Identification of critical regulatory genes in cancer signaling network using controllability analysis

Ravindran, Vandana; Sunitha, V.; Bagler, Ganesh

doi:10.1016/j.physa.2017.01.059

Cited by 25 publications

(23 citation statements)

References 29 publications

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“…The aim is to identify the minimum number of inputs, termed ‘driver’ nodes, that can steer the system from any initial state to any final state in finite time. Past studies of network controllability have identified the driver proteins to be associated with human diseases like cancer 22–26 and other molecular interaction networks 27–30 .…”

Section: Introductionmentioning

confidence: 99%

Network controllability analysis of intracellular signalling reveals viruses are actively controlling molecular systems

Ravindran

Nacher

Akutsu

et al. 2019

Sci Rep

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In recent years control theory has been applied to biological systems with the aim of identifying the minimum set of molecular interactions that can drive the network to a required state. However, in an intra-cellular network it is unclear how control can be achieved in practice. To address this limitation we use viral infection, specifically human immunodeficiency virus type 1 (HIV-1) and hepatitis C virus (HCV), as a paradigm to model control of an infected cell. Using a large human signalling network comprised of over 6000 human proteins and more than 34000 directed interactions, we compared two states: normal/uninfected and infected. Our network controllability analysis demonstrates how a virus efficiently brings the dynamically organised host system into its control by mostly targeting existing critical control nodes, requiring fewer nodes than in the uninfected network. The lower number of control nodes is presumably to optimise exploitation of specific sub-systems needed for virus replication and/or involved in the host response to infection. Viral infection of the human system also permits discrimination between available network-control models, which demonstrates that the minimum dominating set (MDS) method better accounts for how the biological information and signals are organised during infection by identifying most viral proteins as critical driver nodes compared to the maximum matching (MM) method. Furthermore, the host driver nodes identified by MDS are distributed throughout the pathways enabling effective control of the cell via the high ‘control centrality’ of the viral and targeted host nodes. Our results demonstrate that control theory gives a more complete and dynamic understanding of virus exploitation of the host system when compared with previous analyses limited to static single-state networks.

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Section: Introductionmentioning

confidence: 99%

Network controllability analysis of intracellular signalling reveals viruses are actively controlling molecular systems

Ravindran

Nacher

Akutsu

et al. 2019

Sci Rep

Self Cite

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“…By using above control type of nodes, many works have been done to investigate the types of nodes in control. For example, input nodes can be used for identifying critical regulatory genes 26 , finding cancer-associated genes 20 , identifying novel disease genes and potential drug targets 9,10,13 .…”

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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“…Current interest stems from the realization that structural controllability is a key property of interest in swarming behavior and in the modeling and understanding complex networks [27]- [35]. For example, identification, characterization, and classification of driver vertices or steering vertices in biomedical networks [36]- [43], which tend This work was supported by National Science Foundation grant n. 1607101.00 and US Air Force grant n. FA9550-16-1-0290.…”

Section: Introductionmentioning

confidence: 99%

A Graphical Characterization of Structurally Controllable Linear Systems With Dependent Parameters

Liu

Morse

2019

IEEE Trans. Automat. Contr.

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One version of the concept of structural controllability defined for single-input systems by Lin and subsequently generalized to multi-input systems by others, states that a parameterized matrix pair (A, B) whose nonzero entries are distinct parameters, is structurally controllable if values can be assigned to the parameters which cause the resulting matrix pair to be controllable. In this paper the concept of structural controllability is broadened to allow for the possibility that a parameter may appear in more than one location in the pair (A, B). Subject to a certain condition on the parameterization called the "binary assumption", an explicit graph-theoretic characterization of such matrix pairs is derived.

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Identification of critical regulatory genes in cancer signaling network using controllability analysis

Cited by 25 publications

References 29 publications

Network controllability analysis of intracellular signalling reveals viruses are actively controlling molecular systems

Network controllability analysis of intracellular signalling reveals viruses are actively controlling molecular systems

Altering control modes of complex networks based on edge removal

A Graphical Characterization of Structurally Controllable Linear Systems With Dependent Parameters

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