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- All Subjects: Biology
- Creators: Kuang, Yang
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Description
There has been important progress in understanding ecological dynamics through the development of the theory of ecological stoichiometry. This fast growing theory provides new constraints and mechanisms that can be formulated into mathematical models. Stoichiometric models incorporate the effects of both food quantity and food quality into a single framework that produce rich dynamics. While the effects of nutrient deficiency on consumer growth are well understood, recent discoveries in ecological stoichiometry suggest that consumer dynamics are not only affected by insufficient food nutrient content (low phosphorus (P): carbon (C) ratio) but also by excess food nutrient content (high P:C). This phenomenon, known as the stoichiometric knife edge, in which animal growth is reduced not only by food with low P content but also by food with high P content, needs to be incorporated into mathematical models. Here we present Lotka-Volterra type models to investigate the growth response of Daphnia to algae of varying P:C ratios. Using a nonsmooth system of two ordinary differential equations (ODEs), we formulate the first model to incorporate the phenomenon of the stoichiometric knife edge. We then extend this stoichiometric model by mechanistically deriving and tracking free P in the environment. This resulting full knife edge model is a nonsmooth system of three ODEs. Bifurcation analysis and numerical simulations of the full model, that explicitly tracks phosphorus, leads to quantitatively different predictions than previous models that neglect to track free nutrients. The full model shows that the grazer population is sensitive to excess nutrient concentrations as a dynamical free nutrient pool induces extreme grazer population density changes. These modeling efforts provide insight on the effects of excess nutrient content on grazer dynamics and deepen our understanding of the effects of stoichiometry on the mechanisms governing population dynamics and the interactions between trophic levels.
ContributorsPeace, Angela (Author) / Kuang, Yang (Thesis advisor) / Elser, James J (Committee member) / Baer, Steven (Committee member) / Tang, Wenbo (Committee member) / Kang, Yun (Committee member) / Arizona State University (Publisher)
Created2014
Description
The phycologist, M. R. Droop, studied vitamin B12 limitation in the flagellate Monochrysis lutheri and concluded that its specific growth rate depended on the concentration of the vitamin within the cell; i.e. the cell quota of the vitamin B12. The Droop model provides a mathematical expression to link growth rate to the intracellular concentration of a limiting nutrient. Although the Droop model has been an important modeling tool in ecology, it has only recently been applied to study cancer biology. Cancer cells live in an ecological setting, interacting and competing with normal and other cancerous cells for nutrients and space, and evolving and adapting to their environment. Here, the Droop equation is used to model three cancers.
First, prostate cancer is modeled, where androgen is considered the limiting nutrient since most tumors depend on androgen for proliferation and survival. The model's accuracy for predicting the biomarker for patients on intermittent androgen deprivation therapy is tested by comparing the simulation results to clinical data as well as to an existing simpler model. The results suggest that a simpler model may be more beneficial for a predictive use, although further research is needed in this field prior to implementing mathematical models as a predictive method in a clinical setting.
Next, two chronic myeloid leukemia models are compared that consider Imatinib treatment, a drug that inhibits the constitutively active tyrosine kinase BCR-ABL. Both models describe the competition of leukemic and normal cells, however the first model also describes intracellular dynamics by considering BCR-ABL as the limiting nutrient. Using clinical data, the differences in estimated parameters between the models and the capacity for each model to predict drug resistance are analyzed.
Last, a simple model is presented that considers ovarian tumor growth and tumor induced angiogenesis, subject to on and off anti-angiogenesis treatment. In this environment, the cell quota represents the intracellular concentration of necessary nutrients provided through blood supply. Mathematical analysis of the model is presented and model simulation results are compared to pre-clinical data. This simple model is able to fit both on- and off-treatment data using the same biologically relevant parameters.
First, prostate cancer is modeled, where androgen is considered the limiting nutrient since most tumors depend on androgen for proliferation and survival. The model's accuracy for predicting the biomarker for patients on intermittent androgen deprivation therapy is tested by comparing the simulation results to clinical data as well as to an existing simpler model. The results suggest that a simpler model may be more beneficial for a predictive use, although further research is needed in this field prior to implementing mathematical models as a predictive method in a clinical setting.
Next, two chronic myeloid leukemia models are compared that consider Imatinib treatment, a drug that inhibits the constitutively active tyrosine kinase BCR-ABL. Both models describe the competition of leukemic and normal cells, however the first model also describes intracellular dynamics by considering BCR-ABL as the limiting nutrient. Using clinical data, the differences in estimated parameters between the models and the capacity for each model to predict drug resistance are analyzed.
Last, a simple model is presented that considers ovarian tumor growth and tumor induced angiogenesis, subject to on and off anti-angiogenesis treatment. In this environment, the cell quota represents the intracellular concentration of necessary nutrients provided through blood supply. Mathematical analysis of the model is presented and model simulation results are compared to pre-clinical data. This simple model is able to fit both on- and off-treatment data using the same biologically relevant parameters.
ContributorsEverett, Rebecca Anne (Author) / Kuang, Yang (Thesis advisor) / Nagy, John (Committee member) / Milner, Fabio (Committee member) / Crook, Sharon (Committee member) / Jackiewicz, Zdzislaw (Committee member) / Arizona State University (Publisher)
Created2015
Description
Microalgae represent a potential sustainable alternative for the enhancement and protection of agricultural crops. The dry biomass and cellular extracts of Scenedesmus dimorphus were applied as a biofertilizer, a foliar spray, and a seed primer to evaluate seed germination, plant growth, and crop yield of Roma tomato plants. The dry biomass was applied as a biofertilizer at 50 g and 100 g per plant, to evaluate its effects on plant development and crop yield. Biofertilizer treatments enhanced plant growth and led to greater crop (fruit) production. Timing of biofertilizer application proved to be of importance - earlier 50 g biofertilizer application resulted in greater plant growth. Scenedesmus dimorphus culture, growth medium, and different concentrations (1%, 5%, 10%, 25%, 50%, 75%, 100%) of aqueous cell extracts were used as seed primers to determine effects on germination. Seeds treated with Scenedesmus dimorphus culture and with extract concentrations higher than 50 % (0.75 g ml-1) triggered faster germination - 2 days earlier than the control group. Extract foliar sprays of 50 ml and 100 ml, were obtained and applied to tomato plants at various extract concentrations (10%, 25%, 50%, 75% and 100%). Plant height, flower development and number of branches were significantly enhanced with 50 % (7.5 g ml-1) extracts. Higher concentration sprays led to a decrease in growth. The extracts were further screened to assess potential antimicrobial activity against the bacterium Escherichia coli ATCC 25922, the fungi Candida albicans ATCC 90028 and Aspergillus brasiliensis ATCC 16404. No antimicrobial activity was observed from the microalga extracts on the selected microorganisms.
ContributorsGarcia-Gonzalez, Jesus (Author) / Sommerfeld, Milton (Thesis advisor) / Steele, Kelly (Committee member) / Henderson, Mark (Committee member) / Arizona State University (Publisher)
Created2014
Description
In 1968, phycologist M.R. Droop published his famous discovery on the functional relationship between growth rate and internal nutrient status of algae in chemostat culture. The simple notion that growth is directly dependent on intracellular nutrient concentration is useful for understanding the dynamics in many ecological systems. The cell quota in particular lends itself to ecological stoichiometry, which is a powerful framework for mathematical ecology. Three models are developed based on the cell quota principal in order to demonstrate its applications beyond chemostat culture.
First, a data-driven model is derived for neutral lipid synthesis in green microalgae with respect to nitrogen limitation. This model synthesizes several established frameworks in phycology and ecological stoichiometry. The model demonstrates how the cell quota is a useful abstraction for understanding the metabolic shift to neutral lipid production that is observed in certain oleaginous species.
Next a producer-grazer model is developed based on the cell quota model and nutrient recycling. The model incorporates a novel feedback loop to account for animal toxicity due to accumulation of nitrogen waste. The model exhibits rich, complex dynamics which leave several open mathematical questions.
Lastly, disease dynamics in vivo are in many ways analogous to those of an ecosystem, giving natural extensions of the cell quota concept to disease modeling. Prostate cancer can be modeled within this framework, with androgen the limiting nutrient and the prostate and cancer cells as competing species. Here the cell quota model provides a useful abstraction for the dependence of cellular proliferation and apoptosis on androgen and the androgen receptor. Androgen ablation therapy is often used for patients in biochemical recurrence or late-stage disease progression and is in general initially effective. However, for many patients the cancer eventually develops resistance months to years after treatment begins. Understanding how and predicting when hormone therapy facilitates evolution of resistant phenotypes has immediate implications for treatment. Cell quota models for prostate cancer can be useful tools for this purpose and motivate applications to other diseases.
First, a data-driven model is derived for neutral lipid synthesis in green microalgae with respect to nitrogen limitation. This model synthesizes several established frameworks in phycology and ecological stoichiometry. The model demonstrates how the cell quota is a useful abstraction for understanding the metabolic shift to neutral lipid production that is observed in certain oleaginous species.
Next a producer-grazer model is developed based on the cell quota model and nutrient recycling. The model incorporates a novel feedback loop to account for animal toxicity due to accumulation of nitrogen waste. The model exhibits rich, complex dynamics which leave several open mathematical questions.
Lastly, disease dynamics in vivo are in many ways analogous to those of an ecosystem, giving natural extensions of the cell quota concept to disease modeling. Prostate cancer can be modeled within this framework, with androgen the limiting nutrient and the prostate and cancer cells as competing species. Here the cell quota model provides a useful abstraction for the dependence of cellular proliferation and apoptosis on androgen and the androgen receptor. Androgen ablation therapy is often used for patients in biochemical recurrence or late-stage disease progression and is in general initially effective. However, for many patients the cancer eventually develops resistance months to years after treatment begins. Understanding how and predicting when hormone therapy facilitates evolution of resistant phenotypes has immediate implications for treatment. Cell quota models for prostate cancer can be useful tools for this purpose and motivate applications to other diseases.
ContributorsPacker, Aaron (Author) / Kuang, Yang (Thesis advisor) / Nagy, John (Committee member) / Smith, Hal (Committee member) / Kostelich, Eric (Committee member) / Kang, Yun (Committee member) / Arizona State University (Publisher)
Created2014
Description
Predicting resistant prostate cancer is critical for lowering medical costs and improving the quality of life of advanced prostate cancer patients. I formulate, compare, and analyze two mathematical models that aim to forecast future levels of prostate-specific antigen (PSA). I accomplish these tasks by employing clinical data of locally advanced prostate cancer patients undergoing androgen deprivation therapy (ADT). I demonstrate that the inverse problem of parameter estimation might be too complicated and simply relying on data fitting can give incorrect conclusions, since there is a large error in parameter values estimated and parameters might be unidentifiable. I provide confidence intervals to give estimate forecasts using data assimilation via an ensemble Kalman Filter. Using the ensemble Kalman Filter, I perform dual estimation of parameters and state variables to test the prediction accuracy of the models. Finally, I present a novel model with time delay and a delay-dependent parameter. I provide a geometric stability result to study the behavior of this model and show that the inclusion of time delay may improve the accuracy of predictions. Also, I demonstrate with clinical data that the inclusion of the delay-dependent parameter facilitates the identification and estimation of parameters.
ContributorsBaez, Javier (Author) / Kuang, Yang (Thesis advisor) / Kostelich, Eric (Committee member) / Crook, Sharon (Committee member) / Gardner, Carl (Committee member) / Nagy, John (Committee member) / Arizona State University (Publisher)
Created2017
Description
The most advanced social insects, the eusocial insects, form often large societies in which there is reproductive division of labor, queens and workers, have overlapping generations, and cooperative brood care where daughter workers remain in the nest with their queen mother and care for their siblings. The eusocial insects are composed of representative species of bees and wasps, and all species of ants and termites. Much is known about their organizational structure, but remains to be discovered.
The success of social insects is dependent upon cooperative behavior and adaptive strategies shaped by natural selection that respond to internal or external conditions. The objective of my research was to investigate specific mechanisms that have helped shaped the structure of division of labor observed in social insect colonies, including age polyethism and nutrition, and phenomena known to increase colony survival such as egg cannibalism. I developed various Ordinary Differential Equation (ODE) models in which I applied dynamical, bifurcation, and sensitivity analysis to carefully study and visualize biological outcomes in social organisms to answer questions regarding the conditions under which a colony can survive. First, I investigated how the population and evolutionary dynamics of egg cannibalism and division of labor can promote colony survival. I then introduced a model of social conflict behavior to study the inclusion of different response functions that explore the benefits of cannibalistic behavior and how it contributes to age polyethism, the change in behavior of workers as they age, and its biological relevance. Finally, I introduced a model to investigate the importance of pollen nutritional status in a honeybee colony, how it affects population growth and influences division of labor within the worker caste. My results first reveal that both cannibalism and division of labor are adaptive strategies that increase the size of the worker population, and therefore, the persistence of the colony. I show the importance of food collection, consumption, and processing rates to promote good colony nutrition leading to the coexistence of brood and adult workers. Lastly, I show how taking into account seasonality for pollen collection improves the prediction of long term consequences.
The success of social insects is dependent upon cooperative behavior and adaptive strategies shaped by natural selection that respond to internal or external conditions. The objective of my research was to investigate specific mechanisms that have helped shaped the structure of division of labor observed in social insect colonies, including age polyethism and nutrition, and phenomena known to increase colony survival such as egg cannibalism. I developed various Ordinary Differential Equation (ODE) models in which I applied dynamical, bifurcation, and sensitivity analysis to carefully study and visualize biological outcomes in social organisms to answer questions regarding the conditions under which a colony can survive. First, I investigated how the population and evolutionary dynamics of egg cannibalism and division of labor can promote colony survival. I then introduced a model of social conflict behavior to study the inclusion of different response functions that explore the benefits of cannibalistic behavior and how it contributes to age polyethism, the change in behavior of workers as they age, and its biological relevance. Finally, I introduced a model to investigate the importance of pollen nutritional status in a honeybee colony, how it affects population growth and influences division of labor within the worker caste. My results first reveal that both cannibalism and division of labor are adaptive strategies that increase the size of the worker population, and therefore, the persistence of the colony. I show the importance of food collection, consumption, and processing rates to promote good colony nutrition leading to the coexistence of brood and adult workers. Lastly, I show how taking into account seasonality for pollen collection improves the prediction of long term consequences.
ContributorsRodríguez Messan, Marisabel (Author) / Kang, Yun (Thesis advisor) / Castillo-Chavez, Carlos (Thesis advisor) / Kuang, Yang (Committee member) / Page Jr., Robert E (Committee member) / Gardner, Carl (Committee member) / Arizona State University (Publisher)
Created2018
Description
Is it possible to treat the mouth as a natural environment, and determine new methods to keep the microbiome in check? The need for biodiversity in health may suggest that every species carries out a specific function that is required to maintain equilibrium and homeostasis within the oral cavity. Furthermore, the relationship between the microbiome and its host is mutually beneficial because the host is providing microbes with an environment in which they can flourish and, in turn, keep their host healthy. Reviewing examples of larger scale environmental shifts could provide a window by which scientists can make hypotheses. Certain medications and healthcare treatments have been proven to cause xerostomia. This disorder is characterized by a dry mouth, and known to be associated with a change in the composition, and reduction, of saliva. Two case studies performed by Bardow et al, and Leal et al, tested and studied the relationships of certain medications and confirmed their side effects on the salivary glands [2,3]. Their results confirmed a relationship between specific medicines, and the correlating complaints of xerostomia. In addition, Vissink et al conducted case studies that helped to further identify how radiotherapy causes hyposalivation of the salivary glands [4]. Specifically patients that have been diagnosed with oral cancer, and are treated by radiotherapy, have been diagnosed with xerostomia. As stated prior, studies have shown that patients having an ecologically balanced and diverse microbiome tend to have healthier mouths. The oral cavity is like any biome, consisting of commensalism within itself and mutualism with its host. Due to the decreased salivary output, caused by xerostomia, increased parasitic bacteria build up within the oral cavity thus causing dental disease. Every human body contains a personalized microbiome that is essential to maintaining health but capable of eliciting disease. The Human Oral Microbiomics Database (HOMD) is a set of reference 16S rRNA gene sequences. These are then used to define individual human oral taxa. By conducting metagenomic experiments at the molecular and cellular level, scientists can identify and label micro species that inhabit the mouth during parasitic outbreaks or a shifting of the microbiome. Because the HOMD is incomplete, so is our ability to cure, or prevent, oral disease. The purpose of the thesis is to research what is known about xerostomia and its effects on the complex microbiome of the oral cavity. It is important that researchers determine whether this particular perspective is worth considering. In addition, the goal is to create novel experiments for treatment and prevention of dental diseases.
ContributorsHalcomb, Michael Jordan (Author) / Chen, Qiang (Thesis director) / Steele, Kelly (Committee member) / Barrett, The Honors College (Contributor) / College of Letters and Sciences (Contributor)
Created2015-05
Description
Abstract
Purpose—Use a framework of genetic knowledge to investigate the association between the genotypes of various genes with phenotypes, specifically the traits of elite athletes, in order to establish a personal opinion on their relevance to athletic performance.
Methods—Assemble and analyze selected published scientific studies on genotype and athletic performance and lastly to formulate a personal opinion on the value of genetic testing of athletes. ACTN3, ACE, MSTN, and apoE were the genes selected for analyses.
Results—Two genes, ACTN3 and ACE, showed a significant relationship of genotype to phenotypic traits related to athletic performance. ApoE did not demonstrate a phenotypic association with athletic performance, however it showed a correlation with injury susceptibility leading to traumatic brain injury (TBI). MSTN did not show a phenotypic association with athletic performance.
Conclusion—When considering the multifactorial nature of athletics, each sport must be investigated individually due to the different individual requirements. ACTN3 and ACE are the most widely studied genes, therefore, considerable data on their relevance to athletic performance was easily obtained and supported a relationship between genotype and athletic performance.
Purpose—Use a framework of genetic knowledge to investigate the association between the genotypes of various genes with phenotypes, specifically the traits of elite athletes, in order to establish a personal opinion on their relevance to athletic performance.
Methods—Assemble and analyze selected published scientific studies on genotype and athletic performance and lastly to formulate a personal opinion on the value of genetic testing of athletes. ACTN3, ACE, MSTN, and apoE were the genes selected for analyses.
Results—Two genes, ACTN3 and ACE, showed a significant relationship of genotype to phenotypic traits related to athletic performance. ApoE did not demonstrate a phenotypic association with athletic performance, however it showed a correlation with injury susceptibility leading to traumatic brain injury (TBI). MSTN did not show a phenotypic association with athletic performance.
Conclusion—When considering the multifactorial nature of athletics, each sport must be investigated individually due to the different individual requirements. ACTN3 and ACE are the most widely studied genes, therefore, considerable data on their relevance to athletic performance was easily obtained and supported a relationship between genotype and athletic performance.
ContributorsMinto, Jordan Taylor- Lloyd (Author) / Steele, Kelly (Thesis director) / Penton, C. Ryan (Committee member) / College of Integrative Sciences and Arts (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Genome-wide, single nucleotide polymorphisms (SNPs) and germination data were analyzed to better understand species delimitation and salt-tolerance within the legume genus Medicago. Molecular phylogenies revealed that the widely-used, genomic model line R108 and two deeply divergent accessions of Medicago truncatula are in fact more closely related to Medicago littoralis than to other accessions representing Medicago truncatula. This result was supported by germination data wherein the two accessions representing deeply divergent Medicago truncatula demonstrated salt-tolerance that was more similar to Medicago littoralis than to other accessions of Medicago truncatula. Molecular phylogenies revealed that two additional accessions representing deeply divergent Medicago truncatula appear to be more closely related to Medicago italica than to other accessions representing Medicago truncatula. The results of the present study elucidate complex evolutionary relationships and contribute to the present understanding of existing salt-tolerance within Medicago.
ContributorsHopkins, Andrew David (Author) / Wojciechowski, Martin (Thesis advisor) / Park, Yujin (Committee member) / Steele, Kelly (Committee member) / Arizona State University (Publisher)
Created2023
Description
Efforts to treat prostate cancer have seen an uptick, as the world’s most commoncancer in men continues to have increasing global incidence. Clinically, metastatic
prostate cancer is most commonly treated with hormonal therapy. The idea behind
hormonal therapy is to reduce androgen production, which prostate cancer cells
require for growth. Recently, the exploration of the synergistic effects of the drugs
used in hormonal therapy has begun. The aim was to build off of these recent
advancements and further refine the synergistic drug model. The advancements I
implement come by addressing biological shortcomings and improving the model’s
internal mechanistic structure. The drug families being modeled, anti-androgens,
and gonadotropin-releasing hormone analogs, interact with androgen production in a
way that is not completely understood in the scientific community. Thus the models
representing the drugs show progress through their ability to capture their effect
on serum androgen. Prostate-specific antigen is the primary biomarker for prostate
cancer and is generally how population models on the subject are validated. Fitting
the model to clinical data and comparing it to other clinical models through the
ability to fit and forecast prostate-specific antigen and serum androgen is how this
improved model achieves validation. The improved model results further suggest that
the drugs’ dynamics should be considered in adaptive therapy for prostate cancer.
prostate cancer is most commonly treated with hormonal therapy. The idea behind
hormonal therapy is to reduce androgen production, which prostate cancer cells
require for growth. Recently, the exploration of the synergistic effects of the drugs
used in hormonal therapy has begun. The aim was to build off of these recent
advancements and further refine the synergistic drug model. The advancements I
implement come by addressing biological shortcomings and improving the model’s
internal mechanistic structure. The drug families being modeled, anti-androgens,
and gonadotropin-releasing hormone analogs, interact with androgen production in a
way that is not completely understood in the scientific community. Thus the models
representing the drugs show progress through their ability to capture their effect
on serum androgen. Prostate-specific antigen is the primary biomarker for prostate
cancer and is generally how population models on the subject are validated. Fitting
the model to clinical data and comparing it to other clinical models through the
ability to fit and forecast prostate-specific antigen and serum androgen is how this
improved model achieves validation. The improved model results further suggest that
the drugs’ dynamics should be considered in adaptive therapy for prostate cancer.
ContributorsReckell, Trevor (Author) / Kostelich, Eric (Thesis advisor) / Kuang, Yang (Committee member) / Mahalov, Alex (Committee member) / Arizona State University (Publisher)
Created2020