### Abstract

The accumulation of high-throughput genomic and proteomic data allows for the reconstruction of the increasingly large and complex metabolic networks. In order to analyze accumulated data and reconstructed networks, it is critical to identify network patterns and evolutionary relations between metabolic networks. But even finding similar networks becomes computationally challenging. Alignment of the reconstructed networks can help to catch model inconsistencies and infer missing elements. We have formulated the network alignment problem which asks for the optimal vertex-to-vertex mapping allowing path contraction, vertex deletion, and vertex insertions. This paper gives the first efficient algorithm for optimal aligning of metabolic pathways with bounded tree width. In particular, the optimal alignment from pathway P to pathway T can be found in time O(|VP||VT|α^{a+1}), where V_{P} and V_{T} are the vertex sets of pathways and a is the tree width of P. This significantly improves alignment tools since the E.coli metabolic network has tree width 3 and more than 90% of pathways of several organisms are series-parallel. We have implemented the algorithm for alignment of metabolic pathways of tree width 2 with arbitrary metabolic networks. Our experiments show that allowing pattern vertex deletion significantly improves alignment. We also have applied the network alignment to identifying inconsistency, inferring missing enzymes, and finding potential candidates for filling the holes.

Original language | English (US) |
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Title of host publication | Proceedings - 10th IEEE International Conference on Data Mining Workshops, ICDMW 2010 |

Pages | 687-694 |

Number of pages | 8 |

DOIs | |

State | Published - Dec 1 2010 |

Event | 10th IEEE International Conference on Data Mining Workshops, ICDMW 2010 - Sydney, NSW, Australia Duration: Dec 14 2010 → Dec 17 2010 |

### Publication series

Name | Proceedings - IEEE International Conference on Data Mining, ICDM |
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ISSN (Print) | 1550-4786 |

### Other

Other | 10th IEEE International Conference on Data Mining Workshops, ICDMW 2010 |
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Country | Australia |

City | Sydney, NSW |

Period | 12/14/10 → 12/17/10 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Engineering(all)

### Cite this

*Proceedings - 10th IEEE International Conference on Data Mining Workshops, ICDMW 2010*(pp. 687-694). [5693363] (Proceedings - IEEE International Conference on Data Mining, ICDM). https://doi.org/10.1109/ICDMW.2010.150

}

*Proceedings - 10th IEEE International Conference on Data Mining Workshops, ICDMW 2010.*, 5693363, Proceedings - IEEE International Conference on Data Mining, ICDM, pp. 687-694, 10th IEEE International Conference on Data Mining Workshops, ICDMW 2010, Sydney, NSW, Australia, 12/14/10. https://doi.org/10.1109/ICDMW.2010.150

**Efficient alignments of metabolic networks with bounded treewidth.** / Cheng, Qiong; Berman, Piotr; Harrison, Rob; Zelikovsky, Alex.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Efficient alignments of metabolic networks with bounded treewidth

AU - Cheng, Qiong

AU - Berman, Piotr

AU - Harrison, Rob

AU - Zelikovsky, Alex

PY - 2010/12/1

Y1 - 2010/12/1

N2 - The accumulation of high-throughput genomic and proteomic data allows for the reconstruction of the increasingly large and complex metabolic networks. In order to analyze accumulated data and reconstructed networks, it is critical to identify network patterns and evolutionary relations between metabolic networks. But even finding similar networks becomes computationally challenging. Alignment of the reconstructed networks can help to catch model inconsistencies and infer missing elements. We have formulated the network alignment problem which asks for the optimal vertex-to-vertex mapping allowing path contraction, vertex deletion, and vertex insertions. This paper gives the first efficient algorithm for optimal aligning of metabolic pathways with bounded tree width. In particular, the optimal alignment from pathway P to pathway T can be found in time O(|VP||VT|αa+1), where VP and VT are the vertex sets of pathways and a is the tree width of P. This significantly improves alignment tools since the E.coli metabolic network has tree width 3 and more than 90% of pathways of several organisms are series-parallel. We have implemented the algorithm for alignment of metabolic pathways of tree width 2 with arbitrary metabolic networks. Our experiments show that allowing pattern vertex deletion significantly improves alignment. We also have applied the network alignment to identifying inconsistency, inferring missing enzymes, and finding potential candidates for filling the holes.

AB - The accumulation of high-throughput genomic and proteomic data allows for the reconstruction of the increasingly large and complex metabolic networks. In order to analyze accumulated data and reconstructed networks, it is critical to identify network patterns and evolutionary relations between metabolic networks. But even finding similar networks becomes computationally challenging. Alignment of the reconstructed networks can help to catch model inconsistencies and infer missing elements. We have formulated the network alignment problem which asks for the optimal vertex-to-vertex mapping allowing path contraction, vertex deletion, and vertex insertions. This paper gives the first efficient algorithm for optimal aligning of metabolic pathways with bounded tree width. In particular, the optimal alignment from pathway P to pathway T can be found in time O(|VP||VT|αa+1), where VP and VT are the vertex sets of pathways and a is the tree width of P. This significantly improves alignment tools since the E.coli metabolic network has tree width 3 and more than 90% of pathways of several organisms are series-parallel. We have implemented the algorithm for alignment of metabolic pathways of tree width 2 with arbitrary metabolic networks. Our experiments show that allowing pattern vertex deletion significantly improves alignment. We also have applied the network alignment to identifying inconsistency, inferring missing enzymes, and finding potential candidates for filling the holes.

UR - http://www.scopus.com/inward/record.url?scp=79951739018&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79951739018&partnerID=8YFLogxK

U2 - 10.1109/ICDMW.2010.150

DO - 10.1109/ICDMW.2010.150

M3 - Conference contribution

AN - SCOPUS:79951739018

SN - 9780769542577

T3 - Proceedings - IEEE International Conference on Data Mining, ICDM

SP - 687

EP - 694

BT - Proceedings - 10th IEEE International Conference on Data Mining Workshops, ICDMW 2010

ER -