Thursday, October 15, 20151:00 PM - 2:00 PMCNLS Conference Room (TA-3, Bldg 1690)|
Characterizing dynamical effects of combinatorial complexity using rule-based modeling
Ryan SudermanCenter for Computational Biology and the University of Kansas
The existence of combinatorial complexity in signaling networks has led to the hypothesis that "ensembles" of heterogenous signaling species could be capable of reliable signal transduction, as an alternative to the formation of well-ordered signaling “machines”. We implemented a rule-based model of the yeast pheromone signaling network, which exhibits considerable combinatorial complexity, in order to both determine if ensemble-like signaling is a viable means of intracellular information transfer and to characterize potential features of ensemble-specific signaling dynamics. We found that these models reproduced the experimentally observed dose-response curves as well as the dynamics of downstream MAPK activation, implying that ensemble signaling is capable of reliable signal transduction. We then focused on the role of the scaffold protein, Ste5, in this network, since scaffolds are of interest for understanding regulatory behavior in signaling and because it is a major source of the combinatorial complexity in this network. With a series of models loosely based on the pheromone network, we systematically characterized the effect of scaffold proteins on the dynamics of both ensemble- and machine-like signaling systems. We found that the molecular context for scaffold-substrate binding can have a profound impact on phenotypically relevant features (e.g. speed of signal transduction, robustness to noise), including inter-pathway crosstalk. More generally, these results show that network complexity must be taken into account in order to fully understand how cells utilize signaling networks to respond appropriately to changes in their environment.
Host: Bill Hlavacek