Cells use signaling networks consisting of multiple interacting proteins to respond to changes in their environment. In many situations, such as chemotaxis, spatial and temporal information must be transmitted through a signaling network. Recent computational studies have emphasized the importance of cellular geometry in signal transduction, but have been limited in their ability to accurately represent complex cell morphologies. We present a finite volume method that addresses this problem. Our method uses Cartesian cut cells in a differential algebraic formulation to handle the complex boundary dynamics encountered in biological systems. The method is second order in space and time. Several models of signaling systems are simulated in realistic cell morphologies obtained from live cell images. We then examine the effects of geometry on signal transduction.
External signals can trigger cells to polarize and move in a specific direction. During migration, spatially localized activity of proteins is maintained. To investigate the effects of morphological changes on intracellular signaling, we present a numerical scheme consisting of a cut cell finite volume spatial discretization coupled with level set methods to simulate the resulting advection-reaction-diffusion equation. We then show that shape deformations drive a Turing-type system into an unstable regime. The method is also applied to a model of a signaling network in a migrating fibroblast.
Determining the signaling mechanisms used by membrane proteins that interact with the cytoskeleton is important for understanding phenomena such as T-cell activation and viral infection. To investigate these interactions, recent experiments have tracked the movements of single lipids and glycosyl-phosphatidylinositol (GPI) anchored protein clusters tagged with 40 nm gold particles. These experiments reveal regions of transient confinement and transient anchorage of the particles. The distribution of transient anchorage release times exhibits a long tail. We developed a stochastic model of the system to explain the transient anchorage release times and the underlying biochemical reaction system
|Advisor:||Adalsteinsson, David, Elston, Timothy C.|
|Commitee:||Forest, Mark G., Jacobson, Kenneth, Miller, Laura A.|
|School:||The University of North Carolina at Chapel Hill|
|School Location:||United States -- North Carolina|
|Source:||DAI-B 70/07, Dissertation Abstracts International|
|Subjects:||Molecular biology, Mathematics|
|Keywords:||Mathematical modeling, Numerical methods, Spatial modeling, Systems biology|
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