Comparison and improvement of algorithms for computing minimal cut sets
Background Constrained minimal cut sets (cMCSs) have recently been introduced as a framework to enumerate minimal genetic intervention strategies for targeted optimization of metabolic networks. Two different algorithmic schemes (adapted Berge algorithm and binary integer programming) have been prop...
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| Veröffentlicht in: | BMC bioinformatics Jg. 14; H. 1; S. 318 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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BioMed Central
06.11.2013
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| ISSN: | 1471-2105, 1471-2105 |
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| Abstract | Background
Constrained minimal cut sets (cMCSs) have recently been introduced as a framework to enumerate minimal genetic intervention strategies for targeted optimization of metabolic networks. Two different algorithmic schemes (adapted Berge algorithm and binary integer programming) have been proposed to compute cMCSs from elementary modes. However, in their original formulation both algorithms are not fully comparable.
Results
Here we show that by a small extension to the integer program both methods become equivalent. Furthermore, based on well-known preprocessing procedures for integer programming we present efficient preprocessing steps which can be used for both algorithms. We then benchmark the numerical performance of the algorithms in several realistic medium-scale metabolic models. The benchmark calculations reveal (i) that these preprocessing steps can lead to an enormous speed-up under both algorithms, and (ii) that the adapted Berge algorithm outperforms the binary integer approach.
Conclusions
Generally, both of our new implementations are by at least one order of magnitude faster than other currently available implementations. |
|---|---|
| AbstractList | Constrained minimal cut sets (cMCSs) have recently been introduced as a framework to enumerate minimal genetic intervention strategies for targeted optimization of metabolic networks. Two different algorithmic schemes (adapted Berge algorithm and binary integer programming) have been proposed to compute cMCSs from elementary modes. However, in their original formulation both algorithms are not fully comparable. Here we show that by a small extension to the integer program both methods become equivalent. Furthermore, based on well-known preprocessing procedures for integer programming we present efficient preprocessing steps which can be used for both algorithms. We then benchmark the numerical performance of the algorithms in several realistic medium-scale metabolic models. The benchmark calculations reveal (i) that these preprocessing steps can lead to an enormous speed-up under both algorithms, and (ii) that the adapted Berge algorithm outperforms the binary integer approach. Generally, both of our new implementations are by at least one order of magnitude faster than other currently available implementations. Constrained minimal cut sets (cMCSs) have recently been introduced as a framework to enumerate minimal genetic intervention strategies for targeted optimization of metabolic networks. Two different algorithmic schemes (adapted Berge algorithm and binary integer programming) have been proposed to compute cMCSs from elementary modes. However, in their original formulation both algorithms are not fully comparable. Here we show that by a small extension to the integer program both methods become equivalent. Furthermore, based on well-known preprocessing procedures for integer programming we present efficient preprocessing steps which can be used for both algorithms. We then benchmark the numerical performance of the algorithms in several realistic medium-scale metabolic models. The benchmark calculations reveal (i) that these preprocessing steps can lead to an enormous speed-up under both algorithms, and (ii) that the adapted Berge algorithm outperforms the binary integer approach. Generally, both of our new implementations are by at least one order of magnitude faster than other currently available implementations. Background: Constrained minimal cut sets (cMCSs) have recently been introduced as a framework to enumerate minimal genetic intervention strategies for targeted optimization of metabolic networks. Two different algorithmic schemes (adapted Berge algorithm and binary integer programming) have been proposed to compute cMCSs from elementary modes. However, in their original formulation both algorithms are not fully comparable. Results: Here we show that by a small extension to the integer program both methods become equivalent. Furthermore, based on well-known preprocessing procedures for integer programming we present efficient preprocessing steps which can be used for both algorithms. We then benchmark the numerical performance of the algorithms in several realistic medium-scale metabolic models. The benchmark calculations reveal (i) that these preprocessing steps can lead to an enormous speed-up under both algorithms, and (ii) that the adapted Berge algorithm outperforms the binary integer approach. Conclusions: Generally, both of our new implementations are by at least one order of magnitude faster than other currently available implementations. Constrained minimal cut sets (cMCSs) have recently been introduced as a framework to enumerate minimal genetic intervention strategies for targeted optimization of metabolic networks. Two different algorithmic schemes (adapted Berge algorithm and binary integer programming) have been proposed to compute cMCSs from elementary modes. However, in their original formulation both algorithms are not fully comparable.BACKGROUNDConstrained minimal cut sets (cMCSs) have recently been introduced as a framework to enumerate minimal genetic intervention strategies for targeted optimization of metabolic networks. Two different algorithmic schemes (adapted Berge algorithm and binary integer programming) have been proposed to compute cMCSs from elementary modes. However, in their original formulation both algorithms are not fully comparable.Here we show that by a small extension to the integer program both methods become equivalent. Furthermore, based on well-known preprocessing procedures for integer programming we present efficient preprocessing steps which can be used for both algorithms. We then benchmark the numerical performance of the algorithms in several realistic medium-scale metabolic models. The benchmark calculations reveal (i) that these preprocessing steps can lead to an enormous speed-up under both algorithms, and (ii) that the adapted Berge algorithm outperforms the binary integer approach.RESULTSHere we show that by a small extension to the integer program both methods become equivalent. Furthermore, based on well-known preprocessing procedures for integer programming we present efficient preprocessing steps which can be used for both algorithms. We then benchmark the numerical performance of the algorithms in several realistic medium-scale metabolic models. The benchmark calculations reveal (i) that these preprocessing steps can lead to an enormous speed-up under both algorithms, and (ii) that the adapted Berge algorithm outperforms the binary integer approach.Generally, both of our new implementations are by at least one order of magnitude faster than other currently available implementations.CONCLUSIONSGenerally, both of our new implementations are by at least one order of magnitude faster than other currently available implementations. Background Constrained minimal cut sets (cMCSs) have recently been introduced as a framework to enumerate minimal genetic intervention strategies for targeted optimization of metabolic networks. Two different algorithmic schemes (adapted Berge algorithm and binary integer programming) have been proposed to compute cMCSs from elementary modes. However, in their original formulation both algorithms are not fully comparable. Results Here we show that by a small extension to the integer program both methods become equivalent. Furthermore, based on well-known preprocessing procedures for integer programming we present efficient preprocessing steps which can be used for both algorithms. We then benchmark the numerical performance of the algorithms in several realistic medium-scale metabolic models. The benchmark calculations reveal (i) that these preprocessing steps can lead to an enormous speed-up under both algorithms, and (ii) that the adapted Berge algorithm outperforms the binary integer approach. Conclusions Generally, both of our new implementations are by at least one order of magnitude faster than other currently available implementations. Background Constrained minimal cut sets (cMCSs) have recently been introduced as a framework to enumerate minimal genetic intervention strategies for targeted optimization of metabolic networks. Two different algorithmic schemes (adapted Berge algorithm and binary integer programming) have been proposed to compute cMCSs from elementary modes. However, in their original formulation both algorithms are not fully comparable. Results Here we show that by a small extension to the integer program both methods become equivalent. Furthermore, based on well-known preprocessing procedures for integer programming we present efficient preprocessing steps which can be used for both algorithms. We then benchmark the numerical performance of the algorithms in several realistic medium-scale metabolic models. The benchmark calculations reveal (i) that these preprocessing steps can lead to an enormous speed-up under both algorithms, and (ii) that the adapted Berge algorithm outperforms the binary integer approach. Conclusions Generally, both of our new implementations are by at least one order of magnitude faster than other currently available implementations. Keywords: Metabolic network analysis, Elementary modes, Minimal cut sets, Knockout strategies, Integer programming, Berge's algorithm |
| ArticleNumber | 318 |
| Audience | Academic |
| Author | Zanghellini, Jürgen Klamt, Steffen Nair, Govind Jungreuthmayer, Christian |
| AuthorAffiliation | 2 Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria 1 Austrian Centre of Industrial Biotechnology, Vienna, Austria 3 Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany |
| AuthorAffiliation_xml | – name: 1 Austrian Centre of Industrial Biotechnology, Vienna, Austria – name: 2 Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria – name: 3 Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/24191903$$D View this record in MEDLINE/PubMed |
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| Keywords | Integer programming Metabolic network analysis Knockout strategies Minimal cut sets Berge’s algorithm Elementary modes |
| Language | English |
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Constrained minimal cut sets (cMCSs) have recently been introduced as a framework to enumerate minimal genetic intervention strategies for targeted... Constrained minimal cut sets (cMCSs) have recently been introduced as a framework to enumerate minimal genetic intervention strategies for targeted... Background Constrained minimal cut sets (cMCSs) have recently been introduced as a framework to enumerate minimal genetic intervention strategies for targeted... Background: Constrained minimal cut sets (cMCSs) have recently been introduced as a framework to enumerate minimal genetic intervention strategies for targeted... |
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| SubjectTerms | Algorithms Analysis Bioinformatics Biomedical and Life Sciences Cell metabolism Comparative analysis Computational Biology - methods Computational Biology/Bioinformatics Computer Appl. in Life Sciences Computer Simulation Escherichia coli - genetics Gene Deletion Gene Knockout Techniques Humans Image Processing, Computer-Assisted Life Sciences Metabolic Networks and Pathways - genetics Methodology Methodology Article Microarrays Models, Genetic Networks analysis Software Thermodynamics |
| Title | Comparison and improvement of algorithms for computing minimal cut sets |
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