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- All Subjects: Biology
- Creators: Kuang, Yang
- Resource Type: Text
Sleep is imperative for health and wellness with direct impacts on brain function, physiology, emotional well-being, performance and safety when compromised. Adolescents and young adults are increasingly affected by factors affecting the maintenance of regular sleep schedules. College and university students are a potentially vulnerable population to sleep deprivation and sleep insufficiency. Possible factors that could contribute to poor sleep hygiene include, but are not limited to, academic pressures, social activities, and increased screen time. Arguably, students are still experiencing bone mineralization, until the age of 30 or even 40 years old, which makes it more important to understand the effects that altered sleep patterns could have on continued development of bone health. It is our understanding that to date, studies assessing the risk of sleep insufficiency on bone mineral density in college students have not been conducted. We hypothesized that college-aged students, between the ages of 18-25 years, with shorter sleep durations, greater sleep schedule variability, and poorer sleep environments will have significantly lower bone mineral density. ActiGraph monitoring, via a wrist ActiWatch was used to quantitatively measure sleep habits for up to 7 consecutive days. During the week-long study participants also captured their self-reported sleep data through the use of a sleep diary. Participants were measured one time within the study for bone mineral density of the lumbar spine and total hip through a dual energy x-ray absorptiometry. This was a preliminary analysis of a larger cross-sectional analysis looked at 17 participants, of which there were 14 females and 3 males, (n=5, 1 and 11 Hispanic, Black and White, respectively). The mean age of participants was 20.8±1.7 y with an average BMI of 22.9±3.2 kg/m2. ActiWatch measurement data showed a mean daily sleep duration of participants to be 437.5 ± 43.1 (372.5 – 509.4) minutes. Mean sleep efficiency (minutes of sleep divided by minutes of time in bed) and mean number of awakenings were 87.4±4.3 (75.4-93.4) minutes and 32.1±6.4 (22.3-42.7) awakenings, respectively. The median time for wake after sleep onset (WASO) was 34.5±10.5 (18.3-67.4) minutes. The mean bone mineral density (BMD) for the hips was 1.06±0.14 (0.81-1.28) g/cm2 with a mean BMD of the lumbar spine being 1.24±0.12 (0.92-1.43) g/cm2. Age-matched Z-scores of the hips was 0.31±0.96 (-1.6-2.1) and lumbar spine was 0.53 (IQR: 0.13, 0.98; -2.25-1.55). Neither sleep duration nor sleep efficiency was significantly correlated to BMD of either locations. While WASO was positively associated with hip and spine BMD, this value was not statistically significant in this population. Overall, associations between sleep and BMD of the femur and spine were not seen in this cohort. Further work utilizing a larger cohort will allow for control of covariates while looking for potential associations between bone health, sleep duration and efficiency.
Obesity increases the risk for colorectal cancer. In mice, a pro-obesity high-fat-diet (HFD) leads to an intestinal phenotype characterized by enhanced proliferation, numbers, function and tumor-initiating capacity of stem cells, the cell-of-origin for many intestinal cancers. This phenotype is driven by a lipid metabolism program facilitated by an intrinsic Peroxisome Proliferator-Activated Receptor/Fatty Acid Oxidation (PPAR/FAO) axis that senses and utilizes cellular lipids. However, the microbiome is a known regulator of lipid metabolism in the gut, but little is understood about how the gut commensals affect access to the lipids and alter stem cell function. Here, we use the long term HFD-fed mouse model to analyze the phenotypic changes in the intestinal stem cells (ISCs) after depletion of the gut microbiota. We find that the loss of the gut microbiome after four weeks of antibiotic treatment imposes significant changes in ISC function leading to reduced HFD ISC regenerative potential. These results indicate that the gut microbiome plays a crucial role in the lipid metabolic process which regulates and maintains the HFD ISC phenotype, and further suggests that the gut microbiome may augment the diet-induced tumor initiating capacity by altering the stem cell function.
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.
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, 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.
Chapter 2 focuses sorely on time where the escape of a generic cancer out of immune control is described by stochastic delayed differential equations (SDDEs). Without time delay and noise, this system demonstrates bistability. The effects of response time of the immune system and stochasticity in the tumor proliferation rate are studied by including delay and noise in the model. Stability, persistence and extinction of the tumor are analyzed. The result shows that both time delay and noise can induce the transition from low tumor burden equilibrium to high tumor equilibrium. The aforementioned work has been published (Han et al., 2019b).
In Chapter 3, Glioblastoma multiforme (GBM) is studied using a partial differential equation (PDE) model. GBM is an aggressive brain cancer with a grim prognosis. A mathematical model of GBM growth with explicit motility, birth, and death processes is proposed. A novel method is developed to approximate key characteristics of the wave profile, which can be compared with MRI data. Several test cases of MRI data of GBM patients are used to yield personalized parameterizations of the model. The aforementioned work has been published (Han et al., 2019a).
Chapter 4 presents an innovative way of forecasting spatial cancer invasion. Most mathematical models, including the ones described in previous chapters, are formulated based on strong assumptions, which are hard, if not impossible, to verify due to complexity of biological processes and lack of quality data. Instead, a nonparametric forecasting method using Gaussian processes is proposed. By exploiting the local nature of the spatio-temporal process, sparse (in terms of time) data is sufficient for forecasting. Desirable properties of Gaussian processes facilitate selection of the size of the local neighborhood and computationally efficient propagation of uncertainty. The method is tested on synthetic data and demonstrates promising results.
changes in the diffusion, growth, and death parameters was performed, in order to find a set of parameter values that accurately model observed tumor growth for a given patient. Additional changes were made to the diffusion parameters to account for the arrangement of nerve tracts in the brain, resulting in varying rates of diffusion. In general, small changes in the growth rates had a large impact on the outcome of the simulations, and for each patient there exists a set of parameters that allow the model to simulate a tumor that matches observed tumor growth in the patient over a period of two or three months. Furthermore, these results are more accurate with anisotropic diffusion, rather than isotropic diffusion. However, these parameters lead to inaccurate results for patients with tumors that undergo no observable growth over the given time interval. While it is possible to simulate long-term tumor growth, the simulation requires multiple comparisons to available MRI scans in order to find a set of parameters that provide an accurate prognosis.