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ERIC DEEDS Harvard University |
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Large multicomponent protein complexes, such as the ribosome and proteasome, are crucial for cellular function. My work focuses on building computational models of how such structures assemble. Rings represent an important class of structural motifs; they can display remarkable thermodynamic stability that causes the overall assembly reaction to approach completion. I will first discuss dynamic phenomena that I can occur in the assembly of rings from monomeric components. When bonds between monomers are too strong relative to a given monomer concentration, the process of assembly enters a “gridlocked” phase during which the fraction of fully formed ring structures remains considerably below 100%. For any concentration of monomers, there is a strength that optimizes the rate of assembly. Rings that include at least one weak bond, however, display optimum assembly dynamics across a wide range of concentrations. Estimating affinities from solved protein structures of ring-like structures, I find that rings universally contain at least one significantly weak bond. Having examined the dynamics of assembly a fairly simple case, I will discuss complex formation in a large protein interaction network that is combinatorially complex, in the sense that it can generate astronomical numbers of possible molecular species. I will describe a recently developed rule- and agent-based modeling technique with which I simulated the network dynamics of such a network derived from curated yeast two-hybrid data. The combinatorial complexity of this network engenders "compositional drift" in the space of molecular possibilities. To produce large complexes that assemble reliably into well-defined, stable structures, cells have had to evolve mechanisms that constrain and eliminate compositional drift. |
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4:15pm B17 Upson Hall Thursday, February 11, 2010 Refreshments at 3:45pm in the Upson 4th Floor Atrium |
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Computer Science Colloquium Spring 2010 |
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www.cs.cornell.edu/events/colloquium |
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The Dynamics of Assembly in Biological Networks |