Model selection for tumor growth with nonlinear mechanical effects
| dc.contributor.advisor | Yankeelov, Thomas E. | |
| dc.creator | Perez, Xoab | |
| dc.date.accessioned | 2019-11-18T17:13:04Z | |
| dc.date.available | 2019-11-18T17:13:04Z | |
| dc.date.created | 2019-05 | |
| dc.date.issued | 2019-05-10 | |
| dc.date.submitted | May 2019 | |
| dc.date.updated | 2019-11-18T17:13:04Z | |
| dc.description.abstract | Accurately modeling in vivo tumor growth is a persistent challenge due to the complexity of tumors and their environments. Accurate models are sought after for their potential to guide treatments and help researchers discover or better understand the underlying biological processes. Previous research has identified a reaction-diffusion formulation coupled with mechanical forces that performs well at modeling tumor growth. The focus of the current research was a similar formulation with a nonlinear constitutive equation instead of a linear constitutive equation for the mechanical forces. In this study the models performed similarly, with the nonlinear model predictions being slightly closer to the actual actual tumor growth on average. This indicates that the linear model may be a sufficiently close approximation, though the parameter estimation procedure needs improvement. Then other nonlinear models can be studied easily using the code developed for this work. | |
| dc.description.department | Computational Science, Engineering, and Mathematics | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | https://hdl.handle.net/2152/78432 | |
| dc.identifier.uri | http://dx.doi.org/10.26153/tsw/5519 | |
| dc.language.iso | en | |
| dc.subject | Nonlinear | |
| dc.subject | Mechanical | |
| dc.subject | Parameter | |
| dc.subject | Estimation | |
| dc.subject | Tumor | |
| dc.subject | Prediction | |
| dc.title | Model selection for tumor growth with nonlinear mechanical effects | |
| dc.type | Thesis | |
| dc.type.material | text | |
| thesis.degree.department | Computational Science, Engineering, and Mathematics | |
| thesis.degree.discipline | Computational Science, Engineering, and Mathematics | |
| thesis.degree.grantor | The University of Texas at Austin | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science in Computational Science, Engineering, and Mathematics |