Constructing and analysing transcriptional regulatory networks via graph algorithm-based integration of different genome-wide high-throughput data

Abstract

Transcriptional regulation is important for cellular responses to external stimuli The high throughput transcription factor knockout microarrays (TFKMs) provide useful information about gene regulation Besides high throughput chromatin immunoprecipitation (ChIP) experiments are now the most comprehensive approaches for identifying the binding sites of transcription factors (TFs) to their target genes These two methods unravel different aspects of transcriptional regulatory networks and have their own natural defects and insufficiency In this dissertation we first propose a method based on the biological knowledge to elucidate the molecular mechanisms for the causative TF-gene pairs identified by high throughput TFKMs The proposed method was applied to the TFKM analysis results of Reimand {em et al } We then demonstrate the biological significance of our refined (i e biologically interpretable) TF knockout targets by assessing their functional enrichment expression coherence and the prevalence of protein-protein interactions Our refined TF knockout targets outperformed the original TF knockout targets across all measures About seven hundred hypotheses of molecular mechanisms for the causative TF-gene pairs generated by our methods have been experimentally validated in the literature In the second part of the dissertation we provide a novel algorithm to extract functional TF-gene binding pairs from the results of high throughput ChIP experiments Compared with previous related works our method outperformed three existing methods The identified functional targets of TFs also showed statistical significance over the randomly assigned TF-gene pairs We also demonstrate that our method is dataset independent and can apply to ChIP-seq data and the {em E coli} genome In the third part of this dissertation we reconstruct the transcriptional regulatory networks in yeast For each TF-gene regulatory pair under different experimental conditions all possible transcriptional regulatory pathways in two underlying networks (constructed using experimentally verified TF-gene binding pairs and TF-gene regulatory pairs from the literature) for the specified experimental conditions were automatically enumerated by TRP mining procedures developed from the graph theory The transcriptional regulatory pathways mined out by our graph data mining procedures provide the panoramic static view on the yeast transcriptional regulatory network The proposed methods and results in this dissertation can facilitate biologists to design and analyse downstream experiments on the cellular regulatory mechanisms

Date of Award	2014 Dec 23
Original language	English
Supervisor	Wei-Sheng Wu (Supervisor)