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Pathway analysis helps researchers gain insight into the biology behind gene expression-based data. By applying this data to known biological pathways, we can learn about mutations or other changes in cellular function, such as those seen in cancer. There are many tools that can be used to analyze pathways; however, it can be difficult to find and learn about the which tool is optimal for use in a certain experiment. This thesis aims to comprehensively review four tools, Cytoscape, PaxtoolsR, PathOlogist, and Reactome, and their role in pathway analysis. This is done by applying a known microarray data set to each tool and testing their different functions. The functions of these programs will then be analyzed to determine their roles in learning about biology and assisting new researchers with their experiments. It was found that each tools holds a very unique and important role in pathway analysis. Visualization pathways have the role of exploring individual pathways and interpreting genomic results. Quantification pathways use statistical tests to determine pathway significance. Together one can find pathways of interest and then explore areas of interest.
ContributorsRehling, Thomas Evan (Author) / Buetow, Kenneth (Thesis director) / Wilson, Melissa (Committee member) / School of Life Sciences (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
While only the sixth most common cancer globally, liver cancer is the third most deadly. Despite the importance of accurate diagnosis and effective treatment, standard diagnostic tests for most solid organ neoplasms are not required for the most common type of liver cancer, Hepatocellular Carcinoma (HCC). In addition, major discrepancies in the practices currently in place limits the ability to develop more precise oncological treatment and prognosis. This study aimed to identify biomarkers, with potential to more accurately diagnose how far cancer has advanced within a patient and determine prognosis. It is the hope that pathways provided by this study form the basis for future research into more standardized practices and potential treatment based on specific affected biological processes. The PathOlogist tool was utilized to calculate activity metrics for 1,324 biological pathways in 374 The Cancer Genome Atlas (TCGA) hepatocellular carcinoma donors. Further statistical analysis was done on two datasets, formed to identify grade or stage at time of diagnosis for the activity levels calculated by PathOlogist. The datasets were evaluated individually. Based on the variance and normality of each pathway’s activity levels in the respective data sets analysis of variance, Tukey-Kramer, Kruskal-Wallis, and Mann-Whitney-Wilcox tests were performed, when appropriate, to determine any statistically significant differences in pathway activity levels. Pathways were identified in both stage and grade data analyses that show significant differences in activity levels across designation. While some overlap is seen, there was a significant number of pathways unique to either stage or grade. These pathways are known to affect the cell cycle, cellular transport, disease, immune system, and metabolism regulation.
The biological pathways named by this research depict prospective biomarkers for progression of hepatocellular carcinoma per subdivision within both stage and grade. These findings may be instrumental to new methods of early and more accurate diagnosis. The distinct differences in identified pathways in grade and stage illustrate the need for these new methods to not only look at stage but also grade when determining prognosis. Furthermore, the pathways identified herein have potential to aid in the development of targeted treatment based on the affected biological processes.
ContributorsGarrison, Alyssa Cameron (Author) / Buetow, Kenneth (Thesis advisor) / Hinde, Katie (Committee member) / Wilson, Melissa (Committee member) / Arizona State University (Publisher)
Created2022
Description
Evolutionary theory provides a rich framework for understanding cancer dynamics across scales of biological organization. The field of cancer evolution has largely been divided into two domains, comparative oncology - the study of cancer across the tree of life, and tumor evolution. This work provides a theoretical framework to unify these subfields with the intent that an understanding of the evolutionary dynamics driving cancer risk at one scale can inform the understanding of the dynamics on another scale. The evolution of multicellular life and the unique vulnerabilities in the cellular mechanisms that underpin it explain the ubiquity of cancer prevalence across the tree of life. The breakdown in cellular cooperation and communication that were required for multicellular life define the hallmarks of cancer. As divergent life histories drove speciation events, it similarly drove divergences in fundamental cancer risk across species. An understanding of the impact that species’ life history theory has on the underlying network of multicellular cooperation and somatic evolution allows for robust predictions on cross-species cancer risk. A large-scale veterinary cancer database is utilized to validate many of the predictions on cancer risk made from life history evolution. Changing scales to the cellular level, it lays predictions on the fate of somatic mutations and the fitness benefits they confer to neoplastic cells compared to their healthy counterparts. The cancer hallmarks, far more than just a way to unify the many seemingly unique pathologies defined as cancer, is a powerful toolset to understand how specific mutations may change the fitness of somatic cells throughout carcinogenesis and tumor progression. Alongside highlighting the significant advances in evolutionary approaches to cancer across scales, this work provides a lucid confirmation that an understanding of both scales provides the most complete portrait of evolutionary cancer dynamics.
ContributorsCompton, Zachary Taylor (Author) / Maley, Carlo C. (Thesis advisor) / Aktipis, Athena (Committee member) / Buetow, Kenneth (Committee member) / Nedelcu, Aurora (Committee member) / Compton, Carolyn (Committee member) / Arizona State University (Publisher)
Created2023
Description
The origin of Life on Earth is the greatest unsolved mystery in the history of science. In spite of progress in almost every scientific endeavor, we still have no clear theory, model, or framework to understand the processes that led to the emergence of life on Earth. Understanding such a processes would provide key insights into astrobiology, planetary science, geochemistry, evolutionary biology, physics, and philosophy. To date, most research on the origin of life has focused on characterizing and synthesizing the molecular building blocks of living systems. This bottom-up approach assumes that living systems are characterized by their component parts, however many of the essential features of life are system level properties which only manifest in the collective behavior of many components. In order to make progress towards solving the origin of life new modeling techniques are needed. In this dissertation I review historical approaches to modeling the origin of life. I proceed to elaborate on new approaches to understanding biology that are derived from statistical physics and prioritize the collective properties of living systems rather than the component parts. In order to study these collective properties of living systems, I develop computational models of chemical systems. Using these computational models I characterize several system level processes which have important implications for understanding the origin of life on Earth. First, I investigate a model of molecular replicators and demonstrate the existence of a phase transition which occurs dynamically in replicating systems. I characterize the properties of the phase transition and argue that living systems can be understood as a non-equilibrium state of matter with unique dynamical properties. Then I develop a model of molecular assembly based on a ribonucleic acid (RNA) system, which has been characterized in laboratory experiments. Using this model I demonstrate how the energetic properties of hydrogen bonding dictate the population level dynamics of that RNA system. Finally I return to a model of replication in which replicators are strongly coupled to their environment. I demonstrate that this dynamic coupling results in qualitatively different evolutionary dynamics than those expected in static environments. A key difference is that when environmental coupling is included, evolutionary processes do not select a single replicating species but rather a dynamically stable community which consists of many species. Finally, I conclude with a discussion of how these computational models can inform future research on the origins of life.
ContributorsMathis, Cole (Nicholas) (Author) / Walker, Sara I (Thesis advisor) / Davies, Paul CW (Committee member) / Chamberlin, Ralph V (Committee member) / Lachmann, Michael (Committee member) / Arizona State University (Publisher)
Created2018
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
In most diploid cells, autosomal genes are equally expressed from the paternal and maternal alleles resulting in biallelic expression. However, as an exception, there exists a small number of genes that show a pattern of monoallelic or biased-allele expression based on the allele’s parent-of-origin. This phenomenon is termed genomic imprinting and is an evolutionary paradox. The best explanation for imprinting is David Haig's kinship theory, which hypothesizes that monoallelic gene expression is largely the result of evolutionary conflict between males and females over maternal involvement in their offspring. One previous RNAseq study has investigated the presence of parent-of-origin effects, or imprinting, in the parasitic jewel wasp Nasonia vitripennis (N. vitripennis) and its sister species Nasonia giraulti (N. giraulti) to test the predictions of kinship theory in a non-eusocial species for comparison to a eusocial one. In order to continue to tease apart the connection between social and eusocial Hymenoptera, this study proposed a similar RNAseq study that attempted to reproduce these results in unique samples of reciprocal F1 Nasonia hybrids. Building a pseudo N. giraulti reference genome, differences were observed when aligning RNAseq reads to a N. vitripennis reference genome compared to aligning reads to a pseudo N. giraulti reference. As well, no evidence for parent-of-origin or imprinting patterns in adult Nasonia were found. These results demonstrated a species-of-origin effect. Importantly, the study continued to build a repository of support with the aim to elucidate the mechanisms behind imprinting in an excellent epigenetic model species, as it can also help with understanding the phenomenon of imprinting in complex human diseases.
ContributorsUnderwood, Avery Elizabeth (Author) / Wilson, Melissa (Thesis advisor) / Buetow, Kenneth (Committee member) / Gile, Gillian (Committee member) / Arizona State University (Publisher)
Created2019
Description
What makes living systems different than non-living ones? Unfortunately this question is impossible to answer, at least currently. Instead, we must face computationally tangible questions based on our current understanding of physics, computation, information, and biology. Yet we have few insights into how living systems might quantifiably differ from their non-living counterparts, as in a mathematical foundation to explain away our observations of biological evolution, emergence, innovation, and organization. The development of a theory of living systems, if at all possible, demands a mathematical understanding of how data generated by complex biological systems changes over time. In addition, this theory ought to be broad enough as to not be constrained to an Earth-based biochemistry. In this dissertation, the philosophy of studying living systems from the perspective of traditional physics is first explored as a motivating discussion for subsequent research. Traditionally, we have often thought of the physical world from a bottom-up approach: things happening on a smaller scale aggregate into things happening on a larger scale. In addition, the laws of physics are generally considered static over time. Research suggests that biological evolution may follow dynamic laws that (at least in part) change as a function of the state of the system. Of the three featured research projects, cellular automata (CA) are used as a model to study certain aspects of living systems in two of them. These aspects include self-reference, open-ended evolution, local physical universality, subjectivity, and information processing. Open-ended evolution and local physical universality are attributed to the vast amount of innovation observed throughout biological evolution. Biological systems may distinguish themselves in terms of information processing and storage, not outside the theory of computation. The final research project concretely explores real-world phenomenon by means of mapping dominance hierarchies in the evolution of video game strategies. Though the main question of how life differs from non-life remains unanswered, the mechanisms behind open-ended evolution and physical universality are revealed.
ContributorsAdams, Alyssa M (Author) / Walker, Sara I (Thesis advisor) / Davies, Paul CW (Committee member) / Pavlic, Theodore P (Committee member) / Chamberlin, Ralph V (Committee member) / Arizona State University (Publisher)
Created2017
Description
Hepatocellular carcinoma (HCC) is the third leading cause of cancer death worldwide and exhibits a male-bias in occurrence and mortality. Previous studies have provided insight into the role of inherited genetic regulation of transcription in modulating sex-differences in HCC etiology and mortality. This study uses pathway analysis to add insight into the biological processes that drive sex-differences in HCC etiology as well as a provide additional framework for future studies on sex-biased cancers. Gene expression data from normal, tumor adjacent, and HCC liver tissue were used to calculate pathway scores using a tool called PathOlogist that not only takes into consideration the molecules in a biological pathway, but also the interaction type and directionality of the signaling pathways. Analysis of the pathway scores uncovered etiologically relevant pathways differentiating male and female HCC. In normal and tumor adjacent liver tissue, males showed higher activity of pathways related to translation factors and signaling. Females did not show higher activity of any pathways compared to males in normal and tumor adjacent liver tissue. Work suggest biologic processes that underlie sex-biases in HCC occurrence and mortality. Both males and females differed in the activation of pathways related apoptosis, cell cycle, signaling, and metabolism in HCC. These results identify clinically relevant pathways for future research and therapeutic targeting.
ContributorsRehling, Thomas E (Author) / Buetow, Kenneth (Thesis advisor) / Wilson, Melissa (Committee member) / Maley, Carlo (Committee member) / Arizona State University (Publisher)
Created2021