Cheemalavagu, Neha
(2024)
Predicting gene level sensitivity to signaling perturbation using computational modeling approaches.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
Cells make functional decisions according to various signals in their microenvironment. They transmit such signals via tightly controlled signaling pathways that induce context-specific gene expression responses that govern a multitude of functional responses. Dysregulation of this precise cell signaling process can lead to abnormal transcriptional responses and diseased cell states. With a particular focus on immune cells, we need to get a clearer understanding of how such cells sense numerous, and often conflicting, cytokine signals to induce specific gene responses since many inflammatory diseases arise from improper cytokine
signaling. A more complete picture of the signal-to-gene response, in which we can identify key regulators mediating specific portions of the transcriptional response, will help in the development of targeted therapeutics with minimal off-target effects.
Due to the complexity of the signaling process, systems-level approaches are particularly useful for interrogating how context-specificity is achieved in the signaling response. Additionally, the use of computational modeling, along with rich experimental datasets, allows us to extract the maximum amount of information about the signaling process from the level of stimuli sensing by the cell, all the way down to the induction of genes. Computational modeling encompasses both mechanistic and data-driven statistical approaches, each of which has its inherent strengths and weaknesses when it comes to modeling complex biological systems.
While previous efforts have mostly employed one or the other modeling modality, this dissertation utilizes both, in tandem with diverse datasets describing different portions of the signaling-to-gene process, to predict the impact of signaling perturbation on targeted downstream gene expression. The work focuses on cytokine signaling in macrophages, as cytokines inform the inflammatory functions of these highly plastic immune cells. The work first focuses on the JAK-STAT pathway as the mediator of cytokine-specific transcriptional responses and begins with an integrative mechanistic-to-machine learning model to predict the impact of JAK2 inhibition on STAT phosphorylation dynamics and sets of genes. After establishing a JAK-STAT signaling model, we shift to a broader approach to identify other, non-STAT signaling events predictive of dynamic gene sets. Overall, this work serves as an early conceptual framework for identifying and manipulating signaling events with targeted gene outcomes.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
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| Date: |
27 September 2024 |
| Date Type: |
Publication |
| Defense Date: |
10 April 2024 |
| Approval Date: |
27 September 2024 |
| Submission Date: |
24 April 2024 |
| Access Restriction: |
2 year -- Restrict access to University of Pittsburgh for a period of 2 years. |
| Number of Pages: |
107 |
| 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: |
computational biology, systems immunology, signaling, gene expression, dynamics, mechanistic modeling, statistical modeling, machine learning |
| Date Deposited: |
27 Sep 2024 15:30 |
| Last Modified: |
27 Sep 2024 15:30 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/46201 |
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