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Cell biomechanics of the central nervous system

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dc.contributor.advisor Simona Socrate and Roger Kamm. en_US Bernick, Kristin Briana en_US
dc.contributor.other Massachusetts Institute of Technology. Dept. of Biological Engineering. en_US 2011-11-18T21:00:06Z 2011-11-18T21:00:06Z 2011 en_US 2011 en_US
dc.description Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biological Engineering, 2011. en_US
dc.description Cataloged from PDF version of thesis. en_US
dc.description Includes bibliographical references (p. 133-153). en_US
dc.description.abstract Traumatic brain injury (TBI) is a significant cause of death and morbidity in both the civilian and military populations. The major causes of TBI, such as motor vehicle accidents, falls, sports concussions, and ballistic and explosive blast threats for military personnel, are well established and extensively characterized; however, there remains much to be learned about the specific mechanisms of damage leading to brain injury, especially at the cellular level. In order to understand how cells of the central nervous system (CNS) respond to mechanical insults and stimuli, a combined modeling/experimental approach was adopted. A computational framework was developed to accurately model how cells deform under various macroscopically imposed loading conditions. In addition, in vitro (cell culture) models were established to investigate damage responses to biologically relevant mechanical insults. In order to develop computational models of cell response to mechanical loading, it is essential to have accurate material properties for all cells of interest. In this work, the mechanical responses of neurons and astrocytes were quantified using atomic force microscopy (AFM) at three different loading rates and under relaxation to enable characterization of both the elastic and viscous components of the cell response. AFM data were used to calibrate an eight-parameter rheological model implemented in the framework of a commercial finite element package (Abaqus). Model parameters fit to the measured responses of neurons and astrocytes provide a quantitative measure of homogenized nonlinear viscoelastic properties for each cell type. In order to ensure that the measured responses could be considered representative of cell populations in their physiological environment, cells were also grown and tested on substrates of various stiffness, with the softest substrate mimicking the stiffness of brain tissue. Results of this study showed both the morphology and measured force response of astrocytes to be significantly affected by the stiffness of their substrate, with cells becoming increasingly rounded on soft substrates. Results of simulations suggested that changes in cell morphology were able to account for the observed changes in AFM force response, without significant changes to the cell material properties. In contrast, no significant changes in cell morphology were observed for neurons. These results highlight the importance of growing cells in a biologically relevant environment when studying mechanically mediated responses, such as TBI. To address this requirement, we developed two model systems with CNS cells grown in soft, 3D gels to investigate damage arising from dynamic compressive loading and from a shock pressure wave. These damage protocols, coupled with the single cell computational models, provide a new tool set for characterizing damage mechanisms in CNS cells and for studying TBI in highly controllable in vitro conditions. en_US
dc.description.statementofresponsibility by Kristin Briana Bernick. en_US
dc.format.extent 153 p. en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. en_US
dc.rights.uri en_US
dc.subject Biological Engineering. en_US
dc.title Cell biomechanics of the central nervous system en_US
dc.type Thesis en_US Ph.D. en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Biological Engineering. en_US
dc.identifier.oclc 758862907 en_US

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