Reference : An efficient approach towards the source-target control of Boolean networks
Scientific journals : Article
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/40583
An efficient approach towards the source-target control of Boolean networks
English
Paul, Soumya mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Su, Cui mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Pang, Jun mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Mizera, Andrzej mailto [Polish Academy of Sciences > Institute of Computer Science]
In press
IEEE/ACM Transactions on Computational Biology and Bioinformatics
IEEE Computer Society
Yes
1545-5963
1557-9964
New-York
NY
[en] Boolean networks ; attractors ; network control
[en] We study the problem of computing a minimal subset of nodes of a given asynchronous Boolean network that need to be perturbed in a single-step to drive its dynamics from an initial state to a target steady state (or attractor), which we call the source-target control of Boolean networks. Due to the phenomenon of state-space explosion, a simple global approach that performs computations on the entire network, may not scale well for large networks. We believe that efficient algorithms for such networks must exploit the structure of the networks together with their dynamics. Taking this view, we derive a decomposition-based solution to the minimal source-target control problem which can be significantly faster than the existing approaches on large networks. We then show that the solution can be further optimised if we take into account appropriate information about the source state. We apply our solutions to both real-life biological networks and randomly generated networks, demonstrating the efficiency and efficacy of our approach.
SnT, ANR-FNR
SEC-PBN, AlgoReCell
http://hdl.handle.net/10993/40583
10.1109/TCBB.2019.2915081
https://ieeexplore.ieee.org/abstract/document/8716547
FnR ; FNR11191283 > Thomas Sauter > AlgoReCell > Computational Models and Algorithms for Predicting Cell Reprogramming Determinants with High Efficiency and High Fidelity > 01/03/2017 > 29/02/2020 > 2016

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