[C13] Cancer Biology

[C13-03] Cancer Cell Growth Modeling: A Natural Number Simulation Approach to Population Dynamics

*Takashi Sato¹, Yuichiro Shinagawa¹, Tsuyoshi Osawa² (1. Zeon Corporation (Japan), 2. The University of Tokyo (Japan))

Keywords:

Natural Number Simulation (NNS),population dynamics,cancer,immune

Cancer is estimated to affect one in two people in many developed countries. Understanding the generation and proliferation of cancer cells within the body is becoming increasingly important for the development of effective treatments and the improvement of early diagnosis. Cancer cells are thought to originate from normal cells, undergo mutations, transition into precancerous cells, and eventually become progressive cancer cells. Using mathematical models to understand these dynamics is essential for quantifying the progression of cellular changes and numerically characterizing their properties. In this study, we applied a natural number simulation (NNS) incorporating a recently developed discrete stochastic algorithm to model the dynamics of cancer cells. The model includes eleven components, such as immune cells and antibodies, along with thirteen reaction equations to simulate the time-course changes of each cell type. NNS is advantageous for its ease of implementation and flexibility in accommodating various modeling scenarios. Here, we present the results of our analysis, considering three different patterns of cancer cell dynamics, considering variations in cancer progression and immune responses.