The dynamics of the XlnR regulon of Aspergillus niger: a systems biology approach

The study of biological networks has tremendously improved our understanding of complex bio-molecular processes. To be confident of the findings from a biological network reconstruction, it is important that high quality data sets obtained from well-designed experiments are used. This work focused on studying the xylanolytic activator XlnR regulon of the filamentous fungi A. niger, which has many industrial and commercial values. For instance, it is used in the production of citric acid, in the pulp and paper industry and in many industrial fermentation processes. Various hypotheses were tested in this work, for example a comparison of the performance of time course experiments, variations in gene expression, and the qualitative and quantitative effect of the catabolic repressor protein (CreA) of the XlnR regulon. Transcriptions in the wild type and mutant strains were also compared following induction by D-xylose. By perturbing the regulon with low and high D-xylose concentrations, the target genes revealed various transcription intensities and in some cases varying profiles in time. This is crucial for understanding the network dynamics and parameter identification during the network inference. Mathematical modelling was used to investigate the regulation mechanisms for the target genes in the XlnR regulion. Model-guided designs for time course experiments were studied in a bid to find strategies that enrich the information content of the data sets. In particular kinetic modelling, coupled with network structural prior knowledge, was used to study the network and answer the research questions of interest. This undertaking significantly improved our understanding of how the target genes are regulated following various external perturbation strategies