Lane Center for Computational Biology
School of Computer Science, Carnegie Mellon University
Spatiotemporal Modeling of Actin Cytoskeletal Mechanics
Cells are complex, dynamic systems that actively adapt to various stimuli including mechanical alterations. Central to understanding cellular response to mechanical stimulation is the organization of the cytoskeleton and its actin filament network. While there is extensive research on the downstream signaling effects of mechanical forces, there is a lack of understanding of how physical forces are converted into biochemical signals that are classically understood to control cellular behavior. Here, we approach this problem by utilizing coarsegrained multiscale models of cell mechanics. We begin with a minimalistic network Monte Carlo approach to model cytoskeletal actin filament organization under cyclic stretching-based energy minimization. After we establish that our cytoskeleton model can recapitulate experimental results under single-mode mechanical stimulation, we apply this model to emulate the response of an in vitro network of actin filaments and associated signaling molecules undergoing stretch-based mechanotransduction in order to answer fundamental questions about the physical-biochemical basis of mechanically-induced signaling. Lastly, we upgrade our initial model to also incorporate fluid shear stress such that our model can experience both cyclic stretch and cyclic shear while still maintaining an overall 2D structure.