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A Combinatorial Approach to Determine the Context‑Dependent Role in Transcriptional and Posttranscriptional Regulation in Arabidopsis Thaliana

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A Combinatorial Approach to Determine the Context‑Dependent Role in Transcriptional and Posttranscriptional Regulation in Arabidopsis Thaliana This document is downloaded from DR‑NTU (https://dr.ntu.edu.sg) Nanyang Technological University, Singapore. A combinatorial approach to determine the context‑dependent role in transcriptional and posttranscriptional regulation in Arabidopsis Thaliana Lu, Le 2008 Lu, L. (2008). Combinatorial approach to determine the context‑dependent role in transcriptional and posttranscriptional regulation in Arabidopsis Thaliana. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/6569 https://doi.org/10.32657/10356/6569 Nanyang Technological University Downloaded on 01 Oct 2021 01:50:53 SGT ATTENTION: The Singapore Copyright Act applies to the use of this document. Nanyang Technological University Library A Combinatorial Approach to Determine the Context-dependent Role in Transcriptional and Posttranscriptional Regulation in Arabidopsis Thaliana Lu Le School of Biological Sciences A thesis submitted to the Nanyang Technological University in fulfillment of the requirements for the degree of Doctor of Philosophy 2008 ATTENTION: The Singapore Copyright Act applies to the use of this document. Nanyang Technological University Library ACKNOWLEDGEMENTS First of all, I would like to express my sincere gratitude to my supervisor, Dr. Li Jinming, to whom I really owe a debt, for his care and encouragement, his patience and generosity, his creative ideas and invaluable guidance, and so on and so forth. It is his support and help that made this thesis possible. I would like to thank my co-supervisor Dr. Peter Dröge for his great help and valuable suggestions during my four-year’s postgraduate study. A special thank to my labmate Jia Hui and Xiao Jie, for their friendship, good spirits and various helps. Thanks to my dear friend Qu Yi and Zhang Xiaohong for everything they did for me in the past years. I acknowledge the following people for the permission to reproduce their figures in this thesis: Dr. Phillip D. Zamore at University of Massachusetts Medical School (Figure 1.1.1), Dr. Alexander Schliep at Max Planck Institute for Molecular Genetics in Germany (Figure 1.2.1), Dr. Timothy Gardner at Boston University (Figure 1.4.1 and 1.4.2), and Dr. David Bartel at Whitehead Institute for Biomedical Research in USA (Figure 3.1.1). Last, but not least, I thank my parents and grandparents for their love, which will never end. II ATTENTION: The Singapore Copyright Act applies to the use of this document. Nanyang Technological University Library TABLE OF CONTENTS ACKNOWLEDGEMENTS ............................................................................ II TABLE OF CONTENTS .............................................................................. III LIST OF FIGURES......................................................................................VI LIST OF TABLES........................................................................................VI SUMMARY ...................................................................................................X CHAPTER 1: INTRODUCTION................................................................. 1 1.1 Biological background of the transcriptional and posttranscriptional regulation ..............3 1.1.1 Transcriptional regulation..................................................................................................4 1.1.2 Posttranscriptional regulation............................................................................................5 1.2 Gene regulation analysis through clustering of gene expression data ..................................9 1.2.1 Clustering analysis of microarray data: heuristic clustering methods .............................10 1.2.2 Clustering analysis of microarray data: model-based clustering methods.......................14 1.3 Detection of transcriptional and posttranscriptional regulatory motifs.............................18 1.3.1 Identifying the cis-regulatory control elements shared by the co-expressed genes .........19 1.3.2 Identifying the miRNA target motifs...............................................................................24 1.4 Recent approaches aimed at elucidating gene regulatory networks...................................29 1.4.1 Boolean network models .................................................................................................31 1.4.2 Differential equation models ...........................................................................................32 1.4.3 Bayesian network models................................................................................................34 CHAPTER 2: STATISTIC METHODS – HIDDEN MARKOV MODEL AND BAYESIAN NETWORK ..................................................................... 39 2.1 Hidden Markov model............................................................................................................39 2.1.1 Markov chains .................................................................................................................39 2.1.2 Elements of an HMM ......................................................................................................40 2.1.3 Forward algorithm...........................................................................................................41 2.1.4 Backward algorithm ........................................................................................................42 2.1.5 Viterbi algorithm .............................................................................................................43 2.1.6 Parameter estimation for HMMs: Baum-Welch algorithm..............................................44 III ATTENTION: The Singapore Copyright Act applies to the use of this document. Nanyang Technological University Library 2.2 Bayesian network model.........................................................................................................48 2.2.1 Induction of probabilistic network from data ..................................................................49 2.2.2 K2 algorithm....................................................................................................................50 2.2.3 Predictions of interest......................................................................................................51 CHAPTER 3: PREDICTION OF PLANT MIRNA TARGETS ................... 53 3.1 Introduction.............................................................................................................................54 3.2 Materials and Methods...........................................................................................................57 3.2.1 Materials..........................................................................................................................57 3.2.2 Methods...........................................................................................................................57 3.3 Results......................................................................................................................................62 3.3.1 Training set preparation...................................................................................................62 3.3.2 Simulation study using randomly shuffled sequences.....................................................68 3.3.3 Detecting miRNA target motifs using inhomogeneous HMM ........................................72 3.4 Discussion.................................................................................................................................75 3.4.1 Four methods used to generate randomly shuffled sequences.........................................75 3.4.2 miRNA targets prediction without conservation requirement .........................................78 3.4.3 miRNAs are biased toward target TFs and other regulatory genes..................................79 3.4.4 Posttranscriptional regulation of gene expression by miRNA.........................................79 CHAPTER 4: RECONSTRUCTING REGULATORY NETWORKS ......... 82 4.1 Introduction.............................................................................................................................82 4.2 Materials and methods ...........................................................................................................87 4.2.1 Materials..........................................................................................................................87 4.2.2 Methods...........................................................................................................................88 4.3 Results......................................................................................................................................92 4.3.1 Gene expression dynamics in cop1 mutant time course experiment ...............................92 4.3.2 Discovery of transcriptional and posttranscriptional regulatory motifs using cop1 mutant time-course microarray data ............................................................................................104 4.3.3 Predicting gene expression patterns...............................................................................112 4.4 Discussion...............................................................................................................................114 4.4.1 COP1 acting as a repressor in Arabidopsis photomorphogenic development ...............114 4.4.2 Transcriptional and posttranscriptional regulatory networks.........................................118 4.4.3 Microarray data
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