Stochastic off-lattice modeling of molecular self-assembly in crowded environments by Green's function reaction dynamics.
The environment inside a living cell is dramatically different from that found in in vitro models, presenting a problem for computational models of biochemistry that are only beginning to capture these differences. This deviation between idealized in vitro models and more realistic intracellular conditions is particularly problematic for models of molecular self-assembly, but also specifically hard to address because the large sizes and long assembly times of biological self-assembly systems force the use of highly simplified models. We have developed a prototype of a molecular self-assembly simulator based on the Green's function reaction dynamics (GFRD) model to achieve more realistic models of assembly in the crowded conditions of the cell without unduly sacrificing tractability. We tested the model on a simple representation of dimer assembly in a two-dimensional space. Our simulations verify that the model is computationally efficient, provides a realistic quantitative model of reaction kinetics in uncrowded conditions, and exhibits expected excluded volume effects under conditions of high crowding. This work confirms the effectiveness of the GFRD technique for more realistic coarse-grained modeling of self-assembly in crowded conditions and helps lay the groundwork for exploring the effects of in vivo crowding on more complex assembly systems.
Joint Program in Computational Biology, Carnegie Mellon University and University of Pittsburgh, 654 Mellon Institute, 4400 Fifth Avenue, Pittsburgh, Pennsylvania 15213, USA.
Byoungkoo
B
Lee
Philip R
PR
Leduc
Russell
R
Schwartz
D000465:Algorithms; D000818:Animals; D001671:Biochemistry / Q000379:methods; D001703:Biophysics / Q000379:methods*; D002450:Cell Communication; D002465:Cell Movement; D002468:Cell Physiological Phenomena; D003198:Computer Simulation; D004058:Diffusion; D006801:Humans; D007700:Kinetics; D008954:Models, Biological; D015233:Models, Statistical; D008962:Models, Theoretical; D013269:Stochastic Processes