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

The 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.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Biomedical Engineering

Grantor

The University of Texas at Austin

Year dc:date.issued

2021

Author dc:creator

• Yang, Jianchen (Ph. D. in biomedical engineering)

Advisor dc:contributor.advisor

• Yankeelov, Thomas E.

Committee members dc:contributor.committeemember

• Brock, Amy
• Dunn, Andrew K
• Virostko, Jack

• Metabolism
• Time-resolved microscopy
• Computational
• BT-474
• MDA-MB-231
• Fluorescence
• Oncometabolite
• Warburg effect
• Hypoxia
• Cytochalasin B
• GLUT1
• Linear regression
• K-nearest neighbor
• Decision tree
• Random forest

Language dc:language.iso

en

Identifier URI

http://dx.doi.org/10.26153/tsw/23349

OAI identifier oai:identifier

oai:repositories.lib.utexas.edu:2152/96433