Date of Graduation


Document Type

Thesis (MS)

Program Affiliation

Biomedical Sciences

Degree Name

Masters of Science (MS)

Advisor/Committee Chair

Prahlad Ram

Committee Member

Joya Chandra

Committee Member

Ju-Seog Lee

Committee Member

Luay K. Nakhleh

Committee Member

William Mattox


Cancer is notonedisease butasaga of diseases and is the outcome of disturbed homeostasis in the normal cells due to the deregulation of its genetic makeup. With advent of technologies thatallowdetailed molecular characterizationoftumors, targeted therapies have emerged as a more promising and specific mode of treatment. However, a major challenge with targeted therapy is the acquired resistance in the cancer cells to these therapies, quite often very rapidly in the course of a few months. One of the major targets in cancer has been the EGFR/ErbB2 network in breast and other cancer types. Prior work from our lab and others have shown alterations in the cellular network whereby compensatory upregulation of alternative pathways such as glucose uptake and metabolism can lead to acquired resistance to anti- EGFR/ErbB2 therapy in breast cancer to Lapatinib [1]. However, one the of the very important unanswered questions at the cellular and molecular level is the mechanismsthatleadstoselection of cells that are resistant to Lapatinib whereby there exists two possibilities: 1. Cells are intrinsically resistant and are less likely to respond to the drug and get selected for2.Cellsswitch response phenotype over time leading to increased metabolism and resistance. In this proposal I will develop a predictive computational model that can be used to dynamically model the response of cellstolapatinibanddetermine what underlying response mechanisms can lead to adaptive resistance cell populations based on single cell dynamics. Models to predict the internal environment of the cell by the phenotype and vice versa will be a very novel approach to understand the adaptive resistance mechanism and to overcome it. Here, I propose to utilize an Agent-based cellular automata model to represent the cellularmicroenvironment, which can track the cellular response to drugs by tracking the metabolite or signaling levels which can then be experimentally constrained and tested using live cell FRET reporter constructs.


Measuring single cell responses, Lapatinib, heterogeneous population, Her2+, FRET