ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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Description
Division of labor, whereby different group members perform different functions, is a fundamental attribute of sociality. It appears across social systems, from simple cooperative groups to complex eusocial colonies. A core challenge in sociobiology is to explain how patterns of collective organization are generated. Theoretical models propose that division of labor self-organizes, or emerges, from interactions among group members and the environment; division of labor is also predicted to scale positively with group size. I empirically investigated the emergence and scaling of division of labor in evolutionarily incipient groups of sweat bees and in eusocial colonies of harvester ants. To test whether division of labor is an emergent property of group living during early social evolution, I created de novo communal groups of the normally solitary sweat bee Lasioglossum (Ctenonomia) NDA-1. A division of labor repeatedly arose between nest excavation and guarding tasks; results were consistent with hypothesized effects of spatial organization and intrinsic behavioral variability. Moreover, an experimental increase in group size spontaneously promoted higher task specialization and division of labor. Next, I examined the influence of colony size on division of labor in larger, more integrated colonies of the harvester ant Pogonomyrmex californicus. Division of labor scaled positively with colony size in two contexts: during early colony ontogeny, as colonies grew from tens to hundreds of workers, and among same-aged colonies that varied naturally in size. However, manipulation of colony size did not elicit a short-term response, suggesting that the scaling of division of labor in P. californicus colonies is a product of functional integration and underlying developmental processes, rather than a purely emergent epiphenomenon. This research provides novel insights into the organization of work in insect societies, and raises broader questions about the role of size in sociobiology.
ContributorsHolbrook, Carter Tate (Author) / Fewell, Jennifer H (Thesis advisor) / Gadau, Jürgen (Committee member) / Harrison, Jon F. (Committee member) / Hölldobler, Berthold (Committee member) / Johnson, Robert A. (Committee member) / Arizona State University (Publisher)
Created2011
Description
The most abundantly studied societies, with the exception of humans, are those of the eusocial insects, which include all ants. Eusocial insect societies are typically composed of many dozens to millions of individuals, referred to as nestmates, which require some form of communication to maintain colony cohesion and coordinate the activities within them. Nestmate recognition is the process of distinguishing between nestmates and non-nestmates, and embodies the first line of defense for social insect colonies. In ants, nestmate recognition is widely thought to occur through olfactory cues found on the exterior surfaces of individuals. These cues, called cuticular hydrocarbons (CHCs), comprise the overwhelming majority of ant nestmate profiles and help maintain colony identity. In this dissertation, I investigate how nestmate recognition is influenced by evolutionary, ontogenetic, and environmental factors. First, I contributed to the sequencing and description of three ant genomes including the red harvester ant, Pogonomyrmex barbatus, presented in detail here. Next, I studied how variation in nestmate cues may be shaped through evolution by comparatively studying a family of genes involved in fatty acid and hydrocarbon biosynthesis, i.e., the acyl-CoA desaturases, across seven ant species in comparison with other social and solitary insects. Then, I tested how genetic, developmental, and social factors influence CHC profile variation in P. barbatus, through a three-part study. (1) I conducted a descriptive, correlative study of desaturase gene expression and CHC variation in P. barbatus workers and queens; (2) I explored how larger-scale genetic variation in the P. barbatus species complex influences CHC variation across two genetically isolated lineages (J1/J2 genetic caste determining lineages); and (3) I experimentally examined how CHC development is influenced by an individual’s social environment. In the final part of my work, I resolved discrepancies between previous findings of nestmate recognition behavior in P. barbatus by studying how factors of territorial experience, i.e., spatiotemporal relationships, affect aggressive behaviors among red harvester ant colonies. Through this research, I was able to identify promising methodological approaches and candidate genes, which both broadens our understanding of P. barbatus nestmate recognition systems and supports future functional genetic studies of CHCs in ants.
ContributorsCash, Elizabeth I (Author) / Gadau, Jürgen (Thesis advisor) / Liebig, Jürgen (Thesis advisor) / Fewell, Jennifer (Committee member) / Hölldobler, Berthold (Committee member) / Kusumi, Kenro (Committee member) / Arizona State University (Publisher)
Created2016
Description
Resistance to existing anti-cancer drugs poses a key challenge in the field of medical oncology, in that it results in the tumor not responding to treatment using the same medications to which it responded previously, leading to treatment failure. Adaptive therapy utilizes evolutionary principles of competitive suppression, leveraging competition between drug resistant and drug sensitive cells, to keep the population of drug resistant cells under control, thereby extending time to progression (TTP), relative to standard treatment using maximum tolerated dose (MTD). Development of adaptive therapy protocols is challenging, as it involves many parameters, and the number of parameters increase exponentially for each additional drug. Furthermore, the drugs could have a cytotoxic (killing cells directly), or a cytostatic (inhibiting cell division) mechanism of action, which could affect treatment outcome in important ways. I have implemented hybrid agent-based computational models to investigate adaptive therapy, using either a single drug (cytotoxic or cytostatic), or two drugs (cytotoxic or cytostatic), simulating three different adaptive therapy protocols for treatment using a single drug (dose modulation, intermittent, dose-skipping), and seven different treatment protocols for treatment using two drugs: three dose modulation (DM) protocols (DM Cocktail Tandem, DM Ping-Pong Alternate Every Cycle, DM Ping-Pong on Progression), and four fixed-dose (FD) protocols (FD Cocktail Intermittent, FD Ping-Pong Intermittent, FD Cocktail Dose-Skipping, FD Ping-Pong Dose-Skipping). The results indicate a Goldilocks level of drug exposure to be optimum, with both too little and too much drug having adverse effects. Adaptive therapy works best under conditions of strong cellular competition, such as high fitness costs, high replacement rates, or high turnover. Clonal competition is an important determinant of treatment outcome, and as such treatment using two drugs leads to more favorable outcome than treatment using a single drug. Switching drugs every treatment cycle (ping-pong) protocols work particularly well, as well as cocktail dose modulation, particularly when it is feasible to have a highly sensitive measurement of tumor burden. In general, overtreating seems to have adverse survival outcome, and triggering a treatment vacation, or stopping treatment sooner when the tumor is shrinking seems to work well.
ContributorsSaha, Kaushik (Author) / Maley, Carlo C (Thesis advisor) / Forrest, Stephanie (Committee member) / Anderson, Karen S (Committee member) / Cisneros, Luis H (Committee member) / Arizona State University (Publisher)
Created2023
Modeling Spatial Competition and Adaptive Therapy Protocols in Three-Dimensional Vascularized Tumors
Description
In contrast to traditional chemotherapy for cancer which fails to address tumor heterogeneity, raises patients’ levels of toxicity, and selects for drug-resistant cells, adaptive therapy applies ideas from cancer ecology in employing low-dose drugs to encourage competition between cancerous cells, reducing toxicity and potentially prolonging disease progression. Despite promising results in some clinical trials, optimizing adaptive therapy routines involves navigating a vast space of combina- torial possibilities, including the number of drugs, drug holiday duration, and drug dosages. Computational models can serve as precursors to efficiently explore this space, narrowing the scope of possibilities for in-vivo and in-vitro experiments which are time-consuming, expensive, and specific to tumor types. Among the existing modeling techniques, agent-based models are particularly suited for studying the spatial inter- actions critical to successful adaptive therapy. In this thesis, I introduce CancerSim, a three-dimensional agent-based model fully implemented in C++ that is designed to simulate tumorigenesis, angiogenesis, drug resistance, and resource competition within a tissue. Additionally, the model is equipped to assess the effectiveness of various adaptive therapy regimens. The thesis provides detailed insights into the biological motivation and calibration of different model parameters. Lastly, I propose a series of research questions and experiments for adaptive therapy that CancerSim can address in the pursuit of advancing cancer treatment strategies.
ContributorsShah, Sanjana Saurin (Author) / Daymude, Joshua J (Thesis advisor) / Forrest, Stephanie (Committee member) / Maley, Carlo C (Committee member) / Arizona State University (Publisher)
Created2023