View source for Dynamical Networks in Translation: From Molecules to Cells

Signaling pathways in RNA:protein complexes involved in translation are
identified by
community network analysis derived from molecular dynamics simulations. These
complexes include the amino&#8208;acyl&#8208;tRNA synthetases, which set
the genetic code by charging
tRNAs with their cognate amino acids, the elongation factor EF&#8208;Tu,
which transports the
charged tRNAs to the ribosome, and the ribosome, which is the site of
protein synthesis. A
dynamic contact map defines the edges connecting nodes (amino acids and
nucleotides) in
the physical network whose overall topology is presented as a network of
communities,
local substructures that are highly intraconnected, but loosely
interconnected. While nodes
within a single community can communicate through many alternate pathways,
the
communication between monomers in different communities has to take place
through a
smaller number of critical edges or interactions which are evolutionarily
conserved. The
time dependent variation of these networks during tRNA migration is
consistent with
kinetic data and reaction mechanisms suggested at each step of translation.
In bacterial cells, translation involves thousands of these RNA:protein
complexes which
occupy a large portion of the cell volume and make a major contribution to
the extrinsic
noise of gene expression. Using data from proteomics, cryo&#8208;electron
tomography, and in
vivo single molecule fluorescence experiments, we analyze and compare the
behavior of the
inducible lac genetic switch for Escherichia coli cells modeled under fast
and slow&#8208;growth
conditions. Compared to models in which the spatial heterogeneity is
ignored, the in vivo
models predict an overall increase (decrease) in the
repressor&#8208;operator lifetimes for the
fast (slow) growing cells. The long time simulations of biochemical
pathways under in vivo
cellular conditions of molecular crowding are performed with a
lattice&#8208;based, reactiondiffusion
model that runs on graphics processing units (GPUs).