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Record number 407169
Title Modeling and analysis of the dynamic behavior of the XlnR regulon in Aspergillus niger
Author(s) Omony, J.; Graaff, L.H. de; Straten, G. van; Boxtel, A.J.B. van
Source BMC Systems Biology 5 (2011)Suppl. 1. - ISSN 1752-0509 - 14 p.
Department(s) Systems and Control Group
Systems and Synthetic Biology
Publication type Refereed Article in a scientific journal
Publication year 2011
Keyword(s) gene-expression - transcriptional regulation - regulatory networks - time delays - systems - oscillations - circuits - endoglucanase - approximation - degradation
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
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