Modular decomposition of metabolic systems via null-space analysis

Poolman, Mark G.; Sebu, Cristiana; Pidcock, Michael; Fell, David A.

doi:10.1016/j.jtbi.2007.08.005

Cited by 36 publications

(39 citation statements)

References 28 publications

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“…Unfortunately, it is very hard to compute the best branch decomposition. Therefore, we use heuristics, such as the method by Poolman et al [22]. Although this method gives us a branch decomposition from which we can recognize familiar sub-networks, its branch width is not very small.…”

Section: Resultsmentioning

confidence: 99%

“…Core idea is to compute a similarity matrix by computing the similarity for each pair of reactions. Interestingly, Ma et al [21] use a similarity measure, which is closely related to the one introduced by Poolman et al [22]. Indeed, the decomposition computed by Poolman et al [22] is a branch decomposition.…”

Section: Computing Branch Decompositions For Metabolic Networkmentioning

confidence: 99%

“…Interestingly, Ma et al [21] use a similarity measure, which is closely related to the one introduced by Poolman et al [22]. Indeed, the decomposition computed by Poolman et al [22] is a branch decomposition. In Figure 3 an excerpt of the branch decomposition computed for an E. coli core network [23] is shown.…”

Section: Computing Branch Decompositions For Metabolic Networkmentioning

confidence: 99%

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Hierarchical decomposition of metabolic networks using k-modules

Reimers

2015

Biochemical Society Transactions

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The optimal solutions obtained by flux balance analysis (FBA) are typically not unique. Flux modules have recently been shown to be a very useful tool to simplify and decompose the space of FBA-optimal solutions. Since yield-maximization is sometimes not the primary objective encountered in vivo, we are also interested in understanding the space of sub-optimal solutions. Unfortunately, the flux modules are too restrictive and not suited for this task. We present a generalization, called k-module, which compensates the limited applicability of flux modules to the space of sub-optimal solutions. Intuitively, a k-module is a sub-network with low connectivity to the rest of the network. Recursive application of k-modules yields a hierarchical decomposition of the metabolic network, which is also known as branch decomposition in matroid theory. In

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Section: Resultsmentioning

confidence: 99%

Section: Computing Branch Decompositions For Metabolic Networkmentioning

confidence: 99%

Section: Computing Branch Decompositions For Metabolic Networkmentioning

confidence: 99%

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Hierarchical decomposition of metabolic networks using k-modules

Reimers

2015

Biochemical Society Transactions

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“…This approach is similar to the concepts of reaction correlation coefficient (Poolman et al, 2007) and flux correlation (Poolman et al, 2009) used in conventional FBA, and it leads to a set of r values for the small metabolic network (Table II). Reactions that operate together have a positive r (e.g.…”

Section: Cost-weighted Flux Minimizationmentioning

confidence: 99%

A Method of Accounting for Enzyme Costs in Flux Balance Analysis Reveals Alternative Pathways and Metabolite Stores in an Illuminated Arabidopsis Leaf

2015

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Flux balance analysis of plant metabolism is an established method for predicting metabolic flux phenotypes and for exploring the way in which the plant metabolic network delivers specific outcomes in different cell types, tissues, and temporal phases. A recurring theme is the need to explore the flexibility of the network in meeting its objectives and, in particular, to establish the extent to which alternative pathways can contribute to achieving specific outcomes. Unfortunately, predictions from conventional flux balance analysis minimize the simultaneous operation of alternative pathways, but by introducing fluxweighting factors to allow for the variable intrinsic cost of supporting each flux, it is possible to activate different pathways in individual simulations and, thus, to explore alternative pathways by averaging thousands of simulations. This new method has been applied to a diel genome-scale model of Arabidopsis (Arabidopsis thaliana) leaf metabolism to explore the flexibility of the network in meeting the metabolic requirements of the leaf in the light. This identified alternative flux modes in the CalvinBenson cycle revealed the potential for alternative transitory carbon stores in leaves and led to predictions about the lightdependent contribution of alternative electron flow pathways and futile cycles in energy rebalancing. Notable features of the analysis include the light-dependent tradeoff between the use of carbohydrates and four-carbon organic acids as transitory storage forms and the way in which multiple pathways for the consumption of ATP and NADPH can contribute to the balancing of the requirements of photosynthetic metabolism with the energy available from photon capture.

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“…We have previously introduced the concept of the reaction correlation coefficient (Poolman et al, 2007). Briefly, this is the value of Pearson's correlation coefficient between a pair of fluxes over all possible steady states of a system and is calculated from the stoichiometry matrix.…”

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confidence: 99%

A Genome-Scale Metabolic Model of Arabidopsis and Some of Its Properties

et al. 2009

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We describe the construction and analysis of a genome-scale metabolic model of Arabidopsis (Arabidopsis thaliana) primarily derived from the annotations in the Aracyc database. We used techniques based on linear programming to demonstrate the following: (1) that the model is capable of producing biomass components (amino acids, nucleotides, lipid, starch, and cellulose) in the proportions observed experimentally in a heterotrophic suspension culture; (2) that approximately only 15% of the available reactions are needed for this purpose and that the size of this network is comparable to estimates of minimal network size for other organisms; (3) that reactions may be grouped according to the changes in flux resulting from a hypothetical stimulus (in this case demand for ATP) and that this allows the identification of potential metabolic modules; and (4) that total ATP demand for growth and maintenance can be inferred and that this is consistent with previous estimates in prokaryotes and yeast.Historically, attempts to engineer plant metabolism for increased production of specific useful products have met with mixed success. Problems arise because of considerable metabolic redundancy that allows imposed genetic changes to be circumvented, because of an insufficiently detailed knowledge of the distribution of control of flux, and because the behavior of plant metabolism at the network level is not well described. While increasingly complex metabolic networks are now being characterized with steadystate stable isotope labeling experiments (Libourel and Shachar-Hill, 2008;Schwender, 2008;Kruger and Ratcliffe, 2009), the resulting flux maps still only cover a small percentage of the total metabolic network. The construction of a comprehensive plant metabolic model that includes the complete repertoire of catalyzed transformations represented within a specific genome (a genome-scale metabolic model) would represent a significant step forward in the development of a description of plant metabolic behavior at the network level.The aim of this work is to describe a genome-scale structural model of Arabidopsis (Arabidopsis thaliana) metabolism and to explore the utility of the model as a tool to characterize possible flux behavior states of the metabolic network. Arabidopsis is the logical choice for this exercise because of its well-annotated genome. Moreover, the translation of the Arabidopsis genome into a curated set of metabolic reactions is already well advanced, and the reaction lists are available through the Aracyc database (Mueller et al., 2003;Zhang et al., 2005). While this is an excellent starting point for metabolic modeling, uncritical use of such reaction lists is likely to generate models exhibiting a number of problems, the most fundamental of which is violation of mass conservation. Thus, considerable effort is required to generate a useful genome-scale metabolic model from these databases .Once a stoichiometrically balanced structural model is achieved, the investigator is then faced with the challenge o...

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Modular decomposition of metabolic systems via null-space analysis

Cited by 36 publications

References 28 publications

Hierarchical decomposition of metabolic networks using k-modules

Hierarchical decomposition of metabolic networks using k-modules

A Method of Accounting for Enzyme Costs in Flux Balance Analysis Reveals Alternative Pathways and Metabolite Stores in an Illuminated Arabidopsis Leaf

A Genome-Scale Metabolic Model of Arabidopsis and Some of Its Properties

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