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Modeling of Biomolecular Systems Interactions, Dynamics, and Allostery Session III Abstracts
Regulation of Protein-Protein Binding and Pathway Crosstalk
Anna Panchenko
.
National Institutes of Health, Bethesda, MD, USA.
Phosphorylation offers a dynamic way to regulate protein activity and subcellular localization,
which is achieved through reversibility and fast kinetics of posttranslational modifications.
Adding or removing a dianionic phosphate group on a protein often changes protein’s structural
properties, its stability and dynamics. We estimate the effect of phosphorylation on protein
binding and function for different types of complexes from human proteome. We find that
phosphorylation sites tend to be located on protein-protein binding interfaces and may
orthosterically modulate the strength of interactions. We study the effect of phosphorylation on
protein-protein binding in relation to intrinsic disorder and observe the coupling between
phosphorylation events and protein-protein binding through disorder-order or order-disorder
transitions. Finally we investigate how different phosphorylation patterns may mediate dynamic
regulation of cellular processes and may provide the biological cross-talk between different
biochemical pathways.
Stochastic Simulations of Cellular Processes: from Single Cells to Colonies
Zaida Luthey-Schulten.
University of Illinois at Urbana-Champaign, Urbana, IL, USA
High-performance computing now allows integration of data from structural, single-molecule,
and biochemical studies into coherent computational models of cells and cellular processes under
in vivo
conditions. Here we analyze the stochastic reaction-diffusion dynamics of a genetic
switch, ribosome assembly, and metabolic responses of
Escherichia coli
cells. Using our GPU
based Lattice Microbe software, we simulate the dynamics for an entire cell cycle and compare
the mRNA/protein distributions to those observed in single molecule experiments. We show
how such distributions can be integrated into a flux balance analysis of genome scale models of
metabolic networks. The distribution of growth rates calculated for a colony of bacteria are
analyzed and correlated to changes in fluxes through the metabolic network for various
subpopulations. Finally, reaction-diffusion kinetics of the surrounding medium are coupled with
the cellular metabolic networks to demonstrate how small colonies of interacting bacterial cells
differentially respond to the competition for resources according to their position in the colony.
