Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0

Schellenberger, Jan; Que, Richard; Fleming, Ronan M. T.; Thiele, Ines; Orth, Jeffrey D.; Feist, Adam M.; Zielinski, Daniel C.; Bordbar, Aarash; Lewis, Nathan E.; Rahmanian, Sorena; Kang, Joseph; Hyduke, Daniel R.; Palsson, Bernhard Ø.

doi:10.1038/nprot.2011.308

Cited by 1,401 publications

(1,189 citation statements)

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“…22). New content can be added to an AGORA reconstruction manually or automatically, for example, using rBioNet 15 , which is compatible with the COBRA toolbox 8 and ensures all QC/QA measures defined by the community as described by Thiele and Palsson (2010) 9 . The "Feedback" tab provides contact information to the VMH developers regarding improvements to any resources available on the VMH webpage, where additions to any resource will be upheld by the VMH developers.…”

Section: Methodsmentioning

confidence: 99%

“…Based on lists of human gut microbes reported by Qin et al (2010) 30 and by Rajilić-Stojanović and de Vos (2014) 51 , we obtained 472 additional draft reconstructions from Model SEED and KBase (Department of Energy Systems Biology Knowledgebase, http://kbase.us), both of which use the RAST annotation server 52 to annotate the genomes and build the draft metabolic networks 14 . All reconstructions were downloaded in SBML format and imported into Matlab (Mathworks, Inc., Natick, MA, USA) using the COBRA Toolbox 8 . Each reconstruction was refined using the rBioNet extension 15 to the COBRA Toolbox.…”

Section: Methodsmentioning

confidence: 99%

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Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota

et al. 2016

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1 r e s o u r c eChanges in the composition of the human gut microbiota have been associated with the development of chronic diseases including type 2 diabetes, obesity, and colorectal cancer 1 . Gut bacterial functions, such as synthesis of amino acids and vitamins 2 , breakdown of indigestible plant polysaccharides 3 , and production of metabolites involved in energy metabolism 4 , have been linked to human health. The use of 'omics approaches to study human microbiome communities has led to the generation of enormous data sets whose interpretations require systems biology tools to shed light on the functional capacity of gut microbiomes and their interactions with the human host 5 .In order to infer the metabolic repertoire of a gut metagenome data set, researchers usually map sequenced genes or organisms onto metabolic networks derived from the Kyoto Encyclopedia of Genes and Genomes (KEGG) 6 , and functional annotations from KEGG ontologies 7 . However, this approach cannot identify the contribution of each bacterial species to the metabolic repertoire of the whole gut microbiome, nor can it infer the effects of different gut microbial communities on host metabolism.A technique that can bridge this gap is constraint-based reconstruction and analysis (COBRA) 8 using genome-scale metabolic reconstructions (GENREs) of individual human gut microbes. GENREs are assembled using the genome sequence and experimental information 9 . These reconstructions form the basis for the development of condition-specific metabolic models whose functions are simulated and validated by comparison with experimental results. The models can be used to investigate genotype-phenotype relationships 10 , microbe-microbe interactions 11 , and host-microbe interactions 11 . Numerous tools can be used to automatically generate draft GENREs but such models contain errors 12 and are incomplete.Manual curation of draft reconstructions is time consuming because it involves an extensive literature review and experimental validation of metabolic functions 9 .To provide an extensive resource of GENREs for human gut microbes, we developed a comparative metabolic reconstruction method that enables any refinement to one metabolic reconstruction to be propagated to others. This accelerates reconstruction and improves model quality. We generated AGORA, which includes 773 gut microbes, comprising 205 genera and 605 species. All reconstructions were based on literature-derived experimental data and comparative genomics. The metabolic predictions of two AGORA reconstructions and their derived metabolic models were validated against experimental data. RESULTS Metabolic reconstruction pipelineWe devised a comparative metabolic reconstruction method (Fig. 1a,c), which is analogous to the comparative microbial genome annotation approach 13 that has enabled accelerated annotation by propagation of refinements to one genome to others. First, we downloaded draft GENREs using Model SEED 14 and KBase (US Department of Energy Systems Biology Knowledgebase, http:/...

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota

et al. 2016

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“…This allowed us to use the ArtificialCentering Hit-and-Run sampler from the COBRA Toolbox for the sampling of metabolite concentration levels (Becker et al, 2007;Schellenberger et al, 2011b).…”

Section: Sampling Of Network Consistent Metabolite Concentration Levelsmentioning

confidence: 99%

Identification of metabolic engineering targets for the enhancement of 1,4-butanediol production in recombinant E. coli using large-scale kinetic models

Andreozzi

Chakrabarti

Soh

et al. 2016

Metabolic Engineering

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Identification of metabolic engineering targets for the enhancement of 1,4-butanediol production in recombinant E. coli using largescale kinetic models, Metabolic Engineering, http://dx.doi.org/10. 1016/j.ymben.2016.01.009 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Entities) to analyze the physiology of recombinant E. coli producing 1,4-butanediol (BDO) and to identify potential strategies for improved production of BDO. The framework allowed us to integrate data across multiple levels and to construct a population of large-scale kinetic models despite the lack of available information about kinetic properties of every enzyme in the metabolic pathways. We analyzed these models and we found that the enzymes that primarily control the fluxes leading to BDO production are part of central glycolysis, the lower branch of tricarboxylic acid (TCA) cycle and the novel BDO production route. Interestingly, among the enzymes between the glucose uptake and the BDO pathway, the enzymes belonging to the lower branch of TCA cycle have been identified as the most important for improving BDO production and yield. We also quantified the effects of changes of the target enzymes on other intracellular states like energy charge, cofactor levels, redox state, cellular growth, and byproduct formation.Independent earlier experiments on this strain confirmed that the computationally 3 obtained conclusions are consistent with the experimentally tested designs, and the findings of the present studies can provide guidance for future work on strain improvement. Overall, these studies demonstrate the potential and effectiveness of ORACLE for the accelerated design of microbial cell factories.

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“…Next, it generates a set of points from the solution space of the mass-balance (see (1)) and thermodynamic (see (2)) constraints, by means of a uniform sampling procedure. Specifically, we use an implementation of the ACHR sampler algorithm [10,21] to generate such a set of sampled solutions. This set will be used for computing the expected volume of a flux, using equation (4).…”

Section: Rmev-g: Reaction Minimizing Expected Volume Greedymentioning

confidence: 99%

“…This approach enables researchers to perform quantitative analysis within a validated mathematical model. For instance, COBRA [21] is a computational toolbox based on this approach, that enables researchers to model and infer metabolic networks. Metabolic networks can be defined by two types of constraints.…”

Section: Introductionmentioning

confidence: 99%

Flux Measurement Selection in Metabolic Networks

Megchelenbrink

Huynen

Marchiori

2011

Pattern Recognition in Bioinformatics

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Abstract. Genome-scale metabolic networks can be reconstructed using a constraint-based modeling approach. The stoichiometry of the network and the physiochemical laws still enable organisms to achieve certain objectives -such as biomass composition-through many various pathways. This means that the system is underdetermined and many alternative solutions exist. A known method used to reduce the number of alternative pathways is Flux Balance Analysis (FBA), which tries to optimize a given biological objective function. FBA does not always find a correct solution and for many networks the biological objective function is simply unknown. This leaves researchers no other choice than to measure certain fluxes. In this article we propose a method that combines a sampling approach with a greedy algorithm for finding a subset of k fluxes that, if measured, are expected to reduce as much as possible the solution space towards the 'true' flux distribution. The parameter k is given by the user. Application of the proposed method to a toy example and two real-life metabolic networks indicate its effectiveness. The method achieves significantly more reduction of the solution space than when k fluxes are selected either at random or by a faster simple heuristic procedure. It can be used for guiding the biologists to perform experimental analysis of metabolic networks.

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Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0

Cited by 1,401 publications

References 72 publications

Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota

Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota

Identification of metabolic engineering targets for the enhancement of 1,4-butanediol production in recombinant E. coli using large-scale kinetic models

Flux Measurement Selection in Metabolic Networks

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