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SIMULATING BIOCHEMICAL SIGNALING NETWORKS IN COMPLEX MOVING GEOMETRIES
Wanda Strychalski1, David Adalsteinsson, Timothy C Elston
1Carolina Center for Interdisciplinary Applied Mathematics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599.
SIAM Journal on Scientific Computing : a Publication of the Society for Industrial and Applied Mathematics
|October 3, 2013
Summary
Cell deformations can trigger Turing instability, a key process in cell signaling. Our study shows single-peak solutions are stable in changing cell geometries, crucial for understanding cell migration.
Area of Science:
- Computational biology
- Biophysics
- Cellular signaling
Background:
- Cellular responses rely on complex signaling networks and protein interactions.
- Cell polarization and directed migration involve maintaining spatially localized protein activity.
- Understanding how cell shape changes affect intracellular signaling is crucial.
Purpose of the Study:
- To investigate the impact of morphological changes on intracellular signaling dynamics.
- To develop and apply a numerical method for simulating signaling in changing geometries.
- To analyze pattern formation and stability in biochemical reaction networks within dynamic environments.
Main Methods:
- Developed a numerical scheme using cut cell finite volume discretization and level set methods.
- Simulated advection-reaction-diffusion systems in changing cellular geometries.
- Applied the method to Turing instability models and a migrating fibroblast signaling network.
Main Results:
- Turing instability can arise solely from cell deformations that preserve cell area.
- In dynamic geometries, single-peak solutions for Turing systems are stable, irrespective of oscillation frequency.
- The simulation method successfully modeled signaling in a migrating fibroblast.
Conclusions:
- Cell morphology plays a significant role in regulating intracellular signaling patterns.
- The developed numerical approach is effective for studying signaling dynamics in changing cellular environments.
- Findings provide insights into the stability of signaling patterns during cell migration.
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