus niger is modeled. The model is based on Hill regulation functions and uses ordinary differential equations. The network response to a trigger of D-xylose is considered and stability analysis is performed. The activating, repressive feedback, and the combined effect of the two feedbacks on the network behavior are analyzed.

Results

Simulation and systems analysis showed significant influence of activating and repressing feedback on metabolite expression profiles. The dynamics of the D-xylose input function has an important effect on the profiles of the individual metabolite concentrations. Variation of the time delay in the feedback loop has no significant effect on the pattern of the response. The stability and existence of oscillatory behavior depends on which proteins are involved in the feedback loop.

Conclusions

The dynamics in the regulation properties of the network are dictated mainly by the transcription and translation degradation rate parameters, and by the D-xylose consumption profile. This holds true with and without feedback in the network. Feedback was found to significantly influence the expression dynamics of genes and proteins. Feedback increases the metabolite abundance, changes the steady state values, alters the time trajectories and affects the response oscillatory behavior and stability conditions. The modeling approach provides insight into network behavioral dynamics particularly for small-sized networks. The analysis of the network dynamics has provided useful information for experimental design for future in vitro experimental work.