Enumerating Precursor Sets of Target Metabolites in a Metabolic Network

Cottret, Ludovic; Milreu, Paulo Vieira; Acuña, Vicente; Marchetti-Spaccamela, Alberto; Martinez, Fábio Viduani; Sagot, Marie‐France; Stougie, Leen

doi:10.1007/978-3-540-87361-7_20

Cited by 24 publications

(28 citation statements)

References 8 publications

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“…Importantly, analyzing the metagenome through the lens of genome-scale metabolic models, this study was able to address questions that metagenomic-based studies cannot. Similar models that directly link the network topologies of interacting species can further identify specific metabolic exchanges between microbial species, or between such species and the host (Cottret et al, 2008, 2010). …”

Section: Modeling Microbiome Metabolism and Metabolic Interactionsmentioning

confidence: 99%

Mapping the Inner Workings of the Microbiome: Genomic- and Metagenomic-Based Study of Metabolism and Metabolic Interactions in the Human Microbiome

2014

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The human gut microbiome is a major contributor to human metabolism and health, yet the metabolic processes that are carried out by various community members, the way these members interact with each other and with the host, and the impact of such interactions on the overall metabolic machinery of the microbiome have not yet been mapped. Here, we discuss recent efforts to study the metabolic inner workings of this complex ecosystem. We will specifically highlight two interrelated lines of work, the first aiming to deconvolve the microbiome and to characterize the metabolic capacity of various microbiome species, and the second aiming to utilize computational modeling to infer and study metabolic interactions between these species.

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Section: Modeling Microbiome Metabolism and Metabolic Interactionsmentioning

confidence: 99%

Mapping the Inner Workings of the Microbiome: Genomic- and Metagenomic-Based Study of Metabolism and Metabolic Interactions in the Human Microbiome

2014

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“…However, it is not so clear if this is really helpful to biologists, as the implicitly given graph (defined via possible reactions) could be comparatively huge compared to the actually given sets S and T . Further variants are discussed in [6], where also the interesting question of enumerating minimal solutions is raised. Similar questions can be put forward for the concrete graph-theoretic problem considered in this paper.…”

Section: Discussionmentioning

confidence: 99%

An exact exponential-time algorithm for the Directed Maximum Leaf Spanning Tree problem

Binkele-Raible

Fernau

2012

Journal of Discrete Algorithms

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NP-hard problems on directed graphs Outbranching with a maximum number of leaves Given a directed graph G = (V , A), the Directed Maximum Leaf Spanning Tree problem asks to compute a directed spanning tree with as many leaves as possible. By designing a branching algorithm analyzed with Measure&Conquer, we show that the problem can be solved in time O * (1.9044 n ) using polynomial space. Allowing exponential space, this run time upper bound can be lowered to O * (1.8139 n ). We also provide an example showing a lower-bound for the running time of our algorithm.

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“…In this definition, special care is taken sometimes to deal with “bootstrapping” or “self-regenerating” compounds [6,18,19]. This simplified definition sets aside stoichiometric information, which significantly limits the accuracy of its predictions.…”

Section: Discussionmentioning

confidence: 99%

“…Handorf et al [18] and Cottret et al [19] are the only works that attempt to analyze all minimal nutrient sets. Handorf et al [18] state that enumerating all minimal sets is “impossible” and hence, a random (biased) sampling process is used to enumerate some (at most 1000) of the minimal nutrient sets.…”

Section: Discussionmentioning

confidence: 99%

Computing minimal nutrient sets from metabolic networks via linear constraint solving

et al. 2013

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BackgroundAs more complete genome sequences become available, bioinformatics challenges arise in how to exploit genome sequences to make phenotypic predictions. One type of phenotypic prediction is to determine sets of compounds that will support the growth of a bacterium from the metabolic network inferred from the genome sequence of that organism.ResultsWe present a method for computationally determining alternative growth media for an organism based on its metabolic network and transporter complement. Our method predicted 787 alternative anaerobic minimal nutrient sets for Escherichia coli K–12 MG1655 from the EcoCyc database. The program automatically partitioned the nutrients within these sets into 21 equivalence classes, most of which correspond to compounds serving as sources of carbon, nitrogen, phosphorous, and sulfur, or combinations of these essential elements. The nutrient sets were predicted with 72.5% accuracy as evaluated by comparison with 91 growth experiments. Novel aspects of our approach include (a) exhaustive consideration of all combinations of nutrients rather than assuming that all element sources can substitute for one another(an assumption that can be invalid in general) (b) leveraging the notion of a machinery-duplicating constraint, namely, that all intermediate metabolites used in active reactions must be produced in increasing concentrations to prevent successive dilution from cell division, (c) the use of Satisfiability Modulo Theory solvers rather than Linear Programming solvers, because our approach cannot be formulated as linear programming, (d) the use of Binary Decision Diagrams to produce an efficient implementation.ConclusionsOur method for generating minimal nutrient sets from the metabolic network and transporters of an organism combines linear constraint solving with binary decision diagrams to efficiently produce solution sets to provided growth problems.

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Enumerating Precursor Sets of Target Metabolites in a Metabolic Network

Cited by 24 publications

References 8 publications

Mapping the Inner Workings of the Microbiome: Genomic- and Metagenomic-Based Study of Metabolism and Metabolic Interactions in the Human Microbiome

Mapping the Inner Workings of the Microbiome: Genomic- and Metagenomic-Based Study of Metabolism and Metabolic Interactions in the Human Microbiome

An exact exponential-time algorithm for the Directed Maximum Leaf Spanning Tree problem

Computing minimal nutrient sets from metabolic networks via linear constraint solving

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