Working group: "Computational Biology"

2012, the 15th of February

Eric Deeds

Title: The dynamics of assembly in biological networks.


Many cellular processes rely on the activity of large, multiprotein complexes. In many cases, these complexes represent large "macromolecular machines," like the ribosome and the proteasome, which adopt a well-defined quaternary structure. Over the past decade, however, it has become clear that complex formation occurs in the context of vast protein interaction networks in cells. These networks are combinatorially complex, in the sense that they can generate astronomical numbers of possible molecular species. We employed a recently developed rule- and agent-based modeling technique to simulate the dynamics of a large network derived from curated yeast two-hybrid data. Our results indicate that the combinatorial complexity of this network engenders "drift" in the space of molecular possibilities. We have recently extended this work to consider a rule-based model of pheromone signaling in yeast cells, and we have found that these simulations exhibit similar levels of drift. These findings suggest that signal transduction and processing may occur not via the formation of well-defined macromolecular machines, but rather through "pleiomorphic ensembles" of signaling complexes. Given that the formation of stable and well-defined molecular machines requires the evolution of mechanisms that control or constrain drift in the network, the ability to reliably process external cues using heterogeneous ensembles of complexes may enhance the evolutionary plasticity of signaling systems.