Antimicrobial peptides are active against bacteria, fungi and viruses as part of the innate immune system in animals and insects. Such peptides are currently produced by extracting them from the host organism or by solid phase peptide synthesis; both techniques are expensive and produce low yields. Recombinant DNA technology opens a window to produce these peptides inexpensively and in large quantities utilizing E. coli expression systems. Two antimicrobial peptides, indolicidin and PGQ, were the focus of this work. They are short amphipathic alpha helical antimicrobial peptides that target a broad range of microorganisms. Genes encoding multimers of indolicidin, PGQ and a hybrid of indolicidin:PGQ were placed into protein expression vectors pET32a+ and pET43.1a+, for peptide production in E. coli. A combination of multimerization and the use of a fusion protein were utilized to mask the toxicity of these peptides in E. coli. The multimerized peptide fusion construct was purified using Ni/NTA affinity chromatography. Methionine residues flanking each monomeric unit were utilized to enable cleavage of the multimerized protein and liberating a biologically active peptide. A Trx:indolicidin trimer fusion was produced in the greatest yield of all constructs investigated. Upon cyanogen bromide cleavage, a band corresponding to the theoretical molecular weight of an indolicidin monomer was observed with SDS-PAGE. Antimicrobial activity of monomeric recombinant indolicidin was tested resulting in zones of clearing. Overall the results indicate that multimerizing antimicrobial peptide genes can potentially produce a larger quantity of peptide per bacterial cell. These studies suggest that multimerization of antimicrobial peptide genes represents a means to control in vivo toxicity of the recombinant peptides and increase production relative to single gene fusions.

Wan Wing Kuen. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 114-128). / Abstracts in English and Chinese. / Thesis Abstract --- p.i / Dedication --- p.iii / Acknowledgements --- p.iv / List of Figures --- p.xii / List of Tables --- p.xiv / Abbreviations --- p.xv / Chapter CHAPTER 1: --- INTRODUCTION --- p.1 / Chapter 1.1 --- Research Initiative and Significance --- p.1 / Chapter 1.2 --- Cardiomyocyte and Terminal differentiation --- p.4 / Chapter 1.3 --- Hypertension and Myocardial Hypertrophy --- p.7 / Chapter 1.3.1 --- Hypertension --- p.7 / Chapter 1.3.2 --- Myocardial Hypertrophy --- p.8 / Chapter 1.4 --- Experimental Animal Models --- p.13 / Chapter 1.4.1 --- Spontaneously Hypertensive Rats --- p.15 / Chapter 1.4.2 --- Wistar-Kyoto Rats --- p.16 / Chapter 1.5 --- Combination of Suppression Subtractive Hybridization and cDNA Microarray Analysis --- p.17 / Chapter 1.5.1 --- Suppression Subtractive Hybridization --- p.17 / Chapter 1.5.2 --- cDNA Microarray A nalysis --- p.21 / Chapter 1.5.3 --- Combination of SSH and cDNA microarray --- p.24 / Chapter CHAPTER 2: --- MATERIALS AND METHODS --- p.25 / Chapter 2.1 --- Experimental Animal Models --- p.25 / Chapter 2.2 --- RNA Isolation from Rat Ventricle --- p.26 / Chapter 2.2.1 --- Total RNA Isolation --- p.26 / Chapter 2.2.2 --- Deoxyribonuclease I Digestion --- p.27 / Chapter 2.3 --- Suppression Subtractive Hybridization --- p.29 / Chapter 2.3.1 --- First-Strand cDNA Synthesis --- p.29 / Chapter 2.3.2 --- Second-Strand cDNA Synthesis --- p.30 / Chapter 2.3.3 --- Column Chromatography --- p.31 / Chapter 2.3.4 --- Rsa I Endonuclease Digestion --- p.32 / Chapter 2.3.5 --- Adaptor Ligation --- p.33 / Chapter 2.3.6 --- Ligation Efficiency Analysis --- p.34 / Chapter 2.3.7 --- First Hybridization --- p.35 / Chapter 2.3.8 --- Second Hybridization --- p.36 / Chapter 2.3.9 --- Primary PCR Amplification --- p.36 / Chapter 2.3.10 --- Secondary PCR Amplification --- p.38 / Chapter 2.3.11 --- Subtraction Efficiency Analysis --- p.38 / Chapter 2.4 --- Construction of Subtracted cDNA Libraries --- p.40 / Chapter 2.5 --- cDNA Microarray Analysis --- p.42 / Chapter 2.5.1 --- PCR amplification of Subtracted Clones --- p.42 / Chapter 2.5.2 --- Purification of PCR Products of Subtracted Clones --- p.42 / Chapter 2 5.3 --- Rearrangement of Subtracted Clones into cDNA Microarray Format --- p.43 / Chapter 2.5.4 --- cDNA Microarray Fabrication --- p.43 / Chapter 2.5.5 --- Probe Preparation --- p.44 / Chapter 2.5.6 --- cDNA Microarray Hybridization --- p.46 / Chapter 2.5.7 --- Scanning cDNA Microarray Image and Data Analysis --- p.46 / Chapter 2.6 --- Sequencing of Differentially Expressed Genes --- p.48 / Chapter 2.6.1 --- Dye-terminator Cycle Sequencing --- p.48 / Chapter 2.6.2 --- Post-reaction Cleanup --- p.49 / Chapter 2.6.3 --- Signal Detection and Data Collection --- p.49 / Chapter 2.6.4 --- Sequencing Analysis --- p.50 / Chapter 2.7 --- Reverse Transcription Polymerase Chain Reaction --- p.51 / Chapter 2.8 --- Northern Blot Analysis --- p.54 / Chapter 2.8.1 --- RNA Transfer --- p.54 / Chapter 2.8.2 --- Probe Labeling --- p.55 / Chapter 2.8.3 --- Hybridization --- p.55 / Chapter 2.8.4 --- Chemiluminescent Detection --- p.56 / Chapter CHAPTER 3: --- RESULTS --- p.58 / Chapter 3.1 --- Suppression Subtractive Hybridization --- p.58 / Chapter 3.1.1 --- The Optimal Cycle for SMART cDNA Synthesis --- p.58 / Chapter 3.1.2 --- Adaptor Ligation Efficiency --- p.60 / Chapter 3.1.3 --- Primary and Secondary PCR Amplification of Subtracted cDNA --- p.63 / Chapter 3.1.4 --- Subtraction Efficiency --- p.66 / Chapter 3.2 --- Subtracted cDNA Libraries --- p.68 / Chapter 3.3 --- cDNA Microarray Analysis --- p.69 / Chapter 3.3.1 --- Isolation and Amplification of Subtracted cDNA Clones --- p.69 / Chapter 3.3.2 --- Microarray Scanning and A nalysis --- p.69 / Chapter 3.4 --- Sequencing Results of Subtracted cDNA Clones --- p.81 / Chapter 3.5 --- Reverse Transcription Polymerase Chain Reaction --- p.87 / Chapter 3.6 --- Northern Blot Hybridization --- p.90 / Chapter CHAPTER 4: --- DISCUSSION --- p.93 / Chapter 4.1 --- Subtraction Quality --- p.93 / Chapter 4.2 --- Differential Screening by cDNA Microarray --- p.95 / Chapter 4.2.1 --- Elimination of False Subtracted Clones --- p.95 / Chapter 4.2.2 --- Limitations of cDNA Microarray --- p.96 / Chapter 4.3 --- Differentially Expressed Genes in Hypertensive Heart --- p.98 / Chapter 4.3.1 --- Candidate Genes Showing Up-regulation --- p.99 / Chapter 4.3.1.1 --- Voltage-dependent Anion Channel 1 --- p.99 / Chapter 4.3.1.2 --- Protein Tyrosine Phosphatase 4a 1 --- p.101 / Chapter 4.3.1.3 --- Choline Transporter-like Protein 1 Splice Variant a --- p.102 / Chapter 4.3.2 --- Candidate Genes Showing Down-regulation --- p.103 / Chapter 4.3.2.1 --- Ryanodine Receptor 2 --- p.103 / Chapter 4.3.2.2 --- Guanine Nucleotide-binding Protein β1 Subunit --- p.104 / Chapter 4.3.2.3 --- Solute Carrier Family 3 Member 1 --- p.105 / Chapter 4.4 --- The Pros and Cons of Using Ventricular Tissue but not Cardiomyocytes --- p.107 / Chapter 4.5 --- Future Prospect --- p.109 / Chapter 4.5.1 --- Expression Profiling of Candidate Genes at Different Stages --- p.109 / Chapter 4.5.2 --- In vitro Studies of Candidate Genes --- p.110 / Chapter 4.5.2.1 --- Over-expression of up-regulated genes in Normal Cardiac Cells --- p.110 / Chapter 4.5.2.2 --- Suppression of down-regulated genes in Normal Cardiac Cells --- p.111 / Chapter 4.5.3 --- In vivo Studies of Up-regulated Genes --- p.111 / Chapter 4.5.4 --- Confirmation of Other Potential Candidate Genes --- p.112 / Chapter 4.6 --- Conclusion --- p.113 / REFERENCES --- p.114

Wong Wai Yin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references. / Abstracts in English and Chinese. / Acknowledgement --- p.i / Abstract --- p.iii / Abbreviation --- p.vii / Chapter CHAPTER 1 --- INTRODUCTION --- p.1-1 / Chapter 1.1 --- Classification of common epithelial ovarian tumors --- p.1-2 / Chapter 1.1.1 --- Serous tumors --- p.1-4 / Chapter 1.1.2 --- Mucinous tumors --- p.1-5 / Chapter 1.1.3 --- Endometrioid tumors --- p.1-6 / Chapter 1.1.4 --- Clear cell tumors --- p.1-6 / Chapter 1.1.5 --- Cancer staging --- p.1-7 / Chapter 1.1.6 --- Tumor grading --- p.1-8 / Chapter 1.2 --- Etiology --- p.1-10 / Chapter 1.2.1 --- Factors associated with increased risks --- p.1-10 / Chapter 1.2.2 --- Factors associated with decreased risks --- p.1-12 / Chapter 1.2.3 --- Other factors --- p.1-13 / Chapter 1.3 --- Understanding of progression of ovarian carcinoma --- p.1-13 / Chapter 1.4 --- Current screening test for ovarian cancer --- p.1-15 / Chapter 1.4.1 --- Transvaginal utrasound --- p.1-15 / Chapter 1.4.2 --- Serum tumor markers --- p.1-16 / Chapter 1.5 --- Molecular basis of ovarian cancer --- p.1-18 / Chapter 1.5.1 --- Loss of heterozygosity --- p.1-18 / Chapter 1.5.2 --- Microsatellite instability --- p.1-19 / Chapter 1.5.3 --- Oncogenes --- p.1-19 / Chapter 1.5.4 --- Tumor suppressor genes --- p.1-21 / Chapter 1.6 --- Microarray gene expression profiling analysis --- p.1-25 / Chapter 1.6.1 --- Princeple of DNA micorarray --- p.1-26 / Chapter 1.6.2 --- Types of microarray --- p.1-29 / Chapter 1.7 --- Gene expression profiling of ovarian cancer --- p.1-29 / Chapter 1.7.1 --- Up-regulated genes in ovarian cancer --- p.1-30 / Chapter 1.7.2 --- Down-regulated genes in ovarian cancer --- p.1-32 / Chapter 1.8 --- Project aims --- p.1-35 / Chapter CHPATER 2 --- MATERIALS AND METHODS --- p.2-1 / Chapter 2.1 --- Materials --- p.2-1 / Chapter 2.1.1 --- Patients --- p.2-1 / Chapter 2.1.2 --- Ovarian tissue specimen --- p.2-1 / Chapter 2.2 --- Methods --- p.2-2 / Chapter 2.2.1 --- Preparation of OCT-embedded Specimen Sections --- p.2-2 / Chapter 2.2.2 --- Microdissection of Tumor Cells from Specimen Sections --- p.2-3 / Chapter 2.2.3 --- Disruption of normal ovarian frozen tissue --- p.2-3 / Chapter 2.2.4 --- Total RNA Extraction --- p.2-3 / Chapter 2.2.4.1 --- RNA Isolation --- p.2-4 / Chapter 2.2.4.2 --- DNase I Digestion --- p.2-4 / Chapter 2.2.4.3 --- RNA Cleanup and Elution --- p.2-5 / Chapter 2.2.5 --- Oligonucleotide Microarray --- p.2-6 / Chapter 2.2.5.1 --- Two-Cycle cDNA Synthesis --- p.2-6 / Chapter 2.2.5.2 --- Synthesis of Biotin-Labeled cRNA --- p.2-9 / Chapter 2.2.5.3 --- Fragmenting the cRNA for Target Preparation --- p.2-9 / Chapter 2.2.5.4 --- Target Hybridization --- p.2-10 / Chapter 2.2.5.5 --- "Array Washing, Staining, and Scanning" --- p.2-11 / Chapter 2.2.5.6 --- Statistical Analysis of Microarray Data --- p.2-11 / Chapter 2.2.6 --- Quantitative Real-time Polymerase Chain Reaction --- p.2-13 / Chapter 2.2.6.1 --- Primer and Probe --- p.2-13 / Chapter 2.2.6.2 --- Reverse-transcription --- p.2-13 / Chapter 2.2.6.3 --- Plate Setup --- p.2-14 / Chapter 2.2.6.4 --- Fluocogenic PCR --- p.2-14 / Chapter 2.2.6.5 --- Statistical Analysis of Quantitative Real-time PCR Data --- p.2-15 / Chapter CHAPTER 3 --- RESULTS --- p.3-1 / Chapter 3.1 --- Microarray gene expression data analysis --- p.3-1 / Chapter 3.1.1 --- Unsupervised Gene Selection --- p.3-1 / Chapter 3.1.2 --- Supervised Gene Selection --- p.3-3 / Chapter 3.1.2.1 --- Gene expression profiles distinguish Serous Epithelial Ovarian Tumor from Normal Ovary and identifydifferentially expressed genes --- p.3-3 / Chapter 3.1.2.2 --- Gene expression profiles distinguish Advanced Stage Serous Epithelial Ovarian Tumor from Early Stage Serous Epithelial Ovarian Tumor and identify differentially expressed genes --- p.3-22 / Chapter 3.1.2.3 --- Gene expression profiles distinguish Metastatic Serous Epithelial Ovarian Tumor from Primary Serous Epithelial Ovarian Tumor and identify differentially expressed genes --- p.3-24 / Chapter 3.2 --- Validation of microarray data by quantitative Real-time PCR --- p.3-27 / Chapter 3.2.1 --- Fold change of candidate genes --- p.3-27 / Chapter 3.2.2 --- Correlation between microarray and quantitative real-time PCR results --- p.3-29 / Chapter 3.2.3 --- Comparison of the expression of candidates genes among the different histological types of epithelial ovarian tumors --- p.3-32 / Chapter CHAPTER 4 --- DISCUSSION --- p.4-1 / Chapter 4.1 --- Global gene expression profiling using oligonucleotide microarray --- p.4-1 / Chapter 4.1.1 --- "Sensitivity, specificity and reproducibility of the Affymetrix GeneChip® microarray" --- p.4-1 / Chapter 4.1.2 --- Microarray analysis software --- p.4-3 / Chapter 4.1.2.1 --- DNA-Chip Analyzer software --- p.4-3 / Chapter 4.1.2.2 --- Comparison of statistical methods for analysis of Affymetrix GeneChip® microarray data --- p.4-5 / Chapter 4.2 --- Validation of microarray data --- p.4-7 / Chapter 4.2.1 --- Advantages of using real-time PCR for mRNA quantification --- p.4-8 / Chapter 4.2.2 --- Comparison of mRNA gene expression by RT-PCR and DNA microarray --- p.4-9 / Chapter 4.3 --- Gene expression profiling in serous ovarian cancer compared with normal ovarian epithelium --- p.4-10 / Chapter 4.3.1 --- Potential biomarkers or therapeutic targets in ovarian cancer --- p.4-12 / Chapter 4.4 --- Gene expression profiling in advanced serous ovarian cancer compared with early ovarian cancer --- p.4-16 / Chapter 4.4.1 --- Potential prognostic markers or therapeutic targets in advanced ovarian cancer --- p.4-17 / Chapter 4.5 --- Gene expression profiling in metastatic cancer compared with primary ovarian cancer --- p.4-22 / Chapter 4.5.1 --- Potential predictive markers or therapeutic targets in metastatic cancer of ovary origin --- p.4-23 / Chapter CHAPTER 5 --- CONCLUSIONS --- p.5-1 / Chapter CHAPTER 6 --- FUTURE PROSPECT --- p.6-1 / REFERENCES --- p.R-1

by Hinny Shuk-Yee Lam. / Year shown on spine: 1997. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1996. / Includes bibliographical references (leaves 92-117). / Declaration --- p.i / Acknowledgements --- p.ii / Abstract --- p.iii / Table of Contents --- p.v / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Pancreas and Islets of Langerhans --- p.1 / Chapter 1.1.1 --- Islet Hormones and Glucose Balance --- p.3 / Chapter 1.1.2 --- Glucagon and Its Derived Peptides --- p.4 / Chapter A. --- Tissue-specific Post-translational Processing --- p.4 / Chapter B. --- Features of Proglucagon Gene --- p.6 / Chapter 1.1.3 --- Insulin and Features of Its Gene --- p.9 / Chapter 1.2 --- Regulation of Islet Hormone Secretion --- p.12 / Chapter 1.2.1 --- Endocrine Control --- p.12 / Chapter A --- GIP --- p.13 / Chapter B. --- Truncated GLP-1 --- p.13 / Chapter 1.2.2 --- Paracrine Control --- p.14 / Chapter 1.2.3 --- Neuroendocrine Control --- p.15 / Chapter 1.3 --- Neuropeptide Y --- p.16 / Chapter 1.3.1 --- NPY in Central Nervous System --- p.17 / Chapter 1.3.2 --- NPY in Pancreas --- p.17 / Chapter 1.3.3 --- NPY and Islet Hormones --- p.18 / Chapter 1.4 --- Synthesis and Secretion --- p.19 / Chapter 1.5 --- Objectives of Study --- p.23 / Chapter Chapter 2 --- Materials and Methods --- p.26 / Chapter 2.1 --- Effects of NPY on Islet Gene Expression --- p.26 / Chapter 2.1.1 --- Tissue Culture --- p.26 / Chapter A. --- Materials --- p.26 / Chapter B. --- Maintenance and Passage --- p.26 / Chapter C. --- Experimental Protocol --- p.28 / Chapter 2.1.2 --- Total RNA Isolation --- p.28 / Chapter A. --- Materials --- p.28 / Chapter B. --- Extraction Using FastPrep System --- p.29 / Chapter C. --- Quantification of RNA --- p.30 / Chapter D. --- Preparation of Reagents --- p.30 / Chapter 2.1.3 --- Northern Blot Analysis --- p.31 / Chapter A. --- Materials --- p.31 / Chapter B. --- Formaldehyde Gel Electrophoresis --- p.32 / Chapter C. --- Transfer onto Nylon Membrane --- p.33 / Chapter D. --- Labeling of cDNA Probes --- p.34 / Chapter E. --- Hybridization and Autoradiography --- p.35 / Chapter F. --- Preparation of Reagents --- p.36 / Chapter 2.1.4 --- Preparation of cDNA Probe --- p.37 / Chapter A. --- Materials --- p.37 / Chapter B. --- Preparation of Competent Cells --- p.37 / Chapter C. --- Transformation --- p.38 / Chapter D. --- Plasmid DNA Isolation --- p.39 / Chapter E. --- Restriction Enzyme Digestion --- p.41 / Chapter F. --- Agarose Gel Electrophoresis --- p.42 / Chapter G. --- Isolation of DNA Fragments --- p.42 / Chapter H. --- Preparation of Reagents --- p.43 / Chapter 2.1.5 --- Data Analysis --- p.46 / Chapter 2.2 --- Effects of NPY on Cytosolic Calcium --- p.46 / Chapter 2.2.1 --- Tissue Culture --- p.47 / Chapter 2.2.2 --- Confocal Laser Scanning Microscopy --- p.47 / Chapter A. --- Materials --- p.47 / Chapter B. --- Loading of Dye --- p.48 / Chapter C. --- Cytosolic Calcium Measurement --- p.49 / Chapter D. --- Preparation of Reagents --- p.49 / Chapter Chapter 3 --- Results --- p.51 / Chapter 3.1 --- Studies on Islet Gene Expression --- p.51 / Chapter 3.1.1 --- Effect of NPY on Proglucagon Expression --- p.51 / Chapter A. --- Effect at 11 mM Glucose --- p.51 / Chapter B. --- Effect at 5 mM Glucose --- p.52 / Chapter 3.1.2 --- Effect of NPY on Proinsulin Expression --- p.52 / Chapter 3.1.3 --- "Effect of PYY, PP and FSK on Proglucagon Expression" --- p.53 / Chapter 3.2 --- Studies on Cytosolic Calcium --- p.65 / Chapter 3.2.1 --- Features of InRlG9 Cells --- p.65 / Chapter 3.2.2 --- Effect of NPY on Cellular Calcium Level --- p.66 / Chapter Chapter 4 --- Discussion --- p.77 / Chapter Chapter 5 --- References --- p.92

The expression level of a single gene can vary substantially within and between species, which might facilitate the emergence and fixation of novel expression patterns in the course of evolution. With rapidly accumulating data from genome-wide expression profiling, dense genotyping and individual genome re-sequencing, it is now possible to pinpoint the genetic loci that potentially give rise to gene expression variation. However, what remains elusive is how expression changes could be attributed to the differences in genetic elements, and our understanding of the phenotypic manifestation resulting from gene expression variation is far from comprehensive. In this thesis, I aim to answer these questions in budding yeast and in human. I first studied duplicated genes in budding yeast, which usually shared the identical expression patterns immediately upon duplication events. I searched for the cis-elements, whose divergence might explain the substantial expression variation between the extant paralogs, and established the role of nucleosome occupancy in driving expression differentiation between yeast duplicates. I next investigated the role of trans-factors in establishing species- or population-specific gene expression, and my study was specifically focused on primate microRNAs as a special class of regulators in trans. I first delineated the evolutionary trajectory of an X-linked primate microRNA cluster, and then proposed its function in regulating primate epididymal physiology. I extended this study to human by identifying several microRNAs with highly differentiated regulation among human populations, and such regulatory differentiation was driven by positive selection during recent human evolution. This study for the first time demonstrated high plasticity of the microRNA regulatory interactions in modulating expression variation of their target messengers. Beyond exploring the elements that control gene expression variation, I examined phenotypic manifestation of the observed expression variation in human populations, and my analysis revealed significant implication of expression variation towards differential disease susceptibility among individuals. Lastly, I examined gene expression variation at a micro scale among isogenic cell populations in budding yeast, which is termed “expression noise”. Though expression noise originates from stochasticity, my analysis demonstrated strong topological constraints on expression noise in yeast cellular networks, with which I was able to predict gene expression noise with high accuracy. These observations suggest that the seemingly stochastic gene expression may have been evolutionarily constrained. Taken together, my study presented in this thesis investigates the origin, consequence and evolutionary significance of gene expression variation in eukaryotes.

The expression level of a single gene can vary substantially within and between species, which might facilitate the emergence and fixation of novel expression patterns in the course of evolution. With rapidly accumulating data from genome-wide expression profiling, dense genotyping and individual genome re-sequencing, it is now possible to pinpoint the genetic loci that potentially give rise to gene expression variation. However, what remains elusive is how expression changes could be attributed to the differences in genetic elements, and our understanding of the phenotypic manifestation resulting from gene expression variation is far from comprehensive. In this thesis, I aim to answer these questions in budding yeast and in human. I first studied duplicated genes in budding yeast, which usually shared the identical expression patterns immediately upon duplication events. I searched for the cis-elements, whose divergence might explain the substantial expression variation between the extant paralogs, and established the role of nucleosome occupancy in driving expression differentiation between yeast duplicates. I next investigated the role of trans-factors in establishing species- or population-specific gene expression, and my study was specifically focused on primate microRNAs as a special class of regulators in trans. I first delineated the evolutionary trajectory of an X-linked primate microRNA cluster, and then proposed its function in regulating primate epididymal physiology. I extended this study to human by identifying several microRNAs with highly differentiated regulation among human populations, and such regulatory differentiation was driven by positive selection during recent human evolution. This study for the first time demonstrated high plasticity of the microRNA regulatory interactions in modulating expression variation of their target messengers. Beyond exploring the elements that control gene expression variation, I examined phenotypic manifestation of the observed expression variation in human populations, and my analysis revealed significant implication of expression variation towards differential disease susceptibility among individuals. Lastly, I examined gene expression variation at a micro scale among isogenic cell populations in budding yeast, which is termed “expression noise”. Though expression noise originates from stochasticity, my analysis demonstrated strong topological constraints on expression noise in yeast cellular networks, with which I was able to predict gene expression noise with high accuracy. These observations suggest that the seemingly stochastic gene expression may have been evolutionarily constrained. Taken together, my study presented in this thesis investigates the origin, consequence and evolutionary significance of gene expression variation in eukaryotes.

Thesis (Ph. D.)--University of Missouri--Columbia, 2001. / Typescript. Includes bibliographical references (leaves 109-111). Also available on the Internet.

Thesis (M.S.)--University of Michigan, 2004. / Includes bibliographical references.

Thesis (Ph. D.)--University of Michigan, 2005. / Includes bibliographical references.

Thesis (Ph. D.)--University of Michigan, 2005. / Includes bibliographical references.