Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.09.30.320259v1?rss=1 Authors: Kühlwein, S. D., Ikonomi, N., Schwab, J. D., Kraus, J. M., Rudolph, K. L., Pfister, A., Schuler, R., Kühl, M., Kestler, H. A. Abstract: Biological processes are rarely a consequence of single protein interactions but rather of complex regulatory networks. However, interaction graphs cannot adequately capture temporal changes. Among models that investigate dynamics, Boolean network models can approximate simple features of interaction graphs integrating also dynamics. Nevertheless, dynamic analyses are time-consuming and with growing number of nodes may become infeasible. Therefore, we set up a method to identify minimal sets of nodes able to determine network dynamics. This approach is able to depict dynamics without calculating exhaustively the complete network dynamics. Applying it to a variety of biological networks, we identified small sets of nodes sufficient to determine the dynamic behavior of the whole system. Further characterization of these sets showed that the majority of dynamic decision-makers were not static hubs. Our work suggests a paradigm shift unraveling a new class of nodes different from static hubs and able to determine network dynamics. Copy rights belong to original authors. Visit the link for more info