Sheehan, Robert
(2016)
Closing the loop: A combined computational modeling and experimental approach provides novel insights into immune cell signaling systems and their global effects.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
Systems biology is an approach that marries complimentary disciplines, encouraging the use of quantitative methods to help define, explain, and predict biological processes. By building computational models of biological systems, we can pose new biologically motivated questions and make falsifiable, quantitative predictions. In this thesis I will discuss the cycle of model building and experimental validation, and how it has provided insight into poorly and understood systems and allowed us to predict the effects of perturbations on these systems, which could have real and significant effects in human health and medicine. First, we model the activation of neutrophils in sepsis. By fitting a single model to two sets of data, coming from animals that survive and succumb to the same bacterial challenge, we create a realistic representation of biological variation, showing how a single network architecture can lead to different outcomes. Additionally, this method allows us to identify markers for sepsis susceptibility and identify and optimize a potential treatment option to lead to improved outcomes. Next, we model signaling downstream of the T cell receptor, and how this leads to differentiation decision making in CD4 T cells. By modeling the dynamics of this signaling network under varying antigen doses, we are able to identify network elements critical to dose discrimination, leading to the production of Treg cells following low dose stimulation and Th cells following high dose stimulation. We can then perturb these elements of the network, to potentially fine tune mature T cell populations to alter the trajectories of autoimmune disorders or cancer. Finally, we model the dynamics of IL-17 signaling. This allows us to understand how ubiquitin scaffolds form following cytokine stimulation, leading to the activation of NF-B, and how the ubiquitin editing enzyme A20 acts as a negative feedback regulator by breaking these chains. This allows us to better understand ubiquitin oligomerization as a fulcrum in the system, and how changes in A20 and ubiquitin binding proteins lead to different profiles of NF-B activation and could play a role in inflammatory disorders.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
|
| Date: |
8 September 2016 |
| Date Type: |
Publication |
| Defense Date: |
22 July 2016 |
| Approval Date: |
8 September 2016 |
| Submission Date: |
2 September 2016 |
| Access Restriction: |
1 year -- Restrict access to University of Pittsburgh for a period of 1 year. |
| Number of Pages: |
181 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
School of Medicine > Computational and Systems Biology |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
"computational biology", modeling, "rule-based modeling", immunology, sepsis, "T cell differentiation", IL-17, NF-kB |
| Date Deposited: |
08 Sep 2016 19:27 |
| Last Modified: |
08 Sep 2017 05:15 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/29440 |
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