Consistent Alignment of Metabolic Pathways Without Abstraction

Ay, Ferhat; Kahveci, Tamer; Crécy‐Lagard, Valérie de

doi:10.1142/9781848162648_0021

Cited by 11 publications

(26 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SubMAP accounts for both the effect of pairwise similarities (homology) and the organization of pathways (topology). This combination is motivated by its successful applications on network alignment by Singh et al (2007Singh et al ( , 2008 and Ay et al (2008Ay et al ( , 2009a impossible to trivially extend these methods to our problem. Here, we describe our method that addresses this challenge.…”

Section: Contributionsmentioning

confidence: 99%

“…One standard scoring scheme for this purpose incorporates the homology of the aligned molecules with their topologies (Singh et al, 2007(Singh et al, , 2008Ay et al, 2008Ay et al, , 2009a. Here, we generalize this scheme to one-to-many mappings.…”

Section: Our Algorithm: Submapmentioning

confidence: 99%

“…The first type considers one pathway at a time and explores its important properties such as its robustness (Edwards and Palsson, 2000), steady states (Devloo et al, 2003;Garg et al, 2007;Ay et al, 2009b), modular structure (Lu et al, 2006;Schuster et al, 2002;Ay et al, 2010), network motifs (Milo et al, 298;Wernicke and Rasche, 2006;Grochow and Kellis, 2007), as well as its representation (Michal, 1998;Babur et al, 2010). The second type is the comparative approach which considers multiple pathways to identify their frequent subgraphs (Koyuturk et al, 2004;Qian and Yoon, 2009) and their alignments (Singh et al, 2008(Singh et al, , 2007Liao et al, 2009;Kalaev et al, 2008;Sharan et al, 2005;Kalaev et al, 2009;Dost et al, 2007;Flannick et al, 2006;Koyuturk et al, 2005;Pinter et al, 2005;Berg and Lassig, 2004;Dandekar et al, 1999;Tohsato and Nishimura, 2008;Tohsato et al, 2000;Cheng et al, 2009;Ay et al, 2008Ay et al, , 2009a. Alignment is a fundamental type of comparative analysis that aims to identify similar parts between pathways.…”

Section: Introduction Bmentioning

confidence: 99%

See 2 more Smart Citations

SubMAP: Aligning Metabolic Pathways with Subnetwork Mappings

Kahveci

2010

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

We consider the problem of aligning two metabolic pathways. Unlike traditional approaches, we do not restrict the alignment to one-to-one mappings between the molecules (nodes) of the input pathways (graphs). We follow the observation that, in nature, different organisms can perform the same or similar functions through different sets of reactions and molecules. The number and the topology of the molecules in these alternative sets often vary from one organism to another. With the motivation that an accurate biological alignment should be able to reveal these functionally similar molecule sets across different species, we develop an algorithm that first measures the similarities between different nodes using a mixture of homology and topological similarity. We combine the two metrics by employing an eigenvalue formulation. We then search for an alignment between the two input pathways that maximizes a similarity score, evaluated as the sum of the similarities of the mapped subnetworks of size at most a given integer k, and also does not contain any conflicting mappings. Here we prove that this maximization is NP-hard by a reduction from the maximum weight independent set (MWIS) problem. We then convert our problem to an instance of MWIS and use an efficient vertex-selection strategy to extract the mappings that constitute our alignment. We name our algorithm SubMAP (Subnetwork Mappings in Alignment of Pathways). We evaluate its accuracy and performance on real datasets. Our empirical results demonstrate that SubMAP can identify biologically relevant mappings that are missed by traditional alignment methods. Furthermore, we observe that SubMAP is scalable for metabolic pathways of arbitrary topology, including searching for a query pathway of size 70 against the complete KEGG database of 1,842 pathways. Implementation in C++ is available at

show abstract

Section: Contributionsmentioning

confidence: 99%

Section: Our Algorithm: Submapmentioning

confidence: 99%

Section: Introduction Bmentioning

confidence: 99%

See 1 more Smart Citation

SubMAP: Aligning Metabolic Pathways with Subnetwork Mappings

Kahveci

2010

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this particular domain, the query is a metabolic pathway for a particular organism, and the goal is to find the few pathways in the database that most closely match the query. The actual ranking is obtained using the sum of the topological and homological similarities of the two pathways (We used the alignment software and the scoring measure developed in [3] for this purpose). We compute a surrogate ranking function as the ratio of the number of common enzymes and compounds in two pathways to the total number of enzymes and compounds in the union of the two pathways.…”

Section: Pathway Searchmentioning

confidence: 99%

Surrogate ranking for very expensive similarity queries

Jampani

et al. 2010

2010 IEEE 26th International Conference on Data Engineering (ICDE 2010)

Self Cite

View full text Add to dashboard Cite

Abstract-1 We consider the problem of similarity search in applications where the cost of computing the similarity between two records is very expensive, and the similarity measure is not a metric. In such applications, comparing even a tiny fraction of the database records to a single query record can be orders of magnitude slower than reading the entire database from disk, and indexing is often not possible. We develop a general-purpose, statistical framework for answering top-k queries in such databases, when the database administrator is able to supply an inexpensive surrogate ranking function that substitutes for the actual similarity measure. We develop a robust method that learns the relationship between the surrogate function and the similarity measure. Given a query, we use Bayesian statistics to update the model by taking into account the observed partial results. Using the updated model, we construct bounds on the accuracy of the result set obtained via the surrogate ranking. Our experiments show that our models can produce useful bounds for several real-life applications.

show abstract

“…Metabolic pathway data is an important type of data in biology and provides interaction information on three types of entities important to organisms [1]. By analyzing this data, people can get useful information.…”

Section: Introductionmentioning

confidence: 99%

The Multiple Alignment Algorithm for Metabolic Pathways without Abstraction

Chen

Rocha

Hendrix

et al. 2010

2010 IEEE International Conference on Data Mining Workshops

View full text Add to dashboard Cite

Computational problems associated with metabolic pathways have been extensively studied in computational biology. The problem of aligning multiple metabolic pathways is very challenging. Tohsato et al.'s algorithm for aligning multiple metabolic pathways [22] is based on similarities between enzymes; however, a metabolic pathway consists of three types of entities: reactions, compounds, and enzymes. In this paper, we propose the first algorithm for the problem of aligning multiple metabolic pathways based on the similarities among reactions, compounds, enzymes, and pathway topology.First, we compute a weight between each pair of like entities in different input pathways based on the entities' similarity score and topological structure using the methods in [1]. We then construct a weighted k-partite graph for the reactions, compounds, and enzymes. We extract a mapping between these entities by solving the maximum-weighted kpartite matching problem by applying a novel heuristic algorithm. By analyzing the alignment results of multiple pathways in different organisms, we show that the alignments found by our algorithm correctly identify common subnetworks among multiple pathways.

show abstract

Consistent Alignment of Metabolic Pathways Without Abstraction

Cited by 11 publications

References 18 publications

SubMAP: Aligning Metabolic Pathways with Subnetwork Mappings

SubMAP: Aligning Metabolic Pathways with Subnetwork Mappings

Surrogate ranking for very expensive similarity queries

The Multiple Alignment Algorithm for Metabolic Pathways without Abstraction

Contact Info

Product

Resources

About