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The topology of metabolic isotope labeling networks.

Weitzel M, Wiechert W, Nöh K - BMC Bioinformatics (2007)

Bottom Line: 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.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.

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Affiliation: Institute of Biotechnology, Research Centre Jülich, 52425 Jülich, Germany. michael.weitzel@uni-siegen.de

ABSTRACT

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.

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SCC of network "B" (TCA cycle). A SCC of network "B" found in the TCA cycle on network level one. The pool SucCoA (Succinyl coenzyme A) is bypassed by GlyOx (Glyoxylate) and CO2 (CO2).
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Figure 6: SCC of network "B" (TCA cycle). A SCC of network "B" found in the TCA cycle on network level one. The pool SucCoA (Succinyl coenzyme A) is bypassed by GlyOx (Glyoxylate) and CO2 (CO2).

Mentions: In other words, the labeled atom positions in a cumomer describe a labeled fragment which is shared among its set of isotopomers. From this perspective the reactions in a cumomer network describe how labeled fragments of certain size are transported by the metabolic reactions. These fragments may be permuted by the metabolic reactions so that they are not necessarily contiguous (e.g. ). Paths through the cumomer network graphs can be characterized by the property that a certain subset of atoms is retained by a chain of metabolic reactions (cf. Fig. 6).


The topology of metabolic isotope labeling networks.

Weitzel M, Wiechert W, Nöh K - BMC Bioinformatics (2007)

SCC of network "B" (TCA cycle). A SCC of network "B" found in the TCA cycle on network level one. The pool SucCoA (Succinyl coenzyme A) is bypassed by GlyOx (Glyoxylate) and CO2 (CO2).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2233644&req=5

Figure 6: SCC of network "B" (TCA cycle). A SCC of network "B" found in the TCA cycle on network level one. The pool SucCoA (Succinyl coenzyme A) is bypassed by GlyOx (Glyoxylate) and CO2 (CO2).
Mentions: In other words, the labeled atom positions in a cumomer describe a labeled fragment which is shared among its set of isotopomers. From this perspective the reactions in a cumomer network describe how labeled fragments of certain size are transported by the metabolic reactions. These fragments may be permuted by the metabolic reactions so that they are not necessarily contiguous (e.g. ). Paths through the cumomer network graphs can be characterized by the property that a certain subset of atoms is retained by a chain of metabolic reactions (cf. Fig. 6).

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Biotechnology, Research Centre Jülich, 52425 Jülich, Germany. michael.weitzel@uni-siegen.de

ABSTRACT

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.

Show MeSH