2023 North America Conference on Lung Cancer (NACLC) - Posters and Abstracts-PP01.07 (Poster)Agent -Based Modeling Approach for Screening Cancer Growth

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In this study, the authors propose an agent-based modeling (ABM) approach to screen cancer growth, specifically focusing on small cell lung cancer (SCLC) and its metastasis. The majority of existing cancer models are mathematical models based on experimental data, which may not accurately reflect clinical settings. The ABM approach aims to incorporate intracellular reactions and metastasis in SCLC. The ABM was programmed using Netlogo software and was based on constants from the Itik and Banks model and data from published papers. The study outlines the steps taken to set up and tune the ABM, including incorporating published data on metastatic preferences or rates. <br /><br />The chaotic nature of the model was analyzed using COPASI and compared with the original mathematical equation. A sensitivity analysis was also conducted to assess the model's strength across different settings. The results, conclusions, and limitations of the study were not provided in the summary.<br /><br />The authors suggest that the ABM could be expanded to include more factors such as genotype, medical intervention, or a physical model of the human body. They believe that the ABM represents a rudimentary model that could eventually lead to a quick and accurate way to model cancer growth in the future.<br /><br />However, the application of agent-based modeling in the cancer field is currently limited by technological barriers and the need for a deeper understanding of tumor malignancy and metastasis. The authors explain that current cell lines used to study metastasis may not accurately represent in vivo metastatic progression. As technology advances, the authors hope that both our understanding of metastasis and the technological barriers can be overcome to develop an accurate in silico model of cancer growth and metastasis.<br /><br />Overall, the study introduces an ABM approach for screening cancer growth and highlights the potential challenges and future directions in modeling cancer.

Asset Subtitle

Shawn Zhang

Keywords

agent-based modeling

cancer growth

small cell lung cancer

metastasis

intracellular reactions

Netlogo software

metastatic preferences

chaotic nature

sensitivity analysis

genotype