Understanding the Roadmap of Metabolism by Pathway Analysis

Department of Bioinformatics, Friedrich-Schiller University of Jena, Germany.
Methods in Molecular Biology (Impact Factor: 1.29). 02/2007; 358:199-226. DOI: 10.1007/978-1-59745-244-1_12
Source: PubMed


The theoretical investigation of the structure of metabolic systems has recently attracted increasing interest. In this chapter, the basic concepts of metabolic pathway analysis are described and various applications are outlined. In particular, the concepts of nullspace and elementary flux modes are explained. The presentation is illustrated by a simple example from tyrosine metabolism and a system describing lysine production in Corynebacterium glutamicum. The latter system gives rise to 37 elementary modes, 36 of which produce lysine with different molar yields. The examples illustrate that metabolic pathway analysis is a useful tool for better understanding the complex architecture of intracellular metabolism, for determining the pathways on which the molar conversion yield of a substrate-product pair under study is maximal, and for assigning functions to orphan genes (functional genomics). Moreover, problems emerging in the modeling of large networks are discussed. An outlook on current trends in the field concludes the chapter.

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    • "These properties were condensed in the definition of elementary flux mode (EFM) in the work of Schuster and Hilgetag (1994). Since its introduction, the concept of EFM has received much attention, showing that a wide range of questions in bioengineering and bioinformatics can be addressed using such an approach (Schuster et al., 2007; Trinh et al., 2009). In particular, the prediction of novel biologically meaningful pathways constitutes one of the most important applications of EFM analysis. "
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    ABSTRACT: The reconstruction of metabolic networks at the genome scale has allowed the analysis of metabolic pathways at an unprecedented level of complexity. Elementary flux modes (EFMs) are an appropriate concept for such analysis. However, their number grows in a combinatorial fashion as the size of the metabolic network increases, which renders the application of EFMs approach to large metabolic networks difficult. Novel methods are expected to deal with such complexity. In this article, we present a novel optimization-based method for determining a minimal generating set of EFMs, i.e. a convex basis. We show that a subset of elements of this convex basis can be effectively computed even in large metabolic networks. Our method was applied to examine the structure of pathways producing lysine in Escherichia coli. We obtained a more varied and informative set of pathways in comparison with existing methods. In addition, an alternative pathway to produce lysine was identified using a detour via propionyl-CoA, which shows the predictive power of our novel approach. The source code in C++ is available upon request.
    Bioinformatics 02/2011; 27(4):534-40. DOI:10.1093/bioinformatics/btq681 · 4.98 Impact Factor
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    • "A pathway in such a network represents a series of reactions which transform a given molecule into others. An application for pathway discovery (see [18] [20] for more details on pathway discovery) in metabolic networks is the explanation of DNA experiments. An experiment is performed on DNA cells and these mutated cells (called RNA cells) are placed on DNA chips, which contain specific locations for different strands, so when the cells are placed in the chips, the different strands will fit into their specific locations . "
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    ABSTRACT: In this paper we propose a lazy constraint imposing mechanism for improving the path constraint in GRASPER, a state-of-the-art graph constraint solver, having obtained very promising results in terms of both time and space in solving an interesting problem in the Biochemistry subject area, in comparison with CP(Graph), the state-of-the-art solver.
    Electronic Notes in Theoretical Computer Science 11/2009; 253(4):113-128. DOI:10.1016/j.entcs.2009.10.020
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    • "Nowadays, C.glutamicum is the organism of choice for lysine overproduction due to the higher yields obtained with it. The capability for producing lysine has been previously examined from a pathway oriented perspective (de Graaf, 2000; Mavrovouniotis et al., 1990; Schuster et al., 2007). However, these studies were not conducted at the genome-scale. "
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    ABSTRACT: Elementary flux modes (EFMs) represent a key concept to analyze metabolic networks from a pathway-oriented perspective. In spite of considerable work in this field, the computation of the full set of elementary flux modes in large-scale metabolic networks still constitutes a challenging issue due to its underlying combinatorial complexity. In this article, we illustrate that the full set of EFMs can be enumerated in increasing order of number of reactions via integer linear programming. In this light, we present a novel procedure to efficiently determine the K-shortest EFMs in large-scale metabolic networks. Our method was applied to find the K-shortest EFMs that produce lysine in the genome-scale metabolic networks of Escherichia coli and Corynebacterium glutamicum. A detailed analysis of the biological significance of the K-shortest EFMs was conducted, finding that glucose catabolism, ammonium assimilation, lysine anabolism and cofactor balancing were correctly predicted. The work presented here represents an important step forward in the analysis and computation of EFMs for large-scale metabolic networks, where traditional methods fail for networks of even moderate size. Supplementary data are available at Bioinformatics online.
    Bioinformatics 09/2009; 25(23):3158-65. DOI:10.1093/bioinformatics/btp564 · 4.98 Impact Factor
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