Zhao, Y, Kim, J and Filippone, M (2013) Aggregation algorithm towards large-scale Boolean network analysis. IEEE Transactions on Automatic Control, 58 (8). 8. 1976 - 1985. ISSN 0018-9286
Abstract
The analysis of large-scale Boolean network dynamics is of great importance in understanding complex phenomena where systems are characterized by a large number of components. The computational cost to reveal the number of attractors and the period of each attractor increases exponentially as the number of nodes in the networks increases. This paper presents an efficient algorithm to find attractors for medium to large-scale networks. This is achieved by analyzing subnetworks within the network in a way that allows to reveal the attractors of the full network with little computational cost. In particular, for each subnetwork modeled as a Boolean control network, the input-state cycles are found and they are composed to reveal the attractors of the full network. The proposed algorithm reduces the computational cost significantly, especially in finding attractors of short period, or any periods if the aggregation network is acyclic. Also, this paper shows that finding the best acyclic aggregation is equivalent to finding the strongly connected components of the network graph. Finally, the efficiency of the algorithm is demonstrated on two biological systems, namely a T-cell receptor network and an early flower development network.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2013 IEEE. This is an author produced version of a paper published in IEEE Transactions on Automatic Control. Uploaded in accordance with the publisher's self-archiving policy. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works |
Keywords: | Boolean functions; computational complexity; network theory (graphs; Boolean control network; T-cell receptor network; aggregation network; attractors; best acyclic aggregation; biological systems; computational complexity; early flower development network; input-state cycles; large-scale Boolean network dynamics analysis; network graph; subnetwork analysis; Algorithm design and analysis; Biological system modeling; Complexity theory; Computational efficiency; Computational modeling; Steady-state; Acyclic aggregation; Boolean network; attractor; graph aggregation |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 23 Oct 2014 14:15 |
Last Modified: | 05 Feb 2018 23:42 |
Published Version: | http://dx.doi.org/10.1109/TAC.2013.2251819 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/TAC.2013.2251819 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:80732 |