Integrated algorithms for the network control problem
### 1. Introduction Based on the open-source platform Cytoscape, a convenient app called CytoCtrlAnalyser for network controllability analysis has been designed and achieved.This app supports nine algorithms in network controllability: Firstly, five algorithms are implemented to identify the nodes which should be controlled by input control signals to achieve different control objectives: the minimum driver node set (MDS) [1], the minimum steering node set (MSS) [2], the MSS with preference [3, 4], the steering nodes for output control [5] and the steering node for state transition [6]. Secondly, four algorithms are implemented to calculate nodes importance in controlling the network in different aspects: control capacity [7], control centrality [8], node classification [9] and the probability of a node in a randomly chosen MSS [4]. ### 2. Quick Start Following is a short quick start for the usage of CytoCtrlAnalyser, detailed information of the algorithms implemented in this app will be introduced in related papers. 1. Download and install Cytoscape3.0 []. 2. Download the Jar version of CytoCtrlAnalyser. 3. Start Cytoscape, install the CytoCtrlAnalyser.jar through App Manager(Apps → App Manager → Install from file..). 4. Import or open a network. 5. Click **Apps** → **CytoCtrlAnalyser**→ **Start**. 6. It will switch to CytoCtrlAnalyser tab on left panel, choose one or multiple algorithms as you want. 7. Click **Analysis** button. 8. After analysis is done, results will be shown on the table panel. The nodes should be controlled are marked by check symbols in the table. For the node classification: 0, 1 and 2 correspond to dispensable, neutral and indispensable, respectively. ### 3. References 1. Yang-YuLiu, Jean-JacquesSlotine, and Albert-LászlóBarabási. Controllability of complex networks. Nature, 473:167–173, 2011. 2. Lin Wu, Min Li, Jianxin Wang, and Fang-Xiang Wu. Minimum steering node set of complex networks and its applications to biomolecular networks. IET systems biology, 10(3):116–123, 2016. 3. Lin Wu, Lingkai Tang, Min Li, Jianxin Wang, and Fang-Xiang Wu. The MSS of complex networks with centrality based preference and its application to biomolecular networks. pp. 229–234, 2016. 4. Lin Wu, Lingkai Tang, Min Li, Jianxin Wang, and Fang-Xiang Wu. Biomolecular network controllability with drug binding information. NanoBioscience, IEEE Transactions on, Accepted, 2017. 5. Lin Wu, Yichao Shen, Min Li, and Fang-Xiang Wu. Drug target identification based on structural output controllability ofcomplex networks. In Bioinformatics Research and Applications, pp. 188–199. Springer, 2014. 6. Fang-Xiang Wu, Lin Wu, Jianxin Wang, Juan Liu, and Luonan Chen. Transittability of complex networks and its applications to regulatory biomolecular networks. Sci. Rep., 4, 2014. 7. Tao Jia and A. Barabási. Control capacity and a random sampling method in exploring controllability of complex networks. Sci. Rep., 3:2354, 2013. 8. Yang-Yu Liu, Jean-Jacques Slotine, and Albert-László Barabási. Control centrality and hierarchical structure in complex networks. PLoS One, 7(9):e44459, 2012. 9. Arunachalam Vinayagam, Travis E Gibson, Ho-Joon Lee, Bahar Yilmazel, Charles Roesel, Yanhui Hu, Young Kwon, Amitabh Sharma, Yang-Yu Liu, Norbert Perrimon, et al. Controllability analysis of the directed human protein interaction network identifies disease genes and drug targets. PNAS, 113(18):4976–4981, 2016. ###Related Tools Our group also developed several other related tools: [ CytoNCA] Providing calculation, evaluation and visualization analysis for several centralities of weighted and unweighted network. [ CytoCluster] a Cytoscape app for analysis and visualization of clusters from network [ ClusterViz] Clustering based on FAG-EC, EAGLE or MCODE. Found cluster can be subjected to GO enrichment analysis. [ DyNetViewer] Constructing, analyzing, and visualizing dynamic networks.


Works with Cytoscape 3.0


Version 1.0.0

Released 25 May 2017

Works with Cytoscape 3.0

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