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Multiscale Modeling of Neurobiological Systems

Kaya, Cihan (2018) Multiscale Modeling of Neurobiological Systems. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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The central nervous system (CNS) is one of the most complicated living structures in the universe. A single gene expression, the expression level of a single protein or the concentration of a neurotransmitter could regulate the entire functionality of the CNS. The CNS needs to be investigated as a multiscale system with connections among different levels. The existing technology significantly limits experimental studies, and computational modeling is a useful tool for understanding how parts are connected, regulated, and function together. Ideally, the goal is to develop unified computational methodologies for exploring biological systems at multiple scales ranging from molecular to cellular to tissue level. While rigorous models have been developed at the molecular scale, higher level approaches usually suffer from lack of physical realism and lack of knowledge on model parameters. Molecular level studies can help to define reaction schemes and parameters which could be used in cellular microphysiology models, and image data provide a structural basis for reconstructing the surroundings of the cellular system of interest.
This dissertation develops and tests a new multiscale model of dopaminergic signaling and a detailed model of the activation-triggered subunit exchange mechanism of calcium/calmodulin-dependent kinase type II (CaMKII). The goal is to develop and use computational models to understand the molecular mechanisms of neurotransmission, and how disruptions may cause complex disorders and conditions such as drug abuse. The simulations of the dopamine (DA) signaling model show that the addition of the geometry of the environment and localization of individual molecules significantly affect the DA reuptake. Consequently, the formation of DAT clusters reduces the DA clearance rate and increases DA receptor activity. In addition, the effects of the psychostimulants such as cocaine and amphetamine are also investigated. Constructed model and method can potentially serve as an in silico microscope to understand the molecular basis of signaling and regulation events in the CNS. Calibration of CaMKII model shows the limitations of the current parameter estimation methods for large biological models with long simulation times such as hours. The high dimensional parameter space and the limited and noisy data makes the parameter estimation task a challenge.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Kaya, Cihancihank@pitt.educihank
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Thesis AdvisorBahar, Ivetbahar@pitt.edubahar
Thesis AdvisorFaeder, Jamesfaeder@pitt.edufaeder
Committee ChairSorkin, Alexandersorkin@pitt.edusorkin
Committee MemberPfenning,
Date: 4 September 2018
Date Type: Publication
Defense Date: 27 July 2018
Approval Date: 4 September 2018
Submission Date: 27 August 2018
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 184
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: dopamine, dopamine transporter
Date Deposited: 04 Sep 2018 19:43
Last Modified: 04 Sep 2018 19:43


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