Network Output Controllability-Based Method for Drug Target Identification

Wu, Lin; Shen, Yichao; Li, Min; Wu, Fang-Xiang

doi:10.1109/tnb.2015.2391175

Cited by 34 publications

(26 citation statements)

References 41 publications

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“…Overall, these findings will improve our understanding of the control principles of complex networks and may be useful in controlling various real complex systems, such as drug designs232425, financial markets1626 and biological networks1822.…”

Section: Discussionmentioning

confidence: 84%

Input graph: the hidden geometry in controlling complex networks

Zhang

2016

Sci Rep

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The ability to control a complex network towards a desired behavior relies on our understanding of the complex nature of these social and technological networks. The existence of numerous control schemes in a network promotes us to wonder: what is the underlying relationship of all possible input nodes? Here we introduce input graph, a simple geometry that reveals the complex relationship between all control schemes and input nodes. We prove that the node adjacent to an input node in the input graph will appear in another control scheme, and the connected nodes in input graph have the same type in control, which they are either all possible input nodes or not. Furthermore, we find that the giant components emerge in the input graphs of many real networks, which provides a clear topological explanation of bifurcation phenomenon emerging in dense networks and promotes us to design an efficient method to alter the node type in control. The findings provide an insight into control principles of complex networks and offer a general mechanism to design a suitable control scheme for different purposes.Controlling complex networked systems is a fundamental challenge in natural, social sciences and engineered systems. A networked system is controllable if its state can be controlled from any initial state to a desired accessible state 1,2 by inputting external signals from a few suitable selected nodes, which are called input nodes 3-6 . Existing works 3 provide an efficient method based on maximum matching to find a Minimum Input nodes Set (abbreviated MIS) used to fully control a network.However, these works have primarily focused on analyzing single MIS 4-8 , while the underlying control relationships of nodes and MISs remain elusive. Owing to the structural complexity of a network, its MISs are typically not unique and the number of MISs are exponential to the size of the network 9,10 . The enumeration of all possible MISs is a #P problem 11 which requires high computational costs. A few works analyzed the node types in control 12,13 and control capacities 10 of input nodes. Moreover, although any of its MISs are capable of fully controlling the network, they may composed of nodes with different topological properties, such as high-degree nodes 14 . The existence of physical constraints and limitations 15 may also affect the choice of a suitable MIS. For example, when controlling an inter-bank market 16,17 , one may need certain specific input nodes to ensure that a MIS can be manipulated by a given organization; when controlling a protein interaction network 18 , some proteins cannot be used as input nodes because of technique limitation.Given the existence of numerous MISs in a network, a node can be classified based on its participation in MISs 12 : 1. possible input node, which appear in at least one MIS; 2. redundant node, which never appear in any MIS. Previous works 12 found that the dense networks exhibit a surprising bifurcation phenomenon, in which the majority of nodes are either redundant nodes or possibl...

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Section: Discussionmentioning

confidence: 84%

Input graph: the hidden geometry in controlling complex networks

Zhang

2016

Sci Rep

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“…To the best of our knowledge, however, the existing results considered target controllability under structural (strongly structurally) controllability framework. Thus, the results inevitably ignored the effects of link weights on target controllability . On the other hand, the existing results of target controllability are only pertinent to the fixed interaction topology while switching topology is left largely unconsidered …”

Section: Introductionmentioning

confidence: 99%

“…Examples include the target controllability of complex networks, 34,35 strong target controllability of dynamical networks, 36,37 structural target controllability of undirected networks, 38 and target controllability of biomolecular networks. 39 To the best of our knowledge, however, the existing results considered target controllability under structural (strongly structurally) controllability framework. Thus, the results inevitably ignored the effects of link weights on target controllability.…”

Section: Introductionmentioning

confidence: 99%

“…34,35,[37][38][39] On the other hand, the existing results of target controllability are only pertinent to the fixed interaction topology while switching topology is left largely unconsidered. [34][35][36][37][38][39] In this paper, we study the target controllability of MASs under fixed and switching topologies. For fixed interaction topology, we provide some necessary and/or sufficient algebraic and graph-theoretic conditions for target controllability and analyze the effects of the directed and weighted interaction topology on target controllability.…”

Section: Introductionmentioning

confidence: 99%

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Target controllability of multiagent systems under fixed and switching topologies

Guan

Wang

2019

Intl J Robust & Nonlinear

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Summary In this article, the target controllability of multiagent systems under fixed and switching topologies is investigated, respectively. In the fixed topology setting, some necessary and/or sufficient algebraic and graph‐theoretic conditions are proposed, and the target controllable subspace is quantitatively studied by virtue of almost equitable graph vertex partitions. In the switching topology setting, based on the concepts of the invariant subspace and the target controllable state set, some necessary and sufficient algebraic conditions are obtained. Moreover, the target controllability is studied from the union graph perspective. The results show that when the union graph of all the possible topologies is target controllable, the multiagent system would be target controllable even if each of its subsystems is not. Numerical simulations are provided finally to verify the effectiveness of the theoretical results.

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“…Therefore, it is an important issue in reducing these expenses in drug discovery. The computational methods provide an effective strategy to address this issue [2].…”

Section: Introductionmentioning

confidence: 99%