Simulations of cellular processes: From single cells to colonies

High-performance computing now allows integration of data from biochemical, cryoelectron tomography, and single cell super resolution imaging studies into coherent computational models of cellular processes functioning in live cells. Here we analyze the stochastic reaction-diffusion dynamics of ribosome biogenesis and metabolic responses of Escherichia coli cells. The kinetic model of ribosome assembly is constructed from in vitro pulse chase experiments. Using our GPU based Lattice Microbe software, we have extended the model to the entire bacterial cell by computationally linking the transcription and translation events with ribosome assembly on biologically relevant time scales for slowing growing E. coli. The whole-cell model has the approximately correct growth rate of ribosomes, predicts the localization of early assembly intermediates to the nucleoid region, and reproduces the known assembly timescales for the small subunit with no modifications made to the embedded  in vitro assembly network. Finally, reaction-diffusion kinetics of metabolites in the surrounding medium are coupled with the cellular metabolic networks to demonstrate how dense colonies of interacting bacterial cells differentially respond to the competition for resources according to their position in the colony.

Zaida “Zan” Luthey-Schulten is a Lycan Professor in the Department of Chemistry and a part-time faculty member in the Beckman Institute Theoretical and Computational Biophysics Group. She is also affiliated with the Center for Biophysics and Computational Biology.

Professor Schulten received her Ph.D. in Applied Mathematics from Harvard University in 1975. From 1975 to 1980 she was a Research Fellow at the Max-Planck Institute for Biophysical Chemistry in Goettingen, and from 1980 to 1985 a Research Fellow in the Department of Theoretical Physics at the Technical University of Munich.

Her current research focuses on developing a statistical mechanical framework and computational methods to predict protein structures and on integrating evolutionary and bioinformatic data into molecular dynamics simulations of large integrated biological systems.