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• W2046313315 startingPage "3039" @default.
• W2046313315 abstract "Signaling networks regulate cellular responses to environmental stimuli through cascades of protein interactions. 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 developed a numerical scheme consisting of a cut cell finite volume spatial discretization coupled with level set methods to simulate the resulting advection-reaction-diffusion system. We then apply the method to several biochemical reaction networks in changing geometries. We found that a Turing instability can develop exclusively by cell deformations that maintain constant area. For a Turing system with a geometry-dependent single or double peak solution, simulations in a dynamically changing geometry suggest that a single peak solution is the only stable one, independent of the oscillation frequency. The method is also applied to a model of a signaling network in a migrating fibroblast." @default.
• W2046313315 created "2016-06-24" @default.
• W2046313315 creator A5002387944 @default.
• W2046313315 creator A5035692473 @default.
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• W2046313315 date "2010-01-01" @default.
• W2046313315 modified "2023-10-17" @default.
• W2046313315 title "Simulating Biochemical Signaling Networks in Complex Moving Geometries" @default.
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• W2046313315 doi "https://doi.org/10.1137/090779693" @default.
• W2046313315 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3786195" @default.
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