Identifying synergistic control targets of a biological network based on a merged state transition map.

Abstract

Biological networks are complicatedly wired and therefore we often need more than one control targets to change their attractor state into a desired one. Although various control target selection methods, such as feedback vertex sets (FVS) or minimum dominating sets (MDS) were suggested, the resulting control targets do not indicate synergistic drug targets. The main reason is that such methods are using only the information of network topology. It is true that the steady state or the controllability of a network heavily depends on the network topology, but the synergistic effects are primarily caused by complex network dynamics which are not solely determined by the network topology. Thus, we need to develop a new control strategy that can identify useful synergistic control target pairs based on both topology and dynamics of the network. For this purpose, we have developed a novel method of identifying synergistic control targets by merging the state transition maps before and after virtual perturbations of the network nodes. We also developed a scoring algorithm to evaluate the synergistic effect of each node perturbation. The proposed method compares the average activities and state alteration numbers of network nodes between the desired direction and the opposite direction of the state transition flow in the merged transition map. The scores are weighted based on the phenotypic information according to the attractor classification criteria of the network. We applied the proposed method to published Boolean network models of biological networks and confirmed that our method can identify synergistic drug targets. In addition, by visualizing the merged state transition map and analyzing the state transition flow upon it, we can further reveal the hidden mechanisms of the identified synergistic drug pairs.

Acknowledgements: This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea Government, the Ministry of Science, ICT & Future Planning (2015M3A9A7067220, 2014R1A2A1A10052404, and 2013M3A9A7046303). It was also supported by the KAIST Grand Challenge 30 Project grant.

Keywords: Merged state transition map, Synergistic control targets, Phenotypic attractors, Boolean network, Network control