appears in a smaller region inside of the tumor. Simulations show that if the aggressive strain focuses its efforts on proliferating and does not contribute to angiogenesis signaling when in a hypoxic state, a hypertumor will form. More importantly, this resultant aggressive tumor is paradoxically prone to extinction and hypothesize is the cause of necrosis in many vascularized tumors.
Non-native consumers can significantly alter processes at the population, community, and ecosystem level, and they are a major concern in many aquatic systems. Although the community-level effects of non-native anuran tadpoles are well understood, their ecosystem-level effects have been less studied. Here, I tested the hypothesis that natural densities of non-native bullfrog tadpoles (Lithobates catesbeianus) and native Woodhouse's toad tadpoles (Anaxyrus woodhousii) have dissimilar effects on aquatic ecosystem processes because of differences in grazing and nutrient recycling (excretion and egestion). I measured bullfrog and Woodhouse's carbon, nitrogen, and phosphorus nutrient recycling rates. Then, I determined the impact of tadpole grazing on periphyton biomass (chlorophyll a) during a 39-day mesocosm experiment. Using the same experiment, I also quantified the effect of tadpole grazing and nutrient excretion on periphyton net primary production (NPP). Lastly I measured how dissolved and particulate nutrient concentrations and respiration rates changed in the presence of the two tadpole species. Per unit biomass, I found that bullfrog and Woodhouse's tadpoles excreted nitrogen and phosphorus at similar rates, though Woodhouse's tadpoles egested more carbon, nitrogen, and phosphorus. However, bullfrogs recycled nutrients at higher N:C and N:P ratios. Tadpole excretion did not cause a detectable change in dissolved nutrient concentrations. However, the percent phosphorus in mesocosm detritus was significantly higher in both tadpole treatments, compared to a tadpole-free control. Neither tadpole species decreased periphyton biomass through grazing, although bullfrog nutrient excretion increased areal NPP. This result was due to higher biomass, not higher biomass-specific productivity. Woodhouse's tadpoles significantly decreased respiration in the mesocosm detritus, while bullfrog tadpoles had no effect. This research highlights functional differences between species by showing non-native bullfrog tadpoles and native Woodhouse's tadpoles may have different effects on arid, aquatic ecosystems. Specifically, it indicates bullfrog introductions may alter primary productivity and particulate nutrient dynamics.
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.