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Cancer, Complexity, ComputationLocal Quantitative and Qualitative Sensitivity Analysis of CSC Dynamical Simulation

Cancer, Complexity, Computation: Local Quantitative and Qualitative Sensitivity Analysis of CSC... [Modeling tumour development is a challenging goal that opens a window of opportunity for broad spectra of applications—from different feedback regulations in tumour system to timing and dosage of new treatments testing. When properly calibrated and validated, models can significantly improve our knowledge and contribute to narrowing clinical trials. In this study, we used the ODE model that includes only stem cells (wild and mutated) and differentiated cells to investigate inhibition of the differentiation feedback and the uncertainty in signalling parameters (self-renewal probability and division rate) parameterization of feedback regulation affects tumour growth. Local quantitative and qualitative sensitivity analysis is performed using data for breast tumour tissue. Obtained results indicate that even slight variations in initial values of signaling parameters can lead to considerable differences in tumour size over the course of 250 days. The model exerted the highest sensitivity to self-renewal probability for initial values above 0.5.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Cancer, Complexity, ComputationLocal Quantitative and Qualitative Sensitivity Analysis of CSC Dynamical Simulation

Part of the Emergence, Complexity and Computation Book Series (volume 46)
Editors: Balaz, Igor; Adamatzky, Andrew
Lalic, Branislava; Balaz, Igor
Cancer, Complexity, Computation — Aug 12, 2022
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Publisher
Springer International Publishing
Copyright
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
ISBN
978-3-031-04378-9
Pages
191 –207
DOI
10.1007/978-3-031-04379-6_8
Publisher site
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Abstract

[Modeling tumour development is a challenging goal that opens a window of opportunity for broad spectra of applications—from different feedback regulations in tumour system to timing and dosage of new treatments testing. When properly calibrated and validated, models can significantly improve our knowledge and contribute to narrowing clinical trials. In this study, we used the ODE model that includes only stem cells (wild and mutated) and differentiated cells to investigate inhibition of the differentiation feedback and the uncertainty in signalling parameters (self-renewal probability and division rate) parameterization of feedback regulation affects tumour growth. Local quantitative and qualitative sensitivity analysis is performed using data for breast tumour tissue. Obtained results indicate that even slight variations in initial values of signaling parameters can lead to considerable differences in tumour size over the course of 250 days. The model exerted the highest sensitivity to self-renewal probability for initial values above 0.5.]

Published: Aug 12, 2022

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