Date of Award


Degree Type


Degree Name

Master of Science


Biomedical Engineering

Major Professor

Xiaopeng Zhao

Committee Members

J.A.M Boulet, Allen J. Baker


Mechanosensation in cells is a well known phenomenon that is associated with cellular responses to force. Our knowledge of the trigger mechanism of this phenomenon is, however, limited. Earlier studies in this field have used atomic simulations, which although being accurate, are limited in their feasibility in multi-length scenarios like a mechanosensitive channel that undergoes micro-level changes in the composition of the protein to cause a macro-level change in the state of a biological structure such as the muscle. Finite Element Analysis has been used in various engineering fields to study the mechanical response of complex structures. The current study is a step in utilizing the phenomenal capabilities of Finite Element Analysis in developing and studying a 3D model (Membrane-Channel) of a mechanosensitive channel of large conductance (MscL). A simplified CAD structure of Mycobacterium tuberculosis (TbMscL) was developed in the first stage of this study. The authenticity of this model was tested by applying two types of loading conditions, namely (i) In-plane stretch and (ii) Out-of-plane bending. The results obtained from the first step of analysis are in accordance with previous experimental data, which elucidates the fact that tension within the membrane guides the gating mechanism of the channel and not the curvature of the membrane. The second stage of the analysis involved the use of the same model to study the disease commotio cordis. This was achieved by calculating the loading conditions during the onset of the condition in the human heart and then transferring those conditions to the Membrane-Channel model developed in the first stage. The result showed that although the channel did not fully open but there was a significant change in the channel‟s radius that might cause the flow of ions, thereby changing the state of the channel. It is anticipated that this model will help future research in areas that conventionally have been difficult to model.

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