PathwayBooster: a tool to support the curation of metabolic pathways

Liberal, Rodrigo; Lisowska, Beata K; Leak, David J.; Pinney, John W.

doi:10.1186/s12859-014-0447-2

Cited by 6 publications

(4 citation statements)

References 19 publications

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“…Tools like MetaNetX [134] simplify curation efforts and suggest identifier mappings to unify reactions and metabolites identifiers. Such curation efforts are likely to become even more important in the near future given the recent advances of automated reconstruction tools, now able to generate a full working draft of a model, e.g., MicrobesFlux [135], Pathway Tools [136], PathwayBooster [137], CoReCo [138], and Merlin [139]. Given the importance of obtaining reconciled and high-quality models, MetaNetX also provides a database of models generated after reconciliation of metabolites and biochemical reactions [134].…”

Section: 15 Years Of Human Metabolic Modellingmentioning

confidence: 99%

Human Systems Biology and Metabolic Modelling: A Review—From Disease Metabolism to Precision Medicine

Angione

2019

BioMed Research International

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In cell and molecular biology, metabolism is the only system that can be fully simulated at genome scale. Metabolic systems biology offers powerful abstraction tools to simulate all known metabolic reactions in a cell, therefore providing a snapshot that is close to its observable phenotype. In this review, we cover the 15 years of human metabolic modelling. We show that, although the past five years have not experienced large improvements in the size of the gene and metabolite sets in human metabolic models, their accuracy is rapidly increasing. We also describe how condition-, tissue-, and patient-specific metabolic models shed light on cell-specific changes occurring in the metabolic network, therefore predicting biomarkers of disease metabolism. We finally discuss current challenges and future promising directions for this research field, including machine/deep learning and precision medicine. In the omics era, profiling patients and biological processes from a multiomic point of view is becoming more common and less expensive. Starting from multiomic data collected from patients and N-of-1 trials where individual patients constitute different case studies, methods for model-building and data integration are being used to generate patient-specific models. Coupled with state-of-the-art machine learning methods, this will allow characterizing each patient’s disease phenotype and delivering precision medicine solutions, therefore leading to preventative medicine, reduced treatment, andin silicoclinical trials.

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Section: 15 Years Of Human Metabolic Modellingmentioning

confidence: 99%

Human Systems Biology and Metabolic Modelling: A Review—From Disease Metabolism to Precision Medicine

Angione

2019

BioMed Research International

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“…The presented genome-scale metabolic reconstruction of P. thermoglucosidasius NCIMB 11955 is based on genome sequencing by ERGO™ Integrated Genomics 57 and Sheng et al 58 . Genome annotation was performed through the ERGO™ Integrated Genomics suite 57 and the RAST annotation server 59 , followed by gap filling with Pathway Booster 60 . The reconstruction was extensively manually curated using available literature and databases (KEGG, BRENDA, MetaCyc, MetaNetX and EC2PDB), according to benchmark approaches 61 .…”

Section: Resultsmentioning

confidence: 99%

“…Genome annotation was performed through the ERGOTM Integrated Genomics suite 57 and the RAST server 96 . Pathway Booster was used for gap filling, resolving gaps through comparisons with evolutionarily-related genomes 60 . Upon base construction of the model, further manual curation was done following standard procedures 61 .…”

Section: Methodsmentioning

confidence: 99%

Genome-scale metabolic modelling ofP. thermoglucosidasiusNCIMB 11955 reveals metabolic bottlenecks in anaerobic metabolism

Mol

Bennett

Sánchez

et al. 2021

Preprint

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Parageobacillus thermoglucosidasius represents a thermophilic, facultative anaerobic bacterial chassis, with several desirable traits for metabolic engineering and industrial production. To further optimize strain productivity, a systems level understanding of its metabolism is needed, which can be facilitated by a genome-scale metabolic model. Here, we present p-thermo, the most complete, curated and validated genome-scale model (to date) of Parageobacillus thermoglucosidasius NCIMB 11955. It spans a total of 890 metabolites, 1175 reactions and 917 metabolic genes, forming an extensive knowledge base for P. thermoglucosidasius NCIMB 11955 metabolism. The model accurately predicts aerobic utilization of 22 carbon sources, and the predictive quality of internal fluxes was validated with previously published 13C-fluxomics data. In an application case, p-thermo was used to facilitate more in-depth analysis of reported metabolic engineering efforts, giving additional insight into fermentative metabolism. Finally, p-thermo was used to resolve a previously uncharacterised bottleneck in anaerobic metabolism, by identifying the minimal required supplemented nutrients (thiamin, biotin and iron(III)) needed to sustain anaerobic growth. This highlights the usefulness of p-thermo for guiding the generation of experimental hypotheses and for facilitating data-driven metabolic engineering, expanding the use of P. thermoglucosidasius as a high yield production platform.

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“…This variability can be attributed to the limited accuracy of homology-based methods [ 28 ], misannotations present in large databases [ 29 ], the fact that many genes can only be annotated as hypothetical sequences of unknown function [ 30 , 31 ], and the high fraction of “orphan” enzyme functions that cannot be mapped to a particular genome sequence [ 32 ]. Some, but not all, of this variability can be mitigated by combining multiple databases to increase the coverage of annotation when reconstructing a GEM [ 33 , 34 ]. Furthermore, annotation for GEM reconstruction has an added layer of complexity beyond mapping genes to general ontologies or homologs.…”

Section: Genome Annotationmentioning

confidence: 99%

Addressing uncertainty in genome-scale metabolic model reconstruction and analysis

et al. 2021

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The reconstruction and analysis of genome-scale metabolic models constitutes a powerful systems biology approach, with applications ranging from basic understanding of genotype-phenotype mapping to solving biomedical and environmental problems. However, the biological insight obtained from these models is limited by multiple heterogeneous sources of uncertainty, which are often difficult to quantify. Here we review the major sources of uncertainty and survey existing approaches developed for representing and addressing them. A unified formal characterization of these uncertainties through probabilistic approaches and ensemble modeling will facilitate convergence towards consistent reconstruction pipelines, improved data integration algorithms, and more accurate assessment of predictive capacity.

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PathwayBooster: a tool to support the curation of metabolic pathways

Cited by 6 publications

References 19 publications

Human Systems Biology and Metabolic Modelling: A Review—From Disease Metabolism to Precision Medicine

Human Systems Biology and Metabolic Modelling: A Review—From Disease Metabolism to Precision Medicine

Genome-scale metabolic modelling ofP. thermoglucosidasiusNCIMB 11955 reveals metabolic bottlenecks in anaerobic metabolism

Addressing uncertainty in genome-scale metabolic model reconstruction and analysis

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