GEMSiRV: a software platform for GEnome-scale metabolic model simulation, reconstruction and visualization

Liao, Yu-Chieh; Tsai, Ming-Hsin; Chen, Feng‐Chi; Hsiung, Chao A.

doi:10.1093/bioinformatics/bts267

Cited by 40 publications

(18 citation statements)

References 29 publications

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“…Most importantly, PathwayBooster provides a logical visual representation of its results, significantly reducing the effort needed to identify enzyme misannotations and pathway holes. The information provided by PathwayBooster can be particularly useful when working with a platform for genome-scale model curation such as MEMOSys [ 17 ] or GEMSiRV [ 18 ]. Although several other tools exist to support comparative pathway analysis, PathwayBooster provides a unique combination of features that make it particularly suitable for use in model curation.…”

Section: Discussionmentioning

confidence: 99%

PathwayBooster: a tool to support the curation of metabolic pathways

et al. 2015

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BackgroundDespite several recent advances in the automated generation of draft metabolic reconstructions, the manual curation of these networks to produce high quality genome-scale metabolic models remains a labour-intensive and challenging task.ResultsWe present PathwayBooster, an open-source software tool to support the manual comparison and curation of metabolic models. It combines gene annotations from GenBank files and other sources with information retrieved from the metabolic databases BRENDA and KEGG to produce a set of pathway diagrams and reports summarising the evidence for the presence of a reaction in a given organism’s metabolic network. By comparing multiple sources of evidence within a common framework, PathwayBooster assists the curator in the identification of likely false positive (misannotated enzyme) and false negative (pathway hole) reactions. Reaction evidence may be taken from alternative annotations of the same genome and/or a set of closely related organisms.ConclusionsBy integrating and visualising evidence from multiple sources, PathwayBooster reduces the manual effort required in the curation of a metabolic model. The software is available online at http://www.theosysbio.bio.ic.ac.uk/resources/pathwaybooster/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-014-0447-2) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

PathwayBooster: a tool to support the curation of metabolic pathways

et al. 2015

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“…GEMs are the collection of bio-chemical reactions and associated genes, which indicate the existence of proteins in the target organism (Price et al, 2004; Liu et al, 2010). The manual construction of GEMs is time consuming and laborious, so different approaches have been generated to automate the reconstruction process (Henry et al, 2010; Liao et al, 2012; Agren et al, 2013). Among them, the RAVEN toolbox was recently described, which has the capability to reconstruct a model based on homology or sequence of the target organism (Agren et al, 2013).…”

Section: Genome-scale Metabolic Models: a Platform For Integration Ofmentioning

confidence: 99%

Elucidating the interactions between the human gut microbiota and its host through metabolic modeling

Shoaie

Nielsen

2014

Front. Genet.

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Increased understanding of the interactions between the gut microbiota, diet and environmental effects may allow us to design efficient treatment strategies for addressing global health problems. Existence of symbiotic microorganisms in the human gut provides different functions for the host such as conversion of nutrients, training of the immune system, and resistance to pathogens. The gut microbiome also plays an influential role in maintaining human health, and it is a potential target for prevention and treatment of common disorders including obesity, type 2 diabetes, and atherosclerosis. Due to the extreme complexity of such disorders, it is necessary to develop mathematical models for deciphering the role of its individual elements as well as the entire system and such models may assist in better understanding of the interactions between the bacteria in the human gut and the host by use of genome-scale metabolic models (GEMs). Recently, GEMs have been employed to explore the interactions between predominant bacteria in the gut ecosystems. Additionally, these models enabled analysis of the contribution of each species to the overall metabolism of the microbiota through the integration of omics data. The outcome of these studies can be used for proposing optimal conditions for desired microbiome phenotypes. Here, we review the recent progress and challenges for elucidating the interactions between the human gut microbiota and host through metabolic modeling. We discuss how these models may provide scaffolds for analyzing high-throughput data, developing probiotics and prebiotics, evaluating the effects of probiotics and prebiotics and eventually designing clinical interventions.

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“…Therefore, automation of the process has been of interest. A limited number of software tools for automated reconstruction are currently available (some examples are given in Table 2 ); for instance, AUTOGRAPH [ 26 ], GEMSiRV [ 27 ], MicrobesFlux [ 28 ], MetRxn [ 29 ], Model SEED [ 17 , 30 ], SuBliMinaL Toolbox [ 31 ], FAME [ 32 ], and RAVEN Toolbox [ 33 ] can be named. A systematic comparison between some of these platforms can be found in [ 34 ].…”

Section: Metabolic Network Model Reconstructionmentioning

confidence: 99%

Computational Approaches for Microalgal Biofuel Optimization: A Review

Koussa

Chaiboonchoe

Salehi‐Ashtiani

2014

BioMed Research International

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The increased demand and consumption of fossil fuels have raised interest in finding renewable energy sources throughout the globe. Much focus has been placed on optimizing microorganisms and primarily microalgae, to efficiently produce compounds that can substitute for fossil fuels. However, the path to achieving economic feasibility is likely to require strain optimization through using available tools and technologies in the fields of systems and synthetic biology. Such approaches invoke a deep understanding of the metabolic networks of the organisms and their genomic and proteomic profiles. The advent of next generation sequencing and other high throughput methods has led to a major increase in availability of biological data. Integration of such disparate data can help define the emergent metabolic system properties, which is of crucial importance in addressing biofuel production optimization. Herein, we review major computational tools and approaches developed and used in order to potentially identify target genes, pathways, and reactions of particular interest to biofuel production in algae. As the use of these tools and approaches has not been fully implemented in algal biofuel research, the aim of this review is to highlight the potential utility of these resources toward their future implementation in algal research.

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GEMSiRV: a software platform for GEnome-scale metabolic model simulation, reconstruction and visualization

Abstract: The software is freely available on the web at http://sb.nhri.org.tw/GEMSiRV.

Cited by 40 publications

References 29 publications

PathwayBooster: a tool to support the curation of metabolic pathways

PathwayBooster: a tool to support the curation of metabolic pathways

Elucidating the interactions between the human gut microbiota and its host through metabolic modeling

Computational Approaches for Microalgal Biofuel Optimization: A Review

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