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Mushthofa M, Torres G, Van de Peer Y, Marchal K, De Cock M. ASP-G: an ASP-based method for finding attractors in genetic regulatory networks. Bioinformatics 2014; 30:3086-92. [PMID: 25028722 DOI: 10.1093/bioinformatics/btu481] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Boolean network models are suitable to simulate GRNs in the absence of detailed kinetic information. However, reducing the biological reality implies making assumptions on how genes interact (interaction rules) and how their state is updated during the simulation (update scheme). The exact choice of the assumptions largely determines the outcome of the simulations. In most cases, however, the biologically correct assumptions are unknown. An ideal simulation thus implies testing different rules and schemes to determine those that best capture an observed biological phenomenon. This is not trivial because most current methods to simulate Boolean network models of GRNs and to compute their attractors impose specific assumptions that cannot be easily altered, as they are built into the system. RESULTS To allow for a more flexible simulation framework, we developed ASP-G. We show the correctness of ASP-G in simulating Boolean network models and obtaining attractors under different assumptions by successfully recapitulating the detection of attractors of previously published studies. We also provide an example of how performing simulation of network models under different settings help determine the assumptions under which a certain conclusion holds. The main added value of ASP-G is in its modularity and declarativity, making it more flexible and less error-prone than traditional approaches. The declarative nature of ASP-G comes at the expense of being slower than the more dedicated systems but still achieves a good efficiency with respect to computational time. AVAILABILITY AND IMPLEMENTATION The source code of ASP-G is available at http://bioinformatics.intec.ugent.be/kmarchal/Supplementary_Information_Musthofa_2014/asp-g.zip.
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Affiliation(s)
- Mushthofa Mushthofa
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven, Belgium, Department of Information Technology, IMinds, Ghent University, Gaston Crommenlaan 8, B-9050 Ghent, Belgium and Center for Web and Data Science, Institute of Technology, University of Washington Tacoma, 1900 Commerce Street, Tacoma, WA-98402, USA
| | - Gustavo Torres
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven, Belgium, Department of Information Technology, IMinds, Ghent University, Gaston Crommenlaan 8, B-9050 Ghent, Belgium and Center for Web and Data Science, Institute of Technology, University of Washington Tacoma, 1900 Commerce Street, Tacoma, WA-98402, USA
| | - Yves Van de Peer
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven, Belgium, Department of Information Technology, IMinds, Ghent University, Gaston Crommenlaan 8, B-9050 Ghent, Belgium and Center for Web and Data Science, Institute of Technology, University of Washington Tacoma, 1900 Commerce Street, Tacoma, WA-98402, USA Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven, Belgium, Department of Information Technology, IMinds, Ghent University, Gaston Crommenlaan 8, B-9050 Ghent, Belgium and Center for Web and Data Science, Institute of Technology, University of Washington Tacoma, 1900 Commerce Street, Tacoma, WA-98402, USA Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven
| | - Kathleen Marchal
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven, Belgium, Department of Information Technology, IMinds, Ghent University, Gaston Crommenlaan 8, B-9050 Ghent, Belgium and Center for Web and Data Science, Institute of Technology, University of Washington Tacoma, 1900 Commerce Street, Tacoma, WA-98402, USA Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven, Belgium, Department of Information Technology, IMinds, Ghent University, Gaston Crommenlaan 8, B-9050 Ghent, Belgium and Center for Web and Data Science, Institute of Technology, University of Washington Tacoma, 1900 Commerce Street, Tacoma, WA-98402, USA Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven
| | - Martine De Cock
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven, Belgium, Department of Information Technology, IMinds, Ghent University, Gaston Crommenlaan 8, B-9050 Ghent, Belgium and Center for Web and Data Science, Institute of Technology, University of Washington Tacoma, 1900 Commerce Street, Tacoma, WA-98402, USA Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven, Belgium, Department of Information Technology, IMinds, Ghent University, Gaston Crommenlaan 8, B-9050 Ghent, Belgium and Center for Web and Data Science, Institute of Technology, University of Washington Tacoma, 1900 Commerce Street, Tacoma, WA-98402, USA
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Zheng D, Yang G, Li X, Wang Z, Liu F, He L. An efficient algorithm for computing attractors of synchronous and asynchronous Boolean networks. PLoS One 2013; 8:e60593. [PMID: 23585840 PMCID: PMC3621871 DOI: 10.1371/journal.pone.0060593] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2012] [Accepted: 02/28/2013] [Indexed: 01/30/2023] Open
Abstract
Biological networks, such as genetic regulatory networks, often contain positive and negative feedback loops that settle down to dynamically stable patterns. Identifying these patterns, the so-called attractors, can provide important insights for biologists to understand the molecular mechanisms underlying many coordinated cellular processes such as cellular division, differentiation, and homeostasis. Both synchronous and asynchronous Boolean networks have been used to simulate genetic regulatory networks and identify their attractors. The common methods of computing attractors are that start with a randomly selected initial state and finish with exhaustive search of the state space of a network. However, the time complexity of these methods grows exponentially with respect to the number and length of attractors. Here, we build two algorithms to achieve the computation of attractors in synchronous and asynchronous Boolean networks. For the synchronous scenario, combing with iterative methods and reduced order binary decision diagrams (ROBDD), we propose an improved algorithm to compute attractors. For another algorithm, the attractors of synchronous Boolean networks are utilized in asynchronous Boolean translation functions to derive attractors of asynchronous scenario. The proposed algorithms are implemented in a procedure called geneFAtt. Compared to existing tools such as genYsis, geneFAtt is significantly faster in computing attractors for empirical experimental systems.
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Affiliation(s)
- Desheng Zheng
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
- Departmnent of Electronic Engineering, University of California Los Angeles, Los Angeles, California, United States of America
- * E-mail: (DZ); (GY)
| | - Guowu Yang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
- * E-mail: (DZ); (GY)
| | - Xiaoyu Li
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
- Departmnent of Electronic Engineering, University of California Los Angeles, Los Angeles, California, United States of America
| | - Zhicai Wang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Feng Liu
- Department of Pathology and Laboratory Medicine, David Geffen University of Califonia Los Angeles School of Medicine, Los Angeles, California, United States of America
| | - Lei He
- Departmnent of Electronic Engineering, University of California Los Angeles, Los Angeles, California, United States of America
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