Presenter Information

Mike Mascitti, SUNY GeneseoFollow

Submission Type

Poster

Start Date

4-26-2023

Abstract

Cervical cancer is a complex disease characterized by unpredictable genetic alterations of cells. Computational modeling may be applied to simulate the growth and behavior of cervical cancer within a tissue and the progression of the disease throughout the body. In our computational model, individual cells have the ability to transform behavior between the following states: healthy, precursor lesion (CN1, CN2, CN3), and cancer. Each cell state has a different mutation rate, reproductive rate, and cell life span. Our model simulates the transformation of cells into these different states. The data from our computational model shows the day-to-day growth of cancer within a tissue, and the progression of a cell evolving from healthy to cancerous. Our model suggests how cancer cells become dominant over time within a system and outgrow healthy tissue. Alterations to this model may help determine effectiveness of treatments on individual cells.

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Sponsored by Christopher Leary

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Apr 26th, 12:00 AM

187 - Modeling the Dynamics of Chromosomal Alteration Progression in Cervical Cancer: A Computational Model

Cervical cancer is a complex disease characterized by unpredictable genetic alterations of cells. Computational modeling may be applied to simulate the growth and behavior of cervical cancer within a tissue and the progression of the disease throughout the body. In our computational model, individual cells have the ability to transform behavior between the following states: healthy, precursor lesion (CN1, CN2, CN3), and cancer. Each cell state has a different mutation rate, reproductive rate, and cell life span. Our model simulates the transformation of cells into these different states. The data from our computational model shows the day-to-day growth of cancer within a tissue, and the progression of a cell evolving from healthy to cancerous. Our model suggests how cancer cells become dominant over time within a system and outgrow healthy tissue. Alterations to this model may help determine effectiveness of treatments on individual cells.

 

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