Dataset Information

Stepwise target controllability identifies dysregulations of macrophage networks in multiple sclerosis.

ABSTRACT: Identifying the nodes able to drive the state of a network is crucial to understand, and eventually control, biological systems. Despite recent advances, such identification remains difficult because of the huge number of equivalent controllable configurations, even in relatively simple networks. Based on the evidence that in many applications it is essential to test the ability of individual nodes to control a specific target subset, we develop a fast and principled method to identify controllable driver-target configurations in sparse and directed networks. We demonstrate our approach on simulated networks and experimental gene networks to characterize macrophage dysregulation in human subjects with multiple sclerosis.

SUBMITTER: Bassignana G

PROVIDER: S-EPMC8233109 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Stepwise target controllability identifies dysregulations of macrophage networks in multiple sclerosis.

Bassignana Giulia G Fransson Jennifer J Henry Vincent V Colliot Olivier O Zujovic Violetta V De Vico Fallani Fabrizio F

Network neuroscience (Cambridge, Mass.) 20210427 2

Identifying the nodes able to drive the state of a network is crucial to understand, and eventually control, biological systems. Despite recent advances, such identification remains difficult because of the huge number of equivalent controllable configurations, even in relatively simple networks. Based on the evidence that in many applications it is essential to test the ability of individual nodes to control a specific target subset, we develop a fast and principled method to identify controlla ...[more]

PMID: 34189368

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Project description:MotivationThe concept of controllability within complex networks is pivotal in determining the minimal set of driver vertices required for the exertion of external signals, thereby enabling control over the entire network's vertices. Target controllability further refines this concept by focusing on a subset of vertices within the network as the specific targets for control, both of which are known to be NP-hard problems. Crucially, the effectiveness of the driver set in achieving control of the network is contingent upon satisfying a specific rank condition, as introduced by Kalman. On the other hand, structural controllability provides a complementary approach to understanding network control, emphasizing the identification of driver vertices based on the network's structural properties. However, in structural controllability approaches, the Kalman condition may not always be satisfied.ResultsIn this study, we address the challenge of target controllability by proposing a feed-forward greedy algorithm designed to efficiently handle large networks while meeting the Kalman controllability rank condition. We further enhance our method's efficacy by integrating it with Barabasi et al.'s structural controllability approach. This integration allows for a more comprehensive control strategy, leveraging both the dynamical requirements specified by Kalman's rank condition and the structural properties of the network. Empirical evaluation across various network topologies demonstrates the superior performance of our algorithms compared to existing methods, consistently requiring fewer driver vertices for effective control. Additionally, our method's application to protein-protein interaction networks associated with breast cancer reveals potential drug repurposing candidates, underscoring its biomedical relevance. This study highlights the importance of addressing both structural and dynamical aspects of network controllability for advancing control strategies in complex systems.Availability and implementationThe source code is available for free at:Https://github.com/fatemeKhezry/targetControllability.

Dataset Information

Stepwise target controllability identifies dysregulations of macrophage networks in multiple sclerosis.

Publications

Stepwise target controllability identifies dysregulations of macrophage networks in multiple sclerosis.

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