Kok, Eng Piew (2012) Peculiar feed-forward loop network motifs in Escherichia coli / Kok Eng Piew. Masters thesis, University of Malaya.
Abstract
A feed-forward loop (FFL) is one type of transcription network motifs studied in systems biology. So far, eight types of FFL based on their regulatory patterns have been identified. The majority of FFL found in transcription networks of biological systems have been identified to belong to coherent and incoherent type 1 FFL. Nevertheless, six FFL types (coherent and incoherent type 2, 3 and 4 FFL) are present in low frequencies in Escherichia coli. The persistence and not elimination of these “peculiar” FFL through natural selection is perplexing. One way to understanding this conundrum is to study the dynamics of these FFL and empirically study their relative abundance using public databases. To this end, data mining from RegulonDB and simulation using CellDesigner 4.2 were carried out. Coherent FFL shows delay in target gene expression upon the activation or repression by the activated forms of first and second transcription factors. Incoherent FFL shows acceleration of target gene expression that results in over production of target gene product. In particular, the acceleration of target gene expression in incoherent type 3 and 4 FFL are highly dependent on the promoter binding activity of the second transcription factor. We identified 84 of 1702 transcription factor-operon interactions in RegulonDB to be involved in FFL transcription networks in E. coli. A total of 28 FFL were identified, nine of them found to participate in biochemical processes such as maltose utilization, arabinose utilization and anaerobic respiration. The efficacy of peculiar FFL was discussed in the context of these processes. We find that in silico simulation of FFL dynamics using CellDesigner 4.2 provide explicit results that are useful for guiding biological interpretation of important biological processes in E. coli. It is also a practical means of generating useful hypotheses in gene regulatory networks for experimental validation in the laboratory
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