Input graph: the hidden geometry in controlling complex networks

Zhang, Xizhe; Lv, Tianyang; Pu, Yuanyuan

doi:10.1038/srep38209

Cited by 18 publications

(36 citation statements)

References 34 publications

(56 reference statements)

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“…For a network of n nodes, a set of driver nodes for the bipartite representation of the network, N D , is found using a maximum matching algorithm such as Hopcroft-Karp [36]. For all N D , control adjacent nodes were identified iteratively and an input graph was created as dictated in Zhang et al [72]. The input graph was used to classify nodes as critical (in all minimum input sets), neutral (in some minimum input sets), or redundant (in no minimum input sets).…”

Section: Global Controllability Classificationmentioning

confidence: 99%

A dual controllability analysis of influenza virus-host protein-protein interaction networks for antiviral drug target discovery

et al. 2019

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Background Host factors of influenza virus replication are often found in key topological positions within protein-protein interaction networks. This work explores how protein states can be manipulated through controllability analysis: the determination of the minimum manipulation needed to drive the cell system to any desired state. Here, we complete a two-part controllability analysis of two protein networks: a host network representing the healthy cell state and an influenza A virus-host network representing the infected cell state. In this context, controllability analyses aim to identify key regulating host factors of the infected cell’s progression. This knowledge can be utilized in further biological analysis to understand disease dynamics and isolate proteins for study as drug target candidates. Results Both topological and controllability analyses provide evidence of wide-reaching network effects stemming from the addition of viral-host protein interactions. Virus interacting and driver host proteins are significant both topologically and in controllability, therefore playing important roles in cell behavior during infection. Functional analysis finds overlap of results with previous siRNA studies of host factors involved in influenza replication, NF-kB pathway and infection relevance, and roles as interferon regulating genes. 24 proteins are identified as holding regulatory roles specific to the infected cell by measures of topology, controllability, and functional role. These proteins are recommended for further study as potential antiviral drug targets. Conclusions Seasonal outbreaks of influenza A virus are a major cause of illness and death around the world each year with a constant threat of pandemic infection. This research aims to increase the efficiency of antiviral drug target discovery using existing protein-protein interaction data and network analysis methods. These results are beneficial to future studies of influenza virus, both experimental and computational, and provide evidence that the combination of topology and controllability analyses may be valuable for future efforts in drug target discovery. Electronic supplementary material The online version of this article (10.1186/s12859-019-2917-z) contains supplementary material, which is available to authorized users.

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Section: Global Controllability Classificationmentioning

confidence: 99%

A dual controllability analysis of influenza virus-host protein-protein interaction networks for antiviral drug target discovery

et al. 2019

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“…For a network of n nodes, a set of driver nodes for the bipartite representation of the network, 6 , is found using a maximum matching algorithm such as Hopcroft-Karp 38 . For all 6 , control adjacent nodes were identified iteratively and an input graph was created as dictated in Zhang et al 68 . The input graph was used to classify nodes as critical (in all minimum input sets), neutral (in some minimum input sets), or redundant (in no minimum input sets).…”

Section: Jia Classificationmentioning

confidence: 99%

A Dual Controllability Analysis of Influenza Virus-Host Protein-Protein Interaction Networks for Antiviral Drug Target Discovery

Ackerman

Alcorn

Hase

et al. 2018

Preprint

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Host factors of influenza virus replication are often found in key topological positions within protein-protein interaction networks. This work explores how protein states can be manipulated through controllability analysis: the determination of the minimum manipulation needed to drive the cell system to any desired state. Here, we complete a two-part controllability analysis of two protein networks: a host network representing the healthy cell state and an influenza A virus-host network representing the infected cell state. This knowledge can be utilized to understand disease dynamics and isolate proteins for study as drug target candidates. Both topological and controllability analyses provide evidence of wide-reaching network effects stemming from the addition of viral-host protein interactions. Virus interacting and driver host proteins are significant both topologically and in controllability, therefore playing important roles in cell behavior during infection. 24 proteins are identified as holding regulatory roles specific to the infected cell by measures of topology, controllability, and functional role. These proteins are recommended for further study as potential antiviral drug targets. Importance: Seasonal outbreaks of influenza A virus are a major cause of illness and death around the world each year, with a constant threat of pandemic infection. Even so, the FDA has only approved four treatments, two of which are unsuited for at risk groups such as children and those with breathing complications. This research aims to increase the efficiency of antiviral drug target discovery using existing protein-protein interaction data and network analysis methods. Controllability analyses identify key regulating host factors of the infected cell's progression, findings which are supported by biological context. These results are beneficial to future studies of influenza virus, both experimental and computational. calculations: identifying the network components which must be manipulated for the system to be fully controlled (analogous to determining the non-zero elements of the matrix in classic controllability). Without manipulation, driver nodes will remain unaffected by changes to the rest of the system, rendering the total system uncontrollable. A set of driver nodes (size 6 ) that is capable of controlling the total network is called a minimum input set (MIS). The MIS is not unique and the number of possible MISs scales exponentially with the size of the network 33 . After a primary MIS is calculated, two methods of controllability node classification can be used.In the first method by Liu et al. 34 , the MIS is re-calculated (size 6 ′) after removing each node from the network. The node is then classified by its effect on the manipulation required to control the network, where an increase in the size of the MIS makes it more difficult to control the network and a decrease in the size of the MIS makes it easier to control the network. The absence of: an indispensable node increases the number of driver nodes...

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“…For most of the real networks, the maximum matching is usually not unique because of their structural complexity. Therefore, there may exist many MDSs which can fully control the network 16,19 .…”

Section: Introductionmentioning

confidence: 99%

Altering control modes of complex networks based on edge removal

Zhang

2019

Physica A: Statistical Mechanics and its Applications

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Controlling a complex network is of great importance in many applications. The network can be controlled by inputting external control signals through some selected nodes, which are called input nodes. Previous works found that the majority of the nodes in dense networks are either the input nodes or not, which leads to the bimodality in controlling the complex networks. Due to the physical or economic constraints of many real control scenarios, altering the control mode of a network may be critical to many applications. Here we develop a graph-based algorithm to alter the control mode of a network. The main idea is to change the control connectivity of nodes by removing carefully selected edges. We rigorously prove the correctness of our algorithm and evaluate its performance on both synthetic and real networks. The experimental results show that the control mode of a network can be easily changed by removing few selected edges. Our methods provide the ability to design the desired control mode for different control scenarios, which may be useful in many applications.

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Input graph: the hidden geometry in controlling complex networks

Cited by 18 publications

References 34 publications

A dual controllability analysis of influenza virus-host protein-protein interaction networks for antiviral drug target discovery

A dual controllability analysis of influenza virus-host protein-protein interaction networks for antiviral drug target discovery

A Dual Controllability Analysis of Influenza Virus-Host Protein-Protein Interaction Networks for Antiviral Drug Target Discovery

Altering control modes of complex networks based on edge removal

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