Reconstruction of Transcription Regulatory Network (TRN) and Transcription Factor Activity (TFA) from gene expression data is an important problem in systems biology. Currently, there exist various factor analysis methods for TRN reconstruction, but most approaches have specific assumptions not satisfied by real biological data. Network Component Analysis (NCA) can handle such limitations and is considered to be one of the most effective methods. The prerequisite for NCA is knowledge of the structure of TRN. Such structure can be obtained from ChIP-chip or ChIP-seq experiments, which however have quite limited applications. In order to cope with the difficulty, we resort to heuristic optimization algorithm such as Particle Swarm Optimization (PSO), in order to explore the possible structures of TRN and choose the most plausible one. Regarding the structure estimation problem, we extend classical PSO and propose a novel Probabilistic binary PSO. Furthermore, an improved NCA called FastNCA is adopted to compute the objective function accurately and fast, which enables PSO to run efficiently. Since heuristic optimization cannot guarantee global convergence, we run PSO multiple times and integrate the results. Then GCV-LASSO (Generalized Cross Validation - Least Absolute Shrinkage and Selection Operator) is performed to estimate TRN. We apply our approach and other factor analysis methods on the synthetic data. The results indicate that the proposed PSOFastNCA-GCV-LASSO algorithm gives better estimation. In order to incorporate more prior information on TRN structure and gene expression dynamics in the linear factor analysis model for improved estimation of TRN and TFAs, a linear Bayesian framework is adopted. Under the unified Bayesian framework, Bayesian Linear Sparse Factor Analysis Model (BLSFM) and Bayesian Linear State Space Model (BLSSM) are developed for instantaneous and dynamic TRN, respectively. Various approaches to incorporate partial and ambiguous prior network structure information in the Bayesian framework are proposed to improve performance in practical applications. Furthermore, we propose a novel mechanism for estimating the hyper-parameters of the distribution priors in our BLSFM and BLSSM models, which can significantly improve the estimation compared to traditional ways of hyper-parameter setting. With this development, reasonably good estimation of TFAs and TRN can be obtained even without use of any structure prior of TRN. Extensive numerical experiments are performed to investigate our developed methods under various settings, with comparison to some existing alternative approaches. It is demonstrated that our hyper-parameter estimation method improves the estimation of TFA and TRN in most settings and has superior performance, and that structure priors in general leads to improved estimation performance. Regarding application to real biological data, we execute the PSO-FastNCAGCV-LASSO algorithm developed in the thesis using E. Coli microarray data and obtain sensible estimation of TFAs and TRN. We apply BLSFM without structure priors of TRN, BLSSM without structure priors as well as with partial structure priors to Yeast S. cerevisiae microarray data and obtain a reasonable estimation of TFAs and TRN. / published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

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published_or_final_version / Zoology / Master / Master of Philosophy

published_or_final_version / Zoology / Master / Master of Philosophy

The rpoB gene coding for a β-like subunit (homologous to the E. coli DNA-dependent RNA polymerase β subunit) of the chloroplast DNA-dependent RNA polymerase was located on the chloroplast genome of Euglena gracilis distal to the rrnC ribosomal RNA operon. The complete nucleotide sequence of the gene was determined. The sequence includes 97 bp of the 5S rRNA gene, an intergenic spacer of 1264 bp, the rpoB gene of 4249 bp, 84 bp spacer and 67 bp of the rpoC1 gene. The rpoB gene is of the same polarity as the rRNA operons. The organization of the rpoB and rpoC genes resemble the E. coli rpoB-rpoC and higher plants chloroplast rpoB-rpoC1-rpoC2 operons. The Euglena rpoB gene (1082 codons) encodes a polypeptide with predicted molecular weight of 124,288. The rpoB gene is interrupted by seven Group III introns of 93, 95, 94, 99, 101, 110 and 99 bp, respectively, and a Group II intron of 309 bp. All other known chloroplast rpoB genes lack introns. All the exon-exon junctions were experimentally determined by cDNA cloning and sequencing or direct primer extension RNA sequencing. Transcripts from the rpoB locus were characterized by Northern hybridization. Fully-spliced, monocistronic rpoB mRNAs, as well as rpoB-rpoC1 and rpoB-rpoC1-rpoC2 mRNAs were identified. Unspliced intron-containing transcripts could not be detected in these experiments. The rpoB gene is the first gene in the RNA polymerase rpoB-rpoC1-rpoC2 transcription unit. The three genes are transcribed from a promoter located upstream the rpoB gene. The transcript is processed to mature monocistronic mRNAs. The relative abundance of the mono-, di- and tricistronic mRNAs appear to be similar in RNAs isolated from photoautotrophic, heterotrophic and dark grown cells. The mature 5'- and 3'-ends of the mature rpoB monocistronic transcripts were determined via S1 nuclease mapping and primer extension RNA sequencing. In addition, the sequence of the 23S rRNA from the rrnC operon and the intergenic spacer between the rrnA and rrnB operon were determined. Transcription initiation for the ribosomal RNA transcription unit was determined via Northern analysis and S1 nuclease mapping of chloroplast RNA that was in vitro 5'-end labeled. Two transcription initiation sites were mapped at positions +1 and -50 upstream the 16S rRNA gene. The 3'-ends of the rrnA/rrnB and rrnC 5S rRNA were determined using S1 nuclease protection experiments. The protected fragments were of identical size. The rpoB-C1-C2 DNA sequence has been submitted to EMBL, accession number X17171, and the 23S rRNA DNA sequence was given the number X13310.

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