CABEAN: a software for the control of asynchronous Boolean networks

Su, Cui; Pang, Jun

doi:10.1093/bioinformatics/btaa752

Cited by 23 publications

(19 citation statements)

References 10 publications

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“…We discuss the results of the myeloid differentiation network and the cardiac gene regulatory network in detail and give an overview of the results of the other networks. Our target control methods are implemented in our software CABEAN [39], which contains a realisation of the decompositionbased detection method [40,41] for identifying attractors in Boolean networks efficiently. 1 SMC 2 is implemented in Java.…”

Section: Discussionmentioning

confidence: 99%

Target Control of Asynchronous Boolean Networks

Su¹,

Pang

2021

Preprint

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We study the target control of asynchronous Boolean networks, to identify efficacious interventions that can drive the dynamics of a given Boolean network from any initial state to the desired target attractor. Based on the application time, the control can be realised with three types of perturbations, including instantaneous, temporary and permanent perturbations. We develop efficient methods to compute the target control for a given target attractor with three types of perturbations. We compare our methods with the stable motif-based control on a variety of real-life biological networks to evaluate their performance.We show that our methods scale well for large Boolean networks and they are able to identify a rich set of solutions with a small number of perturbations.

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Section: Discussionmentioning

confidence: 99%

Target Control of Asynchronous Boolean Networks

Su¹,

Pang

2021

Preprint

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“…-Basins approach (BA), attractor control method that uses the basin of attraction of the target attractor to identify control strategies as implemented in [18].…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

“…We compare our model checking approach (MC) to two methods for attractor control: stable motifs (SM) [21] and basins of attraction (BA) [18]. SM works for steady states and the complex attractors captured by the stable motifs (in some cases, complex attractors are not identified and the method cannot be applied).…”

Section: Attractor Controlmentioning

confidence: 99%

“…These approaches are usually focused on targeting an attractor or subspace and they also do not generally uncover all possible minimal control strategies. In order to bridge this gap, recent works have tackled the problem of attractor control by using basins of attraction, sets of states from which only a specific attractor is reachable [18]. Such approaches increase in many cases the amount of strategies identified.…”

Section: Introductionmentioning

confidence: 99%

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Control in Boolean networks with model checking

Cifuentes-Fontanals¹,

Tonello²,

Siebert³

2021

Preprint

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Understanding control mechanisms in biological systems plays a crucial role in important applications, for instance in cell reprogramming. Boolean modeling allows the identification of possible efficient strategies, helping to reduce the usually high and time-consuming experimental efforts. Available approaches to control strategy identification usually focus either on attractor or phenotype control, and are unable to deal with more complex control problems, for instance phenotype avoidance. They also fail to capture, in many situations, all possible minimal strategies, finding instead only sub-optimal solutions. In order to fill these gaps, we present a novel approach to control strategy identification in Boolean networks based on model checking. The method is guaranteed to identify all minimal control strategies, and provides maximal flexibility in the definition of the control target. We investigate the applicability of the approach by considering a range of control problems for different biological systems, comparing the results, where possible, to those obtained by alternative control methods.

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“…NetControl4BioMed allows multi-user collaborative access to building directed protein–protein interaction networks around a set of seed proteins of interest, to upload such networks from external platforms, and to perform structural target controllability analyses with a focus on drug combination identification. Several approaches exist for Boolean network controllability ( Biane et al , 2019 ; Lin et al , 2012 ; Murrugarra et al , 2016 ; Su et al , 2021 ; Zañudo et al , 2015 ). For structural target controllability the only other tool that we are aware of is the Cytoscape app CytoCtrlAnalyser ( Wu et al , 2018 ).…”

Section: Introductionmentioning

confidence: 99%

NetControl4BioMed: a web-based platform for controllability analysis of protein–protein interaction networks

et al. 2021

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Motivation There is an increasing amount of data coming from genome-wide studies identifying disease-specific survivability-essential proteins and host factors critical to a cell becoming infected. Targeting such proteins has a strong potential for targeted, precision therapies. Typically however, too few of them are drug targetable. An alternative approach is to influence them through drug targetable proteins upstream of them. Structural target network controllability is a suitable solution to this problem. It aims to discover suitable source nodes (e.g., drug targetable proteins) in a directed interaction network that can control (through a suitable set of input functions) a desired set of targets. Results We introduce NetControl4BioMed, a free open-source web-based application that allows users to generate or upload directed protein-protein interaction networks and to perform target structural network controllability analyses on them. The analyses can be customized to focus the search on drug targetable source nodes, thus providing drug therapeutic suggestions. The application integrates protein data from HGNC, Ensemble, UniProt, NCBI, and InnateDB, directed interaction data from InnateDB, Omnipath, and SIGNOR, cell-line data from COLT and DepMap, and drug-target data from DrugBank. Availability The application and data are available online at https://netcontrol.combio.org/. The source code is available at https://github.com/Vilksar/NetControl4BioMed under an MIT license.

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CABEAN: a software for the control of asynchronous Boolean networks

Cited by 23 publications

References 10 publications

Target Control of Asynchronous Boolean Networks

Target Control of Asynchronous Boolean Networks

Control in Boolean networks with model checking

NetControl4BioMed: a web-based platform for controllability analysis of protein–protein interaction networks

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