Tyler M. Earnest, Reika Watanabe, John E. Stone, Julia Mahamid, Wolfgang
Baumeister, Elizabeth Villa, and Zaida Luthey-Schulten.
Challenges of integrating stochastic dynamics and cryo-electron
tomograms in whole-cell simulations.
Journal of Physical Chemistry B, 121:3871-3881, 2017.
EARN2017-ZLS
Cryo-electron tomography (cryo-ET) has rapidly emerged as a powerful tool to
investigate the internal, three-dimensional spatial organization of the cell. In
parallel, the GPU-based technology to perform spatially resolved stochastic
simulations of whole cells has arisen, allowing the simulation of complex
biochemical networks over cell cycle time scales using data taken from -omics,
single molecule experiments, and in vitro kinetics. By using real cell geometry
derived from cryo-ET data, we have the opportunity to imbue these highly
detailed structural data—frozen in time—with realistic biochemical dynamics and
investigate how cell structure affects the behavior of the embedded chemical
reaction network. Here we present two examples to illustrate the challenges and
techniques involved in integrating structural data into stochastic simulations.
First, a tomographic reconstruction of Saccharomyces cerevisiae is used to
construct the geometry of an entire cell through which a simple stochastic
model of an inducible genetic switch is studied. Second, a tomogram of the
nuclear periphery in a HeLa cell is converted directly to the simulation geometry
through which we study the effects of cellular substructure on the stochastic
dynamics of gene repression. These simple chemical models allow us to
illustrate how to build whole-cell simulations using cryo-ET derived geometry
and the challenges involved in such a process.
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