Computational modeling of tumor cell growth as a function of nutrient dynamics guided by time-resolved microscopy

dc.contributor.advisorYankeelov, Thomas E.
dc.contributor.committeeMemberBrock, Amy
dc.contributor.committeeMemberDunn, Andrew K
dc.contributor.committeeMemberVirostko, Jack
dc.creatorYang, Jianchen (Ph. D. in biomedical engineering)
dc.date.accessioned2022-01-31T19:54:23Z
dc.date.available2022-01-31T19:54:23Z
dc.date.created2021-12
dc.date.issued2021-12-03
dc.date.submittedDecember 2021
dc.date.updated2022-01-31T19:54:24Z
dc.description.abstractThe varying and extreme nutrient conditions found in the tumor microenvironment force reprogramming of metabolism in tumor cells. This metabolic reprogramming has been identified as a hallmark of cancer. This dissertation focuses on the development and validation of an experimental-mathematical approach that predicts how the dynamics of glucose and lactate influence tumor metabolism and development. Firstly, we developed a baseline model that predicts tumor cell growth as a function of glucose availability. We employed time-resolved microscopy to track the temporal change in the number of live and dead tumor cells under different initial conditions and seeding densities. A family of mathematical models that describes the overall tumor cell growth in response to the initial glucose and confluence was constructed. The most parsimonious model selected from the family using the Akaike Information Criteria was calibrated and validated in two breast cancer cell lines (BT-474 and MDA-MB-231) and demonstrated accuracy in predicting tumor growth. Secondly, we developed noninvasive imaging of nutrient dynamics with a stable transfection of two FRET reporters, one assaying glucose concentration and one assaying lactate concentration, in the MDA-MB-231 breast cancer cell line. The FRET ratio from both reporters was found to increase with increasing concentration of the corresponding ligand and decrease over time for high initial concentration of the ligand. Significant differences in the FRET ratio corresponding to metabolic inhibition were found when cells were treated with glucose/lactate transporter inhibitors. The FRET reporters enabled us to track intracellular glucose and lactate dynamics, providing insight into tumor metabolism and response to therapy over time. Finally, we compared mechanism-based and machine learning models for predicting tumor cells growth when we introduced an inhibitor of glucose uptake as a potential treatment. We extended the baseline model to account for glucose uptake inhibition, considering both the real glucose level in the system and the glucose level accessible to tumor cells. The random forest model provided the best prediction while the mechanism-based model presented a comparable predictive capability.
dc.description.departmentBiomedical Engineering
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/2152/96433
dc.identifier.urihttp://dx.doi.org/10.26153/tsw/23349
dc.language.isoen
dc.subjectMetabolism
dc.subjectTime-resolved microscopy
dc.subjectComputational
dc.subjectBT-474
dc.subjectMDA-MB-231
dc.subjectFluorescence
dc.subjectOncometabolite
dc.subjectWarburg effect
dc.subjectHypoxia
dc.subjectCytochalasin B
dc.subjectGLUT1
dc.subjectLinear regression
dc.subjectK-nearest neighbor
dc.subjectDecision tree
dc.subjectRandom forest
dc.titleComputational modeling of tumor cell growth as a function of nutrient dynamics guided by time-resolved microscopy
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentBiomedical Engineering
thesis.degree.disciplineBiomedical Engineering
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

Access full-text files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
YANG-DISSERTATION-2021.pdf
Size:
7.7 MB
Format:
Adobe Portable Document Format

License bundle

Now showing 1 - 2 of 2
No Thumbnail Available
Name:
PROQUEST_LICENSE.txt
Size:
4.45 KB
Format:
Plain Text
Description:
No Thumbnail Available
Name:
LICENSE.txt
Size:
1.84 KB
Format:
Plain Text
Description: