Matching Items (16)
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
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
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
This research investigates the biophysical and institutional mechanisms affecting the distribution of metals in the Sonoran Desert of Arizona. To date, a long-term, interdisciplinary perspective on metal pollution in the region has been lacking. To address this gap, I integrated approaches from environmental chemistry, historical geography, and institutional economics to study the history of metal pollution in the desert. First, by analyzing the chemistry embodied in the sequentially-grown spines of long-lived cacti, I created a record of metal pollution that details biogeochemical trends in the desert since the 1980s. These data suggest that metal pollution is not simply a legacy of early industrialization. Instead, I found evidence of recent metal pollution in both the heart of the city and a remote, rural location. To understand how changing land uses may have contributed to this, I next explored the historical geography of industrialization in the desert. After identifying cities and mining districts as hot spots for airborne metals, I used a mixture of historical reports, maps, and memoirs to reconstruct the industrial history of these polluted landscapes. In the process, I identified three key transitions in the energy-metal nexus that drove the redistribution of metals from mineral deposits to urban communities. These transitions coincided with the Columbian exchange, the arrival of the railroads, and the economic restructuring that accompanied World War II. Finally, to determine how legal and political forces may be influencing the fate of metals, I studied the evolution of the rights and duties affecting metals in their various forms. This allowed me to track changes in the institutions regulating metals from the mining laws of the 19th century through their treatment as occupational and public health hazards in the 20th century. In the process, I show how Arizona’s environmental and resource institutions were often transformed by extra-territorial concerns. Ultimately, this created an institutional system that compartmentalizes metals and fails to appreciate their capacity to mobilize across legal and biophysical boundaries to accumulate in the environment. Long-term, interdisciplinary perspectives such as this are critical for untangling the complex web of elements and social relations transforming the modern world.
ContributorsHester, Cyrus M (Author) / Larson, Kelli L (Thesis advisor) / Laubichler, Manfred D (Thesis advisor) / MacFadyen, Joshua (Committee member) / Arizona State University (Publisher)
Created2019
Description
This dissertation focuses on creating a pluralistic approach to understanding and measuring interdisciplinarity at various scales to further the study of the evolution of knowledge and innovation. Interdisciplinarity is considered an important research component and is closely linked to higher rates of innovation. If the goal is to create more innovative research, we must understand how interdisciplinarity operates.
I begin by examining interdisciplinarity with a small scope, the research university. This study uses metadata to create co-authorship networks and examine how a change in university policies to increase interdisciplinarity can be successful. The New American University Initiative (NAUI) at Arizona State University (ASU) set forth the goal of making ASU a world hub for interdisciplinary research. This kind of interdisciplinarity is produced from a deliberate, engineered, reorganization of the individuals within the university and the knowledge they contain. By using a set of social network analysis measurements, I created an algorithm to measure the changes to the co-authorship networks that resulted from increased university support for interdisciplinary research.
The second case study increases the scope of interdisciplinarity from individual universities to a single scientific discourse, the Anthropocene. The idea of the Anthropocene began as an idea about the need for a new geological epoch and underwent unsupervised interdisciplinary expansion due to climate change integrating itself into the core of the discourse. In contrast to the NAUI which was specifically engineered to increase interdisciplinarity, the I use keyword co-occurrence networks to measure how the Anthropocene discourse increases its interdisciplinarity through unsupervised expansion after climate change becomes a core keyword within the network and behaves as an anchor point for new disciplines to connect and join the discourse.
The scope of interdisciplinarity increases again with the final case study about the field of evolutionary medicine. Evolutionary medicine is a case of engineered interdisciplinary integration between evolutionary biology and medicine. The primary goal of evolutionary medicine is to better understand "why we get sick" through the lens of evolutionary biology. This makes it an excellent candidate to understand large-scale interdisciplinarity. I show through multiple type of networks and metadata analyses that evolutionary medicine successfully integrates the concepts of evolutionary biology into medicine.
By increasing our knowledge of interdisciplinarity at various scales and how it behaves in different initial conditions, we are better able to understand the elusive nature of innovation. Interdisciplinary can mean different things depending on how its defined. I show that a pluralistic approach to defining and measuring interdisciplinarity is not only appropriate but necessary if our goal is to increase interdisciplinarity, the frequency of innovations, and our understanding of the evolution of knowledge.
I begin by examining interdisciplinarity with a small scope, the research university. This study uses metadata to create co-authorship networks and examine how a change in university policies to increase interdisciplinarity can be successful. The New American University Initiative (NAUI) at Arizona State University (ASU) set forth the goal of making ASU a world hub for interdisciplinary research. This kind of interdisciplinarity is produced from a deliberate, engineered, reorganization of the individuals within the university and the knowledge they contain. By using a set of social network analysis measurements, I created an algorithm to measure the changes to the co-authorship networks that resulted from increased university support for interdisciplinary research.
The second case study increases the scope of interdisciplinarity from individual universities to a single scientific discourse, the Anthropocene. The idea of the Anthropocene began as an idea about the need for a new geological epoch and underwent unsupervised interdisciplinary expansion due to climate change integrating itself into the core of the discourse. In contrast to the NAUI which was specifically engineered to increase interdisciplinarity, the I use keyword co-occurrence networks to measure how the Anthropocene discourse increases its interdisciplinarity through unsupervised expansion after climate change becomes a core keyword within the network and behaves as an anchor point for new disciplines to connect and join the discourse.
The scope of interdisciplinarity increases again with the final case study about the field of evolutionary medicine. Evolutionary medicine is a case of engineered interdisciplinary integration between evolutionary biology and medicine. The primary goal of evolutionary medicine is to better understand "why we get sick" through the lens of evolutionary biology. This makes it an excellent candidate to understand large-scale interdisciplinarity. I show through multiple type of networks and metadata analyses that evolutionary medicine successfully integrates the concepts of evolutionary biology into medicine.
By increasing our knowledge of interdisciplinarity at various scales and how it behaves in different initial conditions, we are better able to understand the elusive nature of innovation. Interdisciplinary can mean different things depending on how its defined. I show that a pluralistic approach to defining and measuring interdisciplinarity is not only appropriate but necessary if our goal is to increase interdisciplinarity, the frequency of innovations, and our understanding of the evolution of knowledge.
ContributorsPainter, Deryc T (Author) / Laubichler, Manfred D (Thesis advisor) / Maienschein, Jane (Committee member) / Bliss, Nadya T (Committee member) / Simeone, Michael P (Committee member) / Nesse, Randolph M. (Committee member) / Arizona State University (Publisher)
Created2019
Description
Understanding changes and trends in biomedical knowledge is crucial for individuals, groups, and institutions as biomedicine improves people’s lives, supports national economies, and facilitates innovation. However, as knowledge changes what evidence illustrates knowledge changes? In the case of microbiome, a multi-dimensional concept from biomedicine, there are significant increases in publications, citations, funding, collaborations, and other explanatory variables or contextual factors. What is observed in the microbiome, or any historical evolution of a scientific field or scientific knowledge, is that these changes are related to changes in knowledge, but what is not understood is how to measure and track changes in knowledge. This investigation highlights how contextual factors from the language and social context of the microbiome are related to changes in the usage, meaning, and scientific knowledge on the microbiome. Two interconnected studies integrating qualitative and quantitative evidence examine the variation and change of the microbiome evidence are presented. First, the concepts microbiome, metagenome, and metabolome are compared to determine the boundaries of the microbiome concept in relation to other concepts where the conceptual boundaries have been cited as overlapping. A collection of publications for each concept or corpus is presented, with a focus on how to create, collect, curate, and analyze large data collections. This study concludes with suggestions on how to analyze biomedical concepts using a hybrid approach that combines results from the larger language context and individual words. Second, the results of a systematic review that describes the variation and change of microbiome research, funding, and knowledge are examined. A corpus of approximately 28,000 articles on the microbiome are characterized, and a spectrum of microbiome interpretations are suggested based on differences related to context. The collective results suggest the microbiome is a separate concept from the metagenome and metabolome, and the variation and change to the microbiome concept was influenced by contextual factors. These results provide insight into how concepts with extensive resources behave within biomedicine and suggest the microbiome is possibly representative of conceptual change or a preview of new dynamics within science that are expected in the future.
ContributorsAiello, Kenneth (Author) / Laubichler, Manfred D (Thesis advisor) / Simeone, Michael (Committee member) / Buetow, Kenneth (Committee member) / Walker, Sara I (Committee member) / Arizona State University (Publisher)
Created2018
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