PMID- 17727715
OWN - NLM
STAT- MEDLINE
DCOM- 20080327
LR  - 20230412
IS  - 1471-2105 (Electronic)
IS  - 1471-2105 (Linking)
VI  - 8
DP  - 2007 Aug 29
TI  - The topology of metabolic isotope labeling networks.
PG  - 315
AB  - BACKGROUND: Metabolic Flux Analysis (MFA) based on isotope labeling experiments 
      (ILEs) is a widely established tool for determining fluxes in metabolic pathways. 
      Isotope labeling networks (ILNs) contain all essential information required to 
      describe the flow of labeled material in an ILE. Whereas recent experimental 
      progress paves the way for high-throughput MFA, large network investigations and 
      exact statistical methods, these developments are still limited by the poor 
      performance of computational routines used for the evaluation and design of ILEs. 
      In this context, the global analysis of ILN topology turns out to be a clue for 
      realizing large speedup factors in all required computational procedures. 
      RESULTS: With a strong focus on the speedup of algorithms the topology of ILNs is 
      investigated using graph theoretic concepts and algorithms. A rigorous 
      determination of all cyclic and isomorphic subnetworks, accompanied by the global 
      analysis of ILN connectivity is performed. Particularly, it is proven that ILNs 
      always brake up into a large number of small strongly connected components (SCCs) 
      and, moreover, there are natural isomorphisms between many of these SCCs. All 
      presented techniques are universal, i.e. they do not require special assumptions 
      on the network structure, bidirectionality of fluxes, measurement configuration, 
      or label input. The general results are exemplified with a practically relevant 
      metabolic network which describes the central metabolism of E. coli comprising 
      10390 isotopomer pools. CONCLUSION: Exploiting the topological features of ILNs 
      leads to a significant speedup of all universal algorithms for ILE evaluation. It 
      is proven in theory and exemplified with the E. coli example that a speedup 
      factor of about 1000 compared to standard algorithms is achieved. This widely 
      opens the door for new high performance algorithms suitable for high throughput 
      applications and large ILNs. Moreover, for the first time the global topological 
      analysis of ILNs allows to comprehensively describe and understand the general 
      patterns of label flow in complex networks. This is an invaluable tool for the 
      structural design of new experiments and the interpretation of measured data.
FAU - Weitzel, Michael
AU  - Weitzel M
AD  - Institute of Biotechnology, Research Centre Julich, 52425 Julich, Germany. 
      michael.weitzel@uni-siegen.de
FAU - Wiechert, Wolfgang
AU  - Wiechert W
FAU - Noh, Katharina
AU  - Noh K
LA  - eng
PT  - Journal Article
DEP - 20070829
PL  - England
TA  - BMC Bioinformatics
JT  - BMC bioinformatics
JID - 100965194
SB  - IM
MH  - Algorithms
MH  - *Cluster Analysis
MH  - Computational Biology/*methods
MH  - Efficiency
MH  - Escherichia coli/metabolism
MH  - *Isotope Labeling/classification/methods
MH  - *Metabolic Networks and Pathways
MH  - Neural Networks, Computer
MH  - Research Design
MH  - Work Simplification
PMC - PMC2233644
EDAT- 2007/08/31 09:00
MHDA- 2008/03/28 09:00
PMCR- 2007/08/29
CRDT- 2007/08/31 09:00
PHST- 2007/02/27 00:00 [received]
PHST- 2007/08/29 00:00 [accepted]
PHST- 2007/08/31 09:00 [pubmed]
PHST- 2008/03/28 09:00 [medline]
PHST- 2007/08/31 09:00 [entrez]
PHST- 2007/08/29 00:00 [pmc-release]
AID - 1471-2105-8-315 [pii]
AID - 10.1186/1471-2105-8-315 [doi]
PST - epublish
SO  - BMC Bioinformatics. 2007 Aug 29;8:315. doi: 10.1186/1471-2105-8-315.