The mechanism of MinD stability modulation by MinE in Min protein dynamics

The patterns formed both in vivo and in vitro by the Min protein system have attracted much interest because of the complexity of their dynamic interactions given the apparent simplicity of the component parts. Despite both the experimental and theoretical attention paid to this system, the details of the biochemical interactions of MinD and MinE, the proteins responsible for the patterning, are still unclear. For example, no model consistent with the known biochemistry has yet accounted for the observed dual role of MinE in the membrane stability of MinD. Until now, a statistical comparison of models to the time course of Min protein concentrations on the membrane has not been carried out. Such an approach is a powerful way to test existing and novel models that are difficult to test using a purely experimental approach. Here, we extract time series from previously published fluorescence microscopy time lapse images of in vitro experiments and fit two previously described and one novel mathematical model to the data. We find that the novel model, which we call the Asymmetric Activation with Bridged Stability Model, fits the time-course data best. It is also consistent with known biochemistry and explains the dual MinE role via MinE-dependent membrane stability that transitions under the influence of rising MinE to membrane instability with positive feedback. Our results reveal a more complex network of interactions between MinD and MinE underlying Min-system dynamics than previously considered.Author summary: The Min protein system in E. coli is important both for its functional role in cell division and as a relatively simple biochemical network that demonstrates remarkable and diverse spatiotemporal patterning both in vivo and in vitro. There is a rich history of mathematical modeling in the field but the focus has been largely qualitative, and some experimental observations have remained beyond the explanatory scope of proposed mechanisms. Here we fit models to quantitative time-course data from in vitro experiments to test various hypothesis that would be difficult to test experimentally. We propose a new model that explains the data best, is consistent with experimental observations, and points to a two-phase stability-switching process underlying patterning.