Matching Items (23)
Description
Specific dendritic morphologies are a hallmark of neuronal identity, circuit assembly, and behaviorally relevant function. Despite the importance of dendrites in brain health and disease, the functional consequences of dendritic shape remain largely unknown. This dissertation addresses two fundamental and interrelated aspects of dendrite neurobiology. First, by utilizing the genetic power of Drosophila melanogaster, these studies assess the developmental mechanisms underlying single neuron morphology, and subsequently investigate the functional and behavioral consequences resulting from developmental irregularity. Significant insights into the molecular mechanisms that contribute to dendrite development come from studies of Down syndrome cell adhesion molecule (Dscam). While these findings have been garnered primarily from sensory neurons whose arbors innervate a two-dimensional plane, it is likely that the principles apply in three-dimensional central neurons that provide the structural substrate for synaptic input and neural circuit formation. As such, this dissertation supports the hypothesis that neuron type impacts the realization of Dscam function. In fact, in Drosophila motoneurons, Dscam serves a previously unknown cell-autonomous function in dendrite growth. Dscam manipulations produced a range of dendritic phenotypes with alteration in branch number and length. Subsequent experiments exploited the dendritic alterations produced by Dscam manipulations in order to correlate dendritic structure with the suggested function of these neurons. These data indicate that basic motoneuron function and behavior are maintained even in the absence of all adult dendrites within the same neuron. By contrast, dendrites are required for adjusting motoneuron responses to specific challenging behavioral requirements. Here, I establish a direct link between dendritic structure and neuronal function at the level of the single cell, thus defining the structural substrates necessary for conferring various aspects of functional motor output. Taken together, information gathered from these studies can inform the quest in deciphering how complex cell morphologies and networks form and are precisely linked to their function.
ContributorsHutchinson, Katie Marie (Author) / Duch, Carsten (Thesis advisor) / Neisewander, Janet (Thesis advisor) / Newfeld, Stuart (Committee member) / Smith, Brian (Committee member) / Orchinik, Miles (Committee member) / Arizona State University (Publisher)
Created2013
Description
In somatic cells, the mitotic spindle apparatus is centrosomal and several isoforms of Protein Kinase C (PKC) have been associated with the mitotic spindle, but their role in stabilizing the mitotic spindle is unclear. Other protein kinases such as, Glycogen Synthase Kinase 3â (GSK3â) also have been shown to be associated with the mitotic spindle. In the study in chapter 2, we show the enrichment of active (phosphorylated) PKCæ at the centrosomal region of the spindle apparatus in metaphase stage of 3T3 cells. In order to understand whether the two kinases, PKC and GSK3â are associated with the mitotic spindle, first, the co-localization and close molecular proximity of PKC isoforms with GSK3â was studied in metaphase cells. Second, the involvement of inactive GSK3â in maintaining an intact mitotic spindle was shown. Third, this study showed that addition of a phospho-PKCæ specific inhibitor to cells can disrupt the mitotic spindle microtubules. The mitotic spindle at metaphase in mouse fibroblasts appears to be maintained by PKCæ acting through GSK3â. The MAPK pathway has been implicated in various functions related to cell cycle regulation. MAPKK (MEK) is part of this pathway and the extracellular regulated kinase (ERK) is its known downstream target. GSK3â and PKCæ also have been implicated in cell cycle regulation. In the study in chapter 3, we tested the effects of inhibiting MEK on the activities of ERK, GSK3â, PKCæ, and á-tubulin. Results from this study indicate that inhibition of MEK did not inhibit GSK3â and PKCæ enrichment at the centrosomes. However, the mitotic spindle showed a reduction in the pixel intensity of microtubules and also a reduction in the number of cells in each of the M-phase stages. A peptide activation inhibitor of ERK was also used. Our results indicated a decrease in mitotic spindle microtubules and an absence of cells in most of the M-phase stages. GSK3â and PKCæ enrichment were however not inhibited at the centrosomes. Taken together, the kinases GSK3â and PKCæ may not function as a part of the MAPK pathway to regulate the mitotic spindle.
ContributorsChakravadhanula, Madhavi (Author) / Capco, David G. (Thesis advisor) / Chandler, Douglas (Committee member) / Clark-Curtiss, Josephine (Committee member) / Newfeld, Stuart (Committee member) / Arizona State University (Publisher)
Created2012
Description
Despite the 40-year war on cancer, very limited progress has been made in developing a cure for the disease. This failure has prompted the reevaluation of the causes and development of cancer. One resulting model, coined the atavistic model of cancer, posits that cancer is a default phenotype of the cells of multicellular organisms which arises when the cell is subjected to an unusual amount of stress. Since this default phenotype is similar across cell types and even organisms, it seems it must be an evolutionarily ancestral phenotype. We take a phylostratigraphical approach, but systematically add species divergence time data to estimate gene ages numerically and use these ages to investigate the ages of genes involved in cancer. We find that ancient disease-recessive cancer genes are significantly enriched for DNA repair and SOS activity, which seems to imply that a core component of cancer development is not the regulation of growth, but the regulation of mutation. Verification of this finding could drastically improve cancer treatment and prevention.
ContributorsOrr, Adam James (Author) / Davies, Paul (Thesis director) / Bussey, Kimberly (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Department of Chemistry and Biochemistry (Contributor) / School of Life Sciences (Contributor)
Created2015-05
Description
Background
Drosophila melanogaster has been established as a model organism for investigating the developmental gene interactions. The spatio-temporal gene expression patterns of Drosophila melanogaster can be visualized by in situ hybridization and documented as digital images. Automated and efficient tools for analyzing these expression images will provide biological insights into the gene functions, interactions, and networks. To facilitate pattern recognition and comparison, many web-based resources have been created to conduct comparative analysis based on the body part keywords and the associated images. With the fast accumulation of images from high-throughput techniques, manual inspection of images will impose a serious impediment on the pace of biological discovery. It is thus imperative to design an automated system for efficient image annotation and comparison.
Results
We present a computational framework to perform anatomical keywords annotation for Drosophila gene expression images. The spatial sparse coding approach is used to represent local patches of images in comparison with the well-known bag-of-words (BoW) method. Three pooling functions including max pooling, average pooling and Sqrt (square root of mean squared statistics) pooling are employed to transform the sparse codes to image features. Based on the constructed features, we develop both an image-level scheme and a group-level scheme to tackle the key challenges in annotating Drosophila gene expression pattern images automatically. To deal with the imbalanced data distribution inherent in image annotation tasks, the undersampling method is applied together with majority vote. Results on Drosophila embryonic expression pattern images verify the efficacy of our approach.
Conclusion
In our experiment, the three pooling functions perform comparably well in feature dimension reduction. The undersampling with majority vote is shown to be effective in tackling the problem of imbalanced data. Moreover, combining sparse coding and image-level scheme leads to consistent performance improvement in keywords annotation.
Drosophila melanogaster has been established as a model organism for investigating the developmental gene interactions. The spatio-temporal gene expression patterns of Drosophila melanogaster can be visualized by in situ hybridization and documented as digital images. Automated and efficient tools for analyzing these expression images will provide biological insights into the gene functions, interactions, and networks. To facilitate pattern recognition and comparison, many web-based resources have been created to conduct comparative analysis based on the body part keywords and the associated images. With the fast accumulation of images from high-throughput techniques, manual inspection of images will impose a serious impediment on the pace of biological discovery. It is thus imperative to design an automated system for efficient image annotation and comparison.
Results
We present a computational framework to perform anatomical keywords annotation for Drosophila gene expression images. The spatial sparse coding approach is used to represent local patches of images in comparison with the well-known bag-of-words (BoW) method. Three pooling functions including max pooling, average pooling and Sqrt (square root of mean squared statistics) pooling are employed to transform the sparse codes to image features. Based on the constructed features, we develop both an image-level scheme and a group-level scheme to tackle the key challenges in annotating Drosophila gene expression pattern images automatically. To deal with the imbalanced data distribution inherent in image annotation tasks, the undersampling method is applied together with majority vote. Results on Drosophila embryonic expression pattern images verify the efficacy of our approach.
Conclusion
In our experiment, the three pooling functions perform comparably well in feature dimension reduction. The undersampling with majority vote is shown to be effective in tackling the problem of imbalanced data. Moreover, combining sparse coding and image-level scheme leads to consistent performance improvement in keywords annotation.
ContributorsSun, Qian (Author) / Muckatira, Sherin (Author) / Yuan, Lei (Author) / Ji, Shuiwang (Author) / Newfeld, Stuart (Author) / Kumar, Sudhir (Author) / Ye, Jieping (Author) / Biodesign Institute (Contributor) / Center for Evolution and Medicine (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor) / Ira A. Fulton Schools of Engineering (Contributor)
Created2013-12-03
Description
Background
Grading schemes for breast cancer diagnosis are predominantly based on pathologists' qualitative assessment of altered nuclear structure from 2D brightfield microscopy images. However, cells are three-dimensional (3D) objects with features that are inherently 3D and thus poorly characterized in 2D. Our goal is to quantitatively characterize nuclear structure in 3D, assess its variation with malignancy, and investigate whether such variation correlates with standard nuclear grading criteria.
Methodology
We applied micro-optical computed tomographic imaging and automated 3D nuclear morphometry to quantify and compare morphological variations between human cell lines derived from normal, benign fibrocystic or malignant breast epithelium. To reproduce the appearance and contrast in clinical cytopathology images, we stained cells with hematoxylin and eosin and obtained 3D images of 150 individual stained cells of each cell type at sub-micron, isotropic resolution. Applying volumetric image analyses, we computed 42 3D morphological and textural descriptors of cellular and nuclear structure.
Principal Findings
We observed four distinct nuclear shape categories, the predominant being a mushroom cap shape. Cell and nuclear volumes increased from normal to fibrocystic to metastatic type, but there was little difference in the volume ratio of nucleus to cytoplasm (N/C ratio) between the lines. Abnormal cell nuclei had more nucleoli, markedly higher density and clumpier chromatin organization compared to normal. Nuclei of non-tumorigenic, fibrocystic cells exhibited larger textural variations than metastatic cell nuclei. At p<0.0025 by ANOVA and Kruskal-Wallis tests, 90% of our computed descriptors statistically differentiated control from abnormal cell populations, but only 69% of these features statistically differentiated the fibrocystic from the metastatic cell populations.
Conclusions
Our results provide a new perspective on nuclear structure variations associated with malignancy and point to the value of automated quantitative 3D nuclear morphometry as an objective tool to enable development of sensitive and specific nuclear grade classification in breast cancer diagnosis.
Grading schemes for breast cancer diagnosis are predominantly based on pathologists' qualitative assessment of altered nuclear structure from 2D brightfield microscopy images. However, cells are three-dimensional (3D) objects with features that are inherently 3D and thus poorly characterized in 2D. Our goal is to quantitatively characterize nuclear structure in 3D, assess its variation with malignancy, and investigate whether such variation correlates with standard nuclear grading criteria.
Methodology
We applied micro-optical computed tomographic imaging and automated 3D nuclear morphometry to quantify and compare morphological variations between human cell lines derived from normal, benign fibrocystic or malignant breast epithelium. To reproduce the appearance and contrast in clinical cytopathology images, we stained cells with hematoxylin and eosin and obtained 3D images of 150 individual stained cells of each cell type at sub-micron, isotropic resolution. Applying volumetric image analyses, we computed 42 3D morphological and textural descriptors of cellular and nuclear structure.
Principal Findings
We observed four distinct nuclear shape categories, the predominant being a mushroom cap shape. Cell and nuclear volumes increased from normal to fibrocystic to metastatic type, but there was little difference in the volume ratio of nucleus to cytoplasm (N/C ratio) between the lines. Abnormal cell nuclei had more nucleoli, markedly higher density and clumpier chromatin organization compared to normal. Nuclei of non-tumorigenic, fibrocystic cells exhibited larger textural variations than metastatic cell nuclei. At p<0.0025 by ANOVA and Kruskal-Wallis tests, 90% of our computed descriptors statistically differentiated control from abnormal cell populations, but only 69% of these features statistically differentiated the fibrocystic from the metastatic cell populations.
Conclusions
Our results provide a new perspective on nuclear structure variations associated with malignancy and point to the value of automated quantitative 3D nuclear morphometry as an objective tool to enable development of sensitive and specific nuclear grade classification in breast cancer diagnosis.
ContributorsNandakumar, Vivek (Author) / Kelbauskas, Laimonas (Author) / Hernandez, Kathryn (Author) / Lintecum, Kelly (Author) / Senechal, Patti (Author) / Bussey, Kimberly (Author) / Davies, Paul (Author) / Johnson, Roger (Author) / Meldrum, Deirdre (Author) / Ira A. Fulton Schools of Engineering (Contributor) / School of Electrical, Computer and Energy Engineering (Contributor) / Biodesign Institute (Contributor) / Center for Biosignatures Discovery Automation (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor) / Department of Physics (Contributor)
Created2012-01-05
Description
Background
Multicellular organisms consist of cells of many different types that are established during development. Each type of cell is characterized by the unique combination of expressed gene products as a result of spatiotemporal gene regulation. Currently, a fundamental challenge in regulatory biology is to elucidate the gene expression controls that generate the complex body plans during development. Recent advances in high-throughput biotechnologies have generated spatiotemporal expression patterns for thousands of genes in the model organism fruit fly Drosophila melanogaster. Existing qualitative methods enhanced by a quantitative analysis based on computational tools we present in this paper would provide promising ways for addressing key scientific questions.
Results
We develop a set of computational methods and open source tools for identifying co-expressed embryonic domains and the associated genes simultaneously. To map the expression patterns of many genes into the same coordinate space and account for the embryonic shape variations, we develop a mesh generation method to deform a meshed generic ellipse to each individual embryo. We then develop a co-clustering formulation to cluster the genes and the mesh elements, thereby identifying co-expressed embryonic domains and the associated genes simultaneously. Experimental results indicate that the gene and mesh co-clusters can be correlated to key developmental events during the stages of embryogenesis we study. The open source software tool has been made available at http://compbio.cs.odu.edu/fly/.
Conclusions
Our mesh generation and machine learning methods and tools improve upon the flexibility, ease-of-use and accuracy of existing methods.
Multicellular organisms consist of cells of many different types that are established during development. Each type of cell is characterized by the unique combination of expressed gene products as a result of spatiotemporal gene regulation. Currently, a fundamental challenge in regulatory biology is to elucidate the gene expression controls that generate the complex body plans during development. Recent advances in high-throughput biotechnologies have generated spatiotemporal expression patterns for thousands of genes in the model organism fruit fly Drosophila melanogaster. Existing qualitative methods enhanced by a quantitative analysis based on computational tools we present in this paper would provide promising ways for addressing key scientific questions.
Results
We develop a set of computational methods and open source tools for identifying co-expressed embryonic domains and the associated genes simultaneously. To map the expression patterns of many genes into the same coordinate space and account for the embryonic shape variations, we develop a mesh generation method to deform a meshed generic ellipse to each individual embryo. We then develop a co-clustering formulation to cluster the genes and the mesh elements, thereby identifying co-expressed embryonic domains and the associated genes simultaneously. Experimental results indicate that the gene and mesh co-clusters can be correlated to key developmental events during the stages of embryogenesis we study. The open source software tool has been made available at http://compbio.cs.odu.edu/fly/.
Conclusions
Our mesh generation and machine learning methods and tools improve upon the flexibility, ease-of-use and accuracy of existing methods.
ContributorsZhang, Wenlu (Author) / Feng, Daming (Author) / Li, Rongjian (Author) / Chernikov, Andrey (Author) / Chrisochoides, Nikos (Author) / Osgood, Christopher (Author) / Konikoff, Charlotte (Author) / Newfeld, Stuart (Author) / Kumar, Sudhir (Author) / Ji, Shuiwang (Author) / Biodesign Institute (Contributor) / Center for Evolution and Medicine (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor)
Created2013-12-28
Description
In the U.S., breast cancer (BC) incidences among African American (AA) and CA (CA) women are similar, yet AA women have a significantly higher mortality rate. In addition, AA women often present with tumors at a younger age, with a higher tumor grade/stage and are more likely to be diagnosed with the highly aggressive triple-negative breast cancer (TNBC) subtype. Even within the TNBC subtype, AA women have a worse clinical outcome compared to CA. Although multiple socio-economic and lifestyle factors may contribute to these observed health disparities, it is essential that the underlying biological differences between CA and AA TNBC are identified. In this study, gene expression profiling was performed on archived FFPE samples, obtained from CA and AA women diagnosed with early stage TNBC. Initial analysis revealed a pattern of differential expression in the AA cohort compared to CA. Further molecular characterization results showed that the AA cohort segregated into 3-TNBC molecular subtypes; Basal-like (BL2), Immunomodulatory (IM) and Mesenchymal (M). Gene expression analyses resulted in 190 differentially expressed genes between the AA and CA cohorts. Pathway enrichment analysis demonstrated that differentially expressed genes were over-represented in cytoskeletal remodeling, cell adhesion, tight junctions, and immune response in the AA TNBC -cohort. Furthermore, genes in the Wnt/β-catenin pathway were over-expressed. These results were validated using RT-qPCR on an independent cohort of FFPE samples from AA and CA women with early stage TNBC, and identified Caveolin-1 (CAV1) as being significantly expressed in the AA-TNBC cohort. Furthermore, CAV1 was shown to be highly expressed in a cell line panel of TNBC, in particular, those of the mesenchymal and basal-like molecular subtype. Finally, silencing of CAV1 expression by siRNA resulted in a significant decrease in proliferation in each of the TNBC cell lines. These observations suggest that CAV1 expression may contribute to the more aggressive phenotype observed in AA women diagnosed with TNBC.
ContributorsGetz, Julie (Author) / Baumbach-Reardon, Lisa L (Thesis advisor) / Lake, Douglas F (Thesis advisor) / Bussey, Kimberly (Committee member) / Kusumi, Kenro (Committee member) / Arizona State University (Publisher)
Created2015
Description
Systems biology studies complex biological systems. It is an interdisciplinary field, with biologists working with non-biologists such as computer scientists, engineers, chemists, and mathematicians to address research problems applying systems’ perspectives. How these different researchers and their disciplines differently contributed to the advancement of this field over time is a question worth examining. Did systems biology become a systems-oriented science or a biology-oriented science from 1992 to 2013?
This project utilized computational tools to analyze large data sets and interpreted the results from historical and philosophical perspectives. Tools deployed were derived from scientometrics, corpus linguistics, text-based analysis, network analysis, and GIS analysis to analyze more than 9000 articles (metadata and text) on systems biology. The application of these tools to a HPS project represents a novel approach.
The dissertation shows that systems biology has transitioned from a more mathematical, computational, and engineering-oriented discipline focusing on modeling to a more biology-oriented discipline that uses modeling as a means to address real biological problems. Also, the results show that bioengineering and medical research has increased within systems biology. This is reflected in the increase of the centrality of biology-related concepts such as cancer, over time. The dissertation also compares the development of systems biology in China with some other parts of the world, and reveals regional differences, such as a unique trajectory of systems biology in China related to a focus on traditional Chinese medicine.
This dissertation adds to the historiography of modern biology where few studies have focused on systems biology compared with the history of molecular biology and evolutionary biology.
This project utilized computational tools to analyze large data sets and interpreted the results from historical and philosophical perspectives. Tools deployed were derived from scientometrics, corpus linguistics, text-based analysis, network analysis, and GIS analysis to analyze more than 9000 articles (metadata and text) on systems biology. The application of these tools to a HPS project represents a novel approach.
The dissertation shows that systems biology has transitioned from a more mathematical, computational, and engineering-oriented discipline focusing on modeling to a more biology-oriented discipline that uses modeling as a means to address real biological problems. Also, the results show that bioengineering and medical research has increased within systems biology. This is reflected in the increase of the centrality of biology-related concepts such as cancer, over time. The dissertation also compares the development of systems biology in China with some other parts of the world, and reveals regional differences, such as a unique trajectory of systems biology in China related to a focus on traditional Chinese medicine.
This dissertation adds to the historiography of modern biology where few studies have focused on systems biology compared with the history of molecular biology and evolutionary biology.
ContributorsZou, Yawen (Author) / Laubichler, Manfred (Thesis advisor) / Maienschein, Jane (Thesis advisor) / Creath, Richard (Committee member) / Ellison, Karin (Committee member) / Newfeld, Stuart (Committee member) / Arizona State University (Publisher)
Created2016
Description
Methyl-CpG binding protein 2 (MECP2) is a widely abundant, multifunctional regulator of gene expression with highest levels of expression in mature neurons. In humans, both loss- and gain-of-function mutations of MECP2 cause mental retardation and motor dysfunction classified as either Rett Syndrome (RTT, loss-of-function) or MECP2 Duplication Syndrome (MDS, gain-of-function). At the cellular level, MECP2 mutations cause both synaptic and dendritic defects. Despite identification of MECP2 as a cause for RTT nearly 16 years ago, little progress has been made in identifying effective treatments. Investigating major cellular and molecular targets of MECP2 in model systems can help elucidate how mutation of this single gene leads to nervous system and behavioral defects, which can ultimately lead to novel therapeutic strategies for RTT and MDS. In the work presented here, I use the fruit fly, Drosophila melanogaster, as a model system to study specific cellular and molecular functions of MECP2 in neurons. First, I show that targeted expression of human MECP2 in Drosophila flight motoneurons causes impaired dendritic growth and flight behavioral performance. These effects are not caused by a general toxic effect of MECP2 overexpression in Drosophila neurons, but are critically dependent on the methyl-binding domain of MECP2. This study shows for the first time cellular consequences of MECP2 gain-of-function in Drosophila neurons. Second, I use RNA-Seq to identify KIBRA, a gene associated with learning and memory in humans, as a novel target of MECP2 involved in the dendritic growth phenotype. I confirm bidirectional regulation of Kibra by Mecp2 in mouse, highlighting the translational utility of the Drosophila model. Finally, I use this system to identify a novel role for the C-terminus in regulating the function of MECP in apoptosis and verify this finding in mammalian cell culture. In summary, this work has established Drosophila as a translational model to study the cellular effects of MECP2 gain-of-function in neurons, and provides insight into the function of MECP2 in dendritic growth and apoptosis.
ContributorsWilliams, Alison (Author) / Duch, Carsten (Thesis advisor) / Orchinik, Miles (Committee member) / Gallitano, Amelia (Committee member) / Huentelman, Matthew (Committee member) / Narayanan, Vinodh (Committee member) / Newfeld, Stuart (Committee member) / Arizona State University (Publisher)
Created2015
Description
A central task for historians and philosophers of science is to characterize and analyze the epistemic practices in a given science. The epistemic practice of a science includes its explanatory goals as well as the methods used to achieve these goals. This dissertation addresses the epistemic practices in gene expression research spanning the mid-twentieth century to the twenty-first century. The critical evaluation of the standard historical narratives of the molecular life sciences clarifies certain philosophical problems with respect to reduction, emergence, and representation, and offers new ways with which to think about the development of scientific research and the nature of scientific change.
The first chapter revisits some of the key experiments that contributed to the development of the repression model of genetic regulation in the lac operon and concludes that the early research on gene expression and genetic regulation depict an iterative and integrative process, which was neither reductionist nor holist. In doing so, it challenges a common application of a conceptual framework in the history of biology and offers an alternative framework. The second chapter argues that the concept of emergence in the history and philosophy of biology is too ambiguous to account for the current research in post-genomic molecular biology and it is often erroneously used to argue against some reductionist theses. The third chapter investigates the use of network representations of gene expression in developmental evolution research and takes up some of the conceptual and methodological problems it has generated. The concluding comments present potential avenues for future research arising from each substantial chapter.
In sum, this dissertation argues that the epistemic practices of gene expression research are an iterative and integrative process, which produces theoretical representations of the complex interactions in gene expression as networks. Moreover, conceptualizing these interactions as networks constrains empirical research strategies by the limited number of ways in which gene expression can be controlled through general rules of network interactions. Making these strategies explicit helps to clarify how they can explain the dynamic and adaptive features of genomes.
The first chapter revisits some of the key experiments that contributed to the development of the repression model of genetic regulation in the lac operon and concludes that the early research on gene expression and genetic regulation depict an iterative and integrative process, which was neither reductionist nor holist. In doing so, it challenges a common application of a conceptual framework in the history of biology and offers an alternative framework. The second chapter argues that the concept of emergence in the history and philosophy of biology is too ambiguous to account for the current research in post-genomic molecular biology and it is often erroneously used to argue against some reductionist theses. The third chapter investigates the use of network representations of gene expression in developmental evolution research and takes up some of the conceptual and methodological problems it has generated. The concluding comments present potential avenues for future research arising from each substantial chapter.
In sum, this dissertation argues that the epistemic practices of gene expression research are an iterative and integrative process, which produces theoretical representations of the complex interactions in gene expression as networks. Moreover, conceptualizing these interactions as networks constrains empirical research strategies by the limited number of ways in which gene expression can be controlled through general rules of network interactions. Making these strategies explicit helps to clarify how they can explain the dynamic and adaptive features of genomes.
ContributorsRacine, Valerie (Author) / Maienschein, Jane (Thesis advisor) / Laubichler, Manfred D (Thesis advisor) / Creath, Richard (Committee member) / Newfeld, Stuart (Committee member) / Morange, Michel (Committee member) / Arizona State University (Publisher)
Created2016