Date of Award
2019
Degree Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Graduate Group
Chemical and Biomolecular Engineering
First Advisor
Ravi . Radhakrishnan
Abstract
Systems models of key signaling pathways in cancer have been extensively used to under-
stand and explore the mechanisms of action of drugs and growth factors on cancer cell
signaling. In general, such models predict the effect of environmental stimuli (both chemical such as for e.g., growth factor and drugs as well as mechanical such as matrix stiffness)
in terms of activities of proteins such as ERK or AKT which are important regulators of cell
fate decisions. Although such models have helped uncover important emergent properties
of signaling networks such as ultrasensitivity, bistability, and oscillations, they miss many
key features that would make them useful in a clinical setting. 1) The predictions of activity
of proteins such as ERK or AKT cannot be directly translated into a clinically useful parameter such as cell kill rate. 2) They don’t work as well when there are multiple biological
processes operating under different time and length scales such as receptor-based signaling
(4-6 hours) and cell cycle (24-48 hours). 3) The parameter space of such models often exhibits sloppy/stiff character which affects the accuracy of predictions and the robustness of
these models. Apart from single-cell systems models of signaling, pharmacokinetic and cell
population-based pharmacodynamic models are also extensively used to predict the efficacy
of a particular therapy in a clinical setting. However, there are no direct or consistent ways
of incorporating patient-specific gene/protein expression data in these models. This thesis
describes the development and applications of a multiscale and multiparadigm framework
for signaling and pharmacodynamic models that helps us address some of the above short-
comings. First two single scale systems models are described which introduces methods of
exploration of parameter space and their effect on model predictions. Then the multiscale
framework is described and it is applied to two different cancers - Prostate Adenocarcinoma
and Nephroblastoma (Wilm’s Tumor). Special mathematical techniques were used to de-
velop algorithms that can integrate models of disparate time scales and time resolutions
(continuous vs. discrete-time). Such multiscale modeling frameworks have great potential
in the field of personalized medicine and in understanding the physics of cancer taking into
account the biology of the cells.
Recommended Citation
Ghosh, Alokendra Kumar, "A Heterogeneous And Multiscale Modeling Framework To Develop Patient-Specific Pharmacodynamic Systems Models In Cancer" (2019). Publicly Accessible Penn Dissertations. 3548.
https://repository.upenn.edu/edissertations/3548