Using Monte Carlo Simulations of Retinoblastoma Progression to Model Mutation Rates and Genetic Variability
Session Number
CMPS 38
Advisor(s)
Dr. Christina Fliege, University of Illinois Urbana Champaign
Joshua Allen, University of Illinois Urbana Champaign
Discipline
Computer Science
Start Date
17-4-2024 10:45 AM
End Date
17-4-2024 11:00 AM
Abstract
Human retinoblastoma is a pediatric cancer initiated by RB gene mutations in the developing retina. Originating in the retina, RB evolves in four separate stages. However, most patients do not have a distinct transition through these separate stages and these stages are not always preceded by a detectable preface state, making the cancer difficult to model. In this project, we present a novel method to model retinoblastoma progression, employing Monte Carlo simulations, a class of computational algorithms that rely on repeated random sampling to obtain numerical results, reflecting the randomness of real-life genetic mutations. This technique allows us to randomly generate mutations on the RB1 gene taking into account the genetic variability and hereditary factors known to influence retinoblastoma. Monte Carlo allows us to analyze the mutation rates and evolution of this cancer, also offering insights into the probability distributions of mutation occurrences, the mutations that are most likely to contribute to disease progression, and their impact on retinoblastoma progression.
Using Monte Carlo Simulations of Retinoblastoma Progression to Model Mutation Rates and Genetic Variability
Human retinoblastoma is a pediatric cancer initiated by RB gene mutations in the developing retina. Originating in the retina, RB evolves in four separate stages. However, most patients do not have a distinct transition through these separate stages and these stages are not always preceded by a detectable preface state, making the cancer difficult to model. In this project, we present a novel method to model retinoblastoma progression, employing Monte Carlo simulations, a class of computational algorithms that rely on repeated random sampling to obtain numerical results, reflecting the randomness of real-life genetic mutations. This technique allows us to randomly generate mutations on the RB1 gene taking into account the genetic variability and hereditary factors known to influence retinoblastoma. Monte Carlo allows us to analyze the mutation rates and evolution of this cancer, also offering insights into the probability distributions of mutation occurrences, the mutations that are most likely to contribute to disease progression, and their impact on retinoblastoma progression.