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Statistical and Mechanistic Approaches to Study Cell Signaling Dynamics

Gupta, Sanjana (2020) Statistical and Mechanistic Approaches to Study Cell Signaling Dynamics. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Cells use complex signaling systems to constantly detect environmental changes, relay extracellular information from the cell membrane to the nucleus, and drive cell responses, such as transcription. The ability of each single cell to dynamically respond to changes in its environment is the basis for healthy, functioning, multicellular beings. Diseases often arise from dysregulated signaling, and our ability to manipulate cell responses, that stems from our growing understanding of signaling processes, is often the basis for disease treatments.

Computational approaches can complement experimental studies of cellular systems, allowing us to formalize our growing body of knowledge of cellular biochemistry. Mechanistic modeling provides a natural framework to describe and simulate complex systems with many system components and causal interactions that often lead to non-intuitive emergent behavior, lending itself well to the analysis of signaling systems. Statistical approaches can complement mechanistic modeling by enabling an analysis of complex input-output relationships in the data, providing insight into how cells translate input environmental cues into output responses, even when the underlying mechanisms are only partially understood.

In this thesis, we explore both mechanistic and statistical approaches and address several challenges in modeling signaling processes within a cell, and signaling heterogeneity between cells, using the NF-kB pathway as a model system. First, we evaluate methods to efficiently determine numerical values of model parameters, enabling model simulations that are comparable to experimental data. Second, we develop methods to identify reduced submodels that are sufficient for the data, highlighting simple mechanisms that drive emergent behavior. Third, switching gears to study signaling heterogeneity, we use information-theoretic analyses to evaluate the capabilities of the NF-kB pathway to effectively transduce cytokine dosage information in the presence of biochemical noise. Finally, we develop a framework to calibrate mechanistic models to heterogeneous signaling data, enabling simulation-based analyses of single-cell signaling capabilities.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Gupta, Sanjanasag134@pitt.edusag134
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairFaeder, James
Committee CoChairLee, Robin
Committee MemberBanerjee,
Committee MemberSandholm, Tuomas
Date: 10 August 2020
Date Type: Publication
Defense Date: 9 July 2020
Approval Date: 10 August 2020
Submission Date: 19 July 2020
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 159
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: cell signaling, single-cell dynamics, heterogeneity, computational modeling, bayesian parameter estimation, lasso, information theory
Date Deposited: 11 Aug 2020 02:49
Last Modified: 11 Aug 2020 02:49


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