### Abstract

Motivation: Today, metabolite levels in biological samples can be determined using multiparallel, fast, and precise metabolomic approaches. Correlations between the levels of various metabolites can be searched to gain information about metabolic links. Such correlations are the net result of direct enzymatic conversions and of indirect cellular regulation over transcriptional or biochemical processes. In order to visualize metabolic networks derived from correlation lists graphically, each metabolite pair may be represented as vertices connected by an edge. However, graph complexity rapidly increases with the number of edges and vertices. To gain structural information from metabolite correlation networks, improvements in clarity are needed. Results: To achieve this clarity, three algorithms are combined. First, a list of linear metabolite correlations is generated that can be regarded as a set of pairs of edges (or as 2-cliques). Next, a branch-and-bound algorithm was developed to find all maximal cliques by combining submaximal cliques. Due to a clique assignment procedure, the generation of unnecessary submaximal cliques is avoided in order to maintain high efficiency. Differences and similarities to the Bron-Kerbosch algorithm are pointed out. Lastly, metabolite correlation networks are visualized by clique-metabolite matrices that are sorted to minimize the length of lines that connect different cliques and metabolites. Examples of biochemical hypotheses are given that can be built from interpretation of such clique matrices.

Original language | English (US) |
---|---|

Pages (from-to) | 1198-1208 |

Number of pages | 11 |

Journal | Bioinformatics |

Volume | 17 |

Issue number | 12 |

State | Published - 2002 |

Externally published | Yes |

### Fingerprint

### ASJC Scopus subject areas

- Clinical Biochemistry
- Computer Science Applications
- Computational Theory and Mathematics

### Cite this

*Bioinformatics*,

*17*(12), 1198-1208.

**Visualizing plant metabolomic correlation networks using clique-metabolite matrices.** / Kose, Frank; Weckwerth, Wolfram; Linke, Thomas; Fiehn, Oliver.

Research output: Contribution to journal › Article

*Bioinformatics*, vol. 17, no. 12, pp. 1198-1208.

}

TY - JOUR

T1 - Visualizing plant metabolomic correlation networks using clique-metabolite matrices

AU - Kose, Frank

AU - Weckwerth, Wolfram

AU - Linke, Thomas

AU - Fiehn, Oliver

PY - 2002

Y1 - 2002

N2 - Motivation: Today, metabolite levels in biological samples can be determined using multiparallel, fast, and precise metabolomic approaches. Correlations between the levels of various metabolites can be searched to gain information about metabolic links. Such correlations are the net result of direct enzymatic conversions and of indirect cellular regulation over transcriptional or biochemical processes. In order to visualize metabolic networks derived from correlation lists graphically, each metabolite pair may be represented as vertices connected by an edge. However, graph complexity rapidly increases with the number of edges and vertices. To gain structural information from metabolite correlation networks, improvements in clarity are needed. Results: To achieve this clarity, three algorithms are combined. First, a list of linear metabolite correlations is generated that can be regarded as a set of pairs of edges (or as 2-cliques). Next, a branch-and-bound algorithm was developed to find all maximal cliques by combining submaximal cliques. Due to a clique assignment procedure, the generation of unnecessary submaximal cliques is avoided in order to maintain high efficiency. Differences and similarities to the Bron-Kerbosch algorithm are pointed out. Lastly, metabolite correlation networks are visualized by clique-metabolite matrices that are sorted to minimize the length of lines that connect different cliques and metabolites. Examples of biochemical hypotheses are given that can be built from interpretation of such clique matrices.

AB - Motivation: Today, metabolite levels in biological samples can be determined using multiparallel, fast, and precise metabolomic approaches. Correlations between the levels of various metabolites can be searched to gain information about metabolic links. Such correlations are the net result of direct enzymatic conversions and of indirect cellular regulation over transcriptional or biochemical processes. In order to visualize metabolic networks derived from correlation lists graphically, each metabolite pair may be represented as vertices connected by an edge. However, graph complexity rapidly increases with the number of edges and vertices. To gain structural information from metabolite correlation networks, improvements in clarity are needed. Results: To achieve this clarity, three algorithms are combined. First, a list of linear metabolite correlations is generated that can be regarded as a set of pairs of edges (or as 2-cliques). Next, a branch-and-bound algorithm was developed to find all maximal cliques by combining submaximal cliques. Due to a clique assignment procedure, the generation of unnecessary submaximal cliques is avoided in order to maintain high efficiency. Differences and similarities to the Bron-Kerbosch algorithm are pointed out. Lastly, metabolite correlation networks are visualized by clique-metabolite matrices that are sorted to minimize the length of lines that connect different cliques and metabolites. Examples of biochemical hypotheses are given that can be built from interpretation of such clique matrices.

UR - http://www.scopus.com/inward/record.url?scp=0036137278&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0036137278&partnerID=8YFLogxK

M3 - Article

C2 - 11751228

AN - SCOPUS:0036137278

VL - 17

SP - 1198

EP - 1208

JO - Bioinformatics

JF - Bioinformatics

SN - 1367-4803

IS - 12

ER -