Dynamical Networks in Translation: From Molecules to Cells
Monday, November 14, 10AM – 11AM
Zaida Luthey-Schulten, University of Illinois at Urbana-Champaign
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‐acyl‐tRNA synthetases, which set the genetic code by charging tRNAs with their cognate amino acids, the elongation factor EF‐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‐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‐growth conditions. Compared to models in which the spatial heterogeneity is ignored, the in vivo models predict an overall increase (decrease) in the repressor‐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‐based, reactiondiffusion model that runs on graphics processing units (GPUs).
References: 1. “Experimental and computational analysis of tRNA dynamics”, R. Alexander, J. Eargle, and Z. Luthey‐Schulten, FEBS Letters, 584 (2), 376‐386 (2010) 2. “The role of L1 stalk: tRNA interaction in the ribosome elongation cycle," L. Trabuco, E. Schreiner, J. Eargle, P. Cornish, T. Ha, Z. Luthey‐Schulten, K. Schulten, J. Mol. Biol. 402, 741‐760 (2010). 3. “Dynamic Networks: Signaling Pathways in Protein/tRNA Complexes” , A. Sethi, J. Eargle, A. Black, and Z. Luthey‐Schulten, Proc. Natl. Acad. Sci. USA, 106, 6620‐6625 (2009) 4. “Long time‐scale simulations of in vivo diffusion using GPU hardware”, E. Roberts, J. Stone, L. Sepulveda, Wen‐mei Hwu, and Z. Luthey‐Schulten, in Proceedings 8th IEEE International Meeting on High Performance Computational Biology, (2009). 5. “Noise contributions in an inducible genetic switch: A whole cell simulation study”, E. Roberts, A. Magis, J. Ortiz, W. Baumeister, and Z. Luthey‐Schulten, Plos Comp. Bio (in press).
*refreshments at 9:45 am
Hosted by Ron Elber