Tapping the Wealth of Microbial Data in High-Throughput Metabolic Model Reconstruction

In the era of next-generation sequencing and ubiquitous assembly and binning of metagenomes, new putative genome sequences are being produced from isolate and microbiome samples at ever-increasing rates. Genome-scale metabolic models have enormous utility for supporting the analysis and predictive characterization of these genomes based on sequence data. As a result, tools for rapid automated reconstruction of metabolic models are becoming critically important for supporting the analysis of new genome sequences. Many tools and algorithms have now emerged to support rapid model reconstruction and analysis. Here, we are comparing and contrasting the capabilities and output of a variety of these tools, including ModelSEED, Raven Toolbox, PathwayTools, SuBliMinal Toolbox and merlin.

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Genome scale metabolic model reconstruction for the needs of industrial biotechnology

New Biotechnology ◽

10.1016/j.nbt.2016.06.847 ◽

2016 ◽

Vol 33 ◽

pp. S35 ◽

Cited By ~ 1

Author(s):

Mikko Arvas

Keyword(s):

Metabolic Model ◽

Industrial Biotechnology ◽

Model Reconstruction ◽

Genome Scale

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Addressing uncertainty in genome-scale metabolic model reconstruction and analysis

Genome Biology ◽

10.1186/s13059-021-02289-z ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

David B. Bernstein ◽

Snorre Sulheim ◽

Eivind Almaas ◽

Daniel Segrè

Keyword(s):

Metabolic Model ◽

Ensemble Modeling ◽

Model Reconstruction ◽

Predictive Capacity ◽

Sources Of Uncertainty ◽

Basic Understanding ◽

Biological Insight ◽

Integration Algorithms ◽

Genome Scale

AbstractThe 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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On the inconsistent treatment of gene-protein-reaction rules in context-specific metabolic models

10.1101/593277 ◽

2019 ◽

Author(s):

Miguel Ponce-de-León ◽

Iñigo Apaolaza ◽

Alfonso Valencia ◽

Francisco J. Planes

Keyword(s):

Gene Expression ◽

Metabolic Model ◽

Specific Model ◽

Omics Data ◽

Model Reconstruction ◽

Reconstruction Algorithms ◽

Reconstruction Methods ◽

Metabolic Models ◽

Context Specific ◽

Genome Scale

ABSTRACTWith the publication of high-quality genome-scale metabolic models for several organisms, the Systems Biology community has developed a number of algorithms for their analysis making use of ever growing –omics data. In particular, the reconstruction of the first genome-scale human metabolic model, Recon1, promoted the development of Context-Specific Model (CS-Model) reconstruction methods. This family of algorithms aims to identify the set of metabolic reactions that are active in a cell in a given condition using omics data, such as gene expression levels. Different CS-Model reconstruction algorithms have their own strengths and weaknesses depending on the problem under study and omics data available. However, after careful inspection, we found that all of these algorithms share common issues in the way GPR rules and gene expression data are treated. The first issue is related with how gapfilling reactions are managed after the reconstruction is conducted. The second issue concerns the molecular context, which is used to build the CS-model but neglected for posterior analyses. To evaluate the effect of these issues, we reconstructed ∼400 CS-Models of cancer cell lines and conducted gene essentiality analysis, using CRISPR–Cas9 essentiality data for validation purposes. Altogether, our results illustrate the importance of correcting the errors introduced during the GPR translation in many of the published metabolic reconstructions.

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Pantograph: A template-based method for genome-scale metabolic model reconstruction

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720015500067 ◽

2015 ◽

Vol 13 (02) ◽

pp. 1550006 ◽

Cited By ~ 13

Author(s):

Nicolas Loira ◽

Anna Zhukova ◽

David James Sherman

Keyword(s):

Metabolic Model ◽

Scale Model ◽

Model Reconstruction ◽

Specific Knowledge ◽

Manual Curation ◽

A Genome ◽

Iterative Improvement ◽

Genome Scale ◽

Genome Scale Model ◽

Template Reaction

Genome-scale metabolic models are a powerful tool to study the inner workings of biological systems and to guide applications. The advent of cheap sequencing has brought the opportunity to create metabolic maps of biotechnologically interesting organisms. While this drives the development of new methods and automatic tools, network reconstruction remains a time-consuming process where extensive manual curation is required. This curation introduces specific knowledge about the modeled organism, either explicitly in the form of molecular processes, or indirectly in the form of annotations of the model elements. Paradoxically, this knowledge is usually lost when reconstruction of a different organism is started. We introduce the Pantograph method for metabolic model reconstruction. This method combines a template reaction knowledge base, orthology mappings between two organisms, and experimental phenotypic evidence, to build a genome-scale metabolic model for a target organism. Our method infers implicit knowledge from annotations in the template, and rewrites these inferences to include them in the resulting model of the target organism. The generated model is well suited for manual curation. Scripts for evaluating the model with respect to experimental data are automatically generated, to aid curators in iterative improvement. We present an implementation of the Pantograph method, as a toolbox for genome-scale model reconstruction, curation and validation. This open source package can be obtained from: http://pathtastic.gforge.inria.fr .

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