Fig. S7.

Accuracy of the MIC inference method depends on both network topology and average node degree. (A and B) Representative simulations capturing the TTX-mediated resynchronization for the 3-state and 11-state models, respectively. Next, MIC network inference was performed for simulated networks of three topologies: (C) random, (D), linear nearest neighbor, and (E) Watts–Strogatz small world ($\text{β}=0.05$). These networks are only shown for visualization of network structure. Simulated networks each contained 400 cells to match SCN recordings, and the average AUC was calculated for five dynamically generated networks of each node degree. The area under the ROC curve is plotted as a function of average node degree for the 3-state circadian model (33, 34) (F) and the 11-state circadian model (35) (G) for these network structures. We show that both node degree and network structure affect the accuracy of this method. In general, networks that have a shorter average path length and higher average node degree are more difficult to accurately infer, as it becomes increasingly difficult to differentiate between direct and indirect physical connections.