ERDEM, CEMAL
(2018)
EMPLOYING QUANTITATIVE SYSTEMS PHARMACOLOGY TO CHARACTERIZE DIFFERENCES IN IGF1 AND INSULIN SIGNALING PATHWAYS IN BREAST CANCER.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
Insulin and insulin-like growth factor I (IGF1) have been shown to influence cancer risk and progression through poorly understood mechanisms. Here, new insights on the mechanisms of differential MAPK and Akt activation are revealed by an iterative quantitative systems pharmacology approach. In the first iteration, I combined proteomic screening with computational network inference to uncover differences in IGF1 and insulin induced signaling. Using reverse phase protein array of 21 breast cancer cell lines treated with a time course of IGF1 and insulin, I constructed directed protein expression networks using three separate methods: (i) lasso regression, (ii) conventional matrix inversion, and (iii) entropy maximization. These networks, named here as the time translation models, were analyzed and the inferred interactions were ranked by differential magnitude to identify pathway differences. The two top candidates, chosen for experimental validation, were shown to regulate IGF1/insulin induced phosphorylation events. Both of the knock-down perturbations caused phosphorylation responses stronger in IGF1 stimulated cells compared with insulin. Overall, the time-translation modeling coupled to wet-lab experiments has proven to be powerful in inferring differential interactions downstream of IGF1 and insulin signaling, in vitro. In the second iteration, mechanistic representation of IGF1 and insulin dual signaling cascades by a set of ODEs is generated by rule-based modeling. The mechanistic network modeling provided a framework to elucidate experimental targets downstream of two receptors, which were treated as indistinguishable in previous models. The model included cascades of both mitogen-activated protein kinase (MAPK) and Akt signaling, as well as the crosstalk and feedback loops in between. The parameter perturbation scanning employed for seven different models of seven cell lines yielded new experimental hypotheses on how differential responses of MAPK and Akt originate. Complementary to the first iteration, the results in this part suggested that regulation of insulin receptor substrate 1 (IRS1) is critical in inducing differential MAPK or Akt activation. Compensation and activating feedback mechanisms collectively depressed the efficacy of anti-IGF1R/InsR therapies. With the quantitative systems pharmacologic approach, the networks of signal transduction constructed in this thesis are aimed to discern novel downstream components of the IGF1R/InsR system, and to direct patients with suitable tumor subclasses to efficient personalized clinical interventions.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
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| Date: |
29 March 2018 |
| Date Type: |
Publication |
| Defense Date: |
21 March 2018 |
| Approval Date: |
29 March 2018 |
| Submission Date: |
29 March 2018 |
| Access Restriction: |
2 year -- Restrict access to University of Pittsburgh for a period of 2 years. |
| Number of Pages: |
121 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
School of Medicine > Computational Biology |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
IGF1, Insulin, systems biology, breast cancer, mathematical modeling |
| Date Deposited: |
29 Mar 2018 19:56 |
| Last Modified: |
29 Mar 2020 05:15 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/33977 |
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