P. K. Newton

This paper develops a stochastic model to study adaptive chemotherapy's impact on tumor sub-populations and shows that resistant sub-populations grow with increasing control parameters.

This paper develops optimal dynamic incentive schedules for Hawk-Dove evolutionary games to maximize or minimize aggressive population frequency over time.

This paper evaluates the robustness of adaptive chemotherapy schedules using a stochastic evolutionary game theory model and finds that error distributions grow exponentially with treatment cycles, leading to treatment failure.

This paper develops an optimal control theory to maximize cooperation in the prisoner's dilemma game by dynamically adjusting payoffs over time, with applications in evolutionary biology and related fields.

This paper introduces three novel concepts to aid in designing multi-drug adaptive therapies for cancer by analyzing tumor evolution dynamics and treatment strategies.

The paper introduces an evolutionary model to explain competitive release in metastatic castration-resistant prostate cancer, showing how treatment reduces sensitive cells and allows resistant cells to dominate.

This paper compares low dose metronomic and maximum tolerated dose chemotherapy strategies, finding that metronomic approaches can reduce tumor cells more effectively, especially for fast-growing tumors over multiple cycles.