BiGG Models 2020: multi-strain genome-scale models and expansion across the phylogenetic tree

Abstract The BiGG Models knowledge base (http://bigg.ucsd.edu) is a centralized repository for high-quality genome-scale metabolic models. For the past 12 years, the website has allowed users to browse and search metabolic models. Within this update, we detail new content and features in the repository, continuing the original effort to connect each model to genome annotations and external databases as well as standardization of reactions and metabolites. We describe the addition of 31 new models that expand the portion of the phylogenetic tree covered by BiGG Models. We also describe new functionality for hosting multi-strain models, which have proven to be insightful in a variety of studies centered on comparisons of related strains. Finally, the models in the knowledge base have been benchmarked using Memote, a new community-developed validator for genome-scale models to demonstrate the improving quality and transparency of model content in BiGG Models.

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Deciphering the metabolic capabilities of Bifidobacteria using genome-scale metabolic models

Scientific Reports ◽

10.1038/s41598-019-54696-9 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 4

Author(s):

N. T. Devika ◽

Karthik Raman

Keyword(s):

Short Chain Fatty Acids ◽

Acetate Production ◽

Scale Models ◽

Powerful Approach ◽

Metabolic Models ◽

Commercial Applications ◽

Different Strains ◽

Genome Scale ◽

Modelling Approach

AbstractBifidobacteria, the initial colonisers of breastfed infant guts, are considered as the key commensals that promote a healthy gastrointestinal tract. However, little is known about the key metabolic differences between different strains of these bifidobacteria, and consequently, their suitability for their varied commercial applications. In this context, the present study applies a constraint-based modelling approach to differentiate between 36 important bifidobacterial strains, enhancing their genome-scale metabolic models obtained from the AGORA (Assembly of Gut Organisms through Reconstruction and Analysis) resource. By studying various growth and metabolic capabilities in these enhanced genome-scale models across 30 different nutrient environments, we classified the bifidobacteria into three specific groups. We also studied the ability of the different strains to produce short-chain fatty acids, finding that acetate production is niche- and strain-specific, unlike lactate. Further, we captured the role of critical enzymes from the bifid shunt pathway, which was found to be essential for a subset of bifidobacterial strains. Our findings underline the significance of analysing metabolic capabilities as a powerful approach to explore distinct properties of the gut microbiome. Overall, our study presents several insights into the nutritional lifestyles of bifidobacteria and could potentially be leveraged to design species/strain-specific probiotics or prebiotics.

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Fast automated reconstruction of genome-scale metabolic models for microbial species and communities

10.1101/223198 ◽

2018 ◽

Cited By ~ 2

Author(s):

Daniel Machado ◽

Sergej Andrejev ◽

Melanie Tramontano ◽

Kiran Raosaheb Patil

Keyword(s):

Microbial Communities ◽

Single Species ◽

Model Organisms ◽

Universal Model ◽

Microbial Species ◽

Scale Models ◽

Metabolic Models ◽

User Friendly ◽

Genome Scale ◽

Automated Tool

AbstractGenome-scale metabolic models are instrumental in uncovering operating principles of cellular metabolism and model-guided re-engineering. Recent applications of metabolic models have also demonstrated their usefulness in unraveling cross-feeding within microbial communities. Yet, the application of genome-scale models, especially to microbial communities, is lagging far behind the availability of sequenced genomes. This is largely due to the time-consuming steps of manual cura-tion required to obtain good quality models and thus physiologically meaningful simulation results. Here, we present an automated tool – CarveMe – for reconstruction of species and community level metabolic models. We introduce the concept of a universal model, which is manually curated and simulation-ready. Starting with this universal model and annotated genome sequences, CarveMe uses a top-down approach to build single-species and community models in a fast and scalable manner. We build reconstructions for two model organisms, Escherichia coli and Bacillus subtillis, as well as a collection of human gut bacteria, and show that CarveMe models perform similarly to manually curated models in reproducing experimental phenotypes. Finally, we demonstrate the scalability of CarveMe through reconstructing 5587 bacterial models. Overall, CarveMe provides an open-source and user-friendly tool towards broadening the use of metabolic modeling in studying microbial species and communities.

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Efficient enzyme coupling algorithms identify functional pathways in genome-scale metabolic models

10.1101/608430 ◽

2019 ◽

Author(s):

Dikshant Pradhan ◽

Jason A. Papin ◽

Paul A. Jensen

Keyword(s):

Metabolic Engineering ◽

Mathematical Framework ◽

Continuous Variables ◽

Convex Constraints ◽

Coupling Analysis ◽

Flux Coupling ◽

Scale Models ◽

Metabolic Models ◽

Genome Scale ◽

Flux Coupling Analysis

AbstractFlux coupling identifies sets of reactions whose fluxes are “coupled" or correlated in genome-scale models. By identified sets of coupled reactions, modelers can 1.) reduce the dimensionality of genome-scale models, 2.) identify reactions that must be modulated together during metabolic engineering, and 3.) identify sets of important enzymes using high-throughput data. We present three computational tools to improve the efficiency, applicability, and biological interpretability of flux coupling analysis.The first algorithm (cachedFCF) uses information from intermediate solutions to decrease the runtime of standard flux coupling methods by 10-100 fold. Importantly, cachedFCF makes no assumptions regarding the structure of the underlying model, allowing efficient flux coupling analysis of models with non-convex constraints.We next developed a mathematical framework (FALCON) that incorporates enzyme activity as continuous variables in genome-scale models. Using data from gene expression and fitness assays, we verified that enzyme sets calculated directly from FALCON models are more functionally coherent than sets of enzymes collected from coupled reaction sets.Finally, we present a method (delete-and-couple) for expanding enzyme sets to allow redundancies and branches in the associated metabolic pathways. The expanded enzyme sets align with known biological pathways and retain functional coherence. The expanded enzyme sets allow pathway-level analyses of genome-scale metabolic models.Together, our algorithms extend flux coupling techniques to enzymatic networks and models with transcriptional regulation and other non-convex constraints. By expanding the efficiency and flexibility of flux coupling, we believe this popular technique will find new applications in metabolic engineering, microbial pathogenesis, and other fields that leverage network modeling.

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GEMtractor: Extracting Views into Genome-scale Metabolic Models

10.1101/790725 ◽

2019 ◽

Author(s):

Martin Scharm ◽

Olaf Wolkenhauer ◽

Mahdi Jalili ◽

Ali Salehzadeh-Yazdi

Keyword(s):

Topological Analysis ◽

Web Based ◽

Link Type ◽

Scale Models ◽

Multipartite Graphs ◽

Metabolic Models ◽

Genome Scale

ABSTRACTSummaryComputational metabolic models typically encode for graphs of species, reactions, and enzymes. Comparing genome-scale models through topological analysis of multipartite graphs is challenging. However, in many practical cases it is not necessary to compare the full networks. The GEMtractor is a web-based tool to trim models encoded in SBML. It can be used to extract subnetworks, for example focusing on reaction- and enzyme-centric views into the model.Availability and ImplementationThe GEMtractor is licensed under the terms of GPLv3 and developed at github.com/binfalse/GEMtractor – a public version is available at sbi.uni-rostock.de/[email protected] and [email protected]

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Integration of Time-Series Transcriptomic Data with Genome-Scale CHO Metabolic Models for mAb Engineering

Processes ◽

10.3390/pr8030331 ◽

2020 ◽

Vol 8 (3) ◽

pp. 331 ◽

Cited By ~ 4

Author(s):

Zhuangrong Huang ◽

Seongkyu Yoon

Keyword(s):

Cell Lines ◽

Chinese Hamster ◽

Parental Cell ◽

Process Conditions ◽

Parental Cell Line ◽

Specific Cell ◽

Scale Models ◽

Metabolic Models ◽

Genome Scale ◽

The Impact

Chinese hamster ovary (CHO) cells are the most commonly used cell lines in biopharmaceutical manufacturing. Genome-scale metabolic models have become a valuable tool to study cellular metabolism. Despite the presence of reference global genome-scale CHO model, context-specific metabolic models may still be required for specific cell lines (for example, CHO-K1, CHO-S, and CHO-DG44), and for specific process conditions. Many integration algorithms have been available to reconstruct specific genome-scale models. These methods are mainly based on integrating omics data (i.e., transcriptomics, proteomics, and metabolomics) into reference genome-scale models. In the present study, we aimed to investigate the impact of time points of transcriptomics integration on the genome-scale CHO model by assessing the prediction of growth rates with each reconstructed model. We also evaluated the feasibility of applying extracted models to different cell lines (generated from the same parental cell line). Our findings illustrate that gene expression at various stages of culture slightly impacts the reconstructed models. However, the prediction capability is robust enough on cell growth prediction not only across different growth phases but also in expansion to other cell lines.

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ErrorTracer: an algorithm for identifying the origins of inconsistencies in genome-scale metabolic models

Bioinformatics ◽

10.1093/bioinformatics/btz761 ◽

2019 ◽

Author(s):

Nikolay Martyushenko ◽

Eivind Almaas

Keyword(s):

Large Scale ◽

Work Flow ◽

Supplementary Information ◽

Scale Models ◽

Model Size ◽

Metabolic Models ◽

Model Publication ◽

Genome Scale ◽

Model Visualization ◽

Community Standard

Abstract Motivation The number and complexity of genome-scale metabolic models is steadily increasing, empowered by automated model-generation algorithms. The quality control of the models, however, has always remained a significant challenge, the most fundamental being reactions incapable of carrying flux. Numerous automated gap-filling algorithms try to address this problem, but can rarely resolve all of a model’s inconsistencies. The need for fast inconsistency checking algorithms has also been emphasized with the recent community push for automated model-validation before model publication. Previously, we wrote a graphical software to allow the modeller to solve the remaining errors manually. Nevertheless, model size and complexity remained a hindrance to efficiently tracking origins of inconsistency. Results We developed the ErrorTracer algorithm in order to address the shortcomings of existing approaches: ErrorTracer searches for inconsistencies, classifies them and identifies their origins. The algorithm is ∼2 orders of magnitude faster than current community standard methods, using only seconds even for large-scale models. This allows for interactive exploration in direct combination with model visualization, markedly simplifying the whole error-identification and correction work flow. Availability and implementation Windows and Linux executables and source code are available under the EPL 2.0 Licence at https://github.com/TheAngryFox/ModelExplorer and https://www.ntnu.edu/almaaslab/downloads. Supplementary information Supplementary data are available at Bioinformatics online.

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A workflow for generating multi-strain genome-scale metabolic models of prokaryotes

Nature Protocols ◽

10.1038/s41596-019-0254-3 ◽

2019 ◽

Vol 15 (1) ◽

pp. 1-14 ◽

Cited By ~ 6

Author(s):

Charles J. Norsigian ◽

Xin Fang ◽

Yara Seif ◽

Jonathan M. Monk ◽

Bernhard O. Palsson

Keyword(s):

Metabolic Models ◽

Genome Scale ◽

Strain Genome

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TEX-FBA: A constraint-based method for integrating gene expression, thermodynamics, and metabolomics data into genome-scale metabolic models

10.1101/536235 ◽

2019 ◽

Cited By ~ 3

Author(s):

Vikash Pandey ◽

Daniel Hernandez Gardiol ◽

Anush Chiappino-Pepe ◽

Vassily Hatzimanikatis

Keyword(s):

Gene Expression ◽

Experimental Data ◽

Solution Space ◽

Metabolic Model ◽

Scale Models ◽

Different Types ◽

Transcriptomics Data ◽

Metabolic Reactions ◽

Metabolic Models ◽

Genome Scale

AbstractA large number of genome-scale models of cellular metabolism are available for various organisms. These models include all known metabolic reactions based on the genome annotation. However, the reactions that are active are dependent on the cellular metabolic function or environmental condition. Constraint-based methods that integrate condition-specific transcriptomics data into models have been used extensively to investigate condition-specific metabolism. Here, we present a method (TEX-FBA) for modeling condition-specific metabolism that combines transcriptomics and reaction thermodynamics data to generate a thermodynamically-feasible condition-specific metabolic model. TEX-FBA is an extension of thermodynamic-based flux balance analysis (TFA), which allows the simultaneous integration of different stages of experimental data (e.g., absolute gene expression, metabolite concentrations, thermodynamic data, and fluxomics) and the identification of alternative metabolic states that maximize consistency between gene expression levels and condition-specific reaction fluxes. We applied TEX-FBA to a genome-scale metabolic model ofEscherichia coliby integrating available condition-specific experimental data and found a marked reduction in the flux solution space. Our analysis revealed a marked correlation between actual gene expression profile and experimental flux measurements compared to the one obtained from a randomly generated gene expression profile. We identified additional essential reactions from the membrane lipid and folate metabolism when we integrated transcriptomics data of the given condition on the top of metabolomics and thermodynamics data. These results show TEX-FBA is a promising new approach to study condition-specific metabolism when different types of experimental data are available.Author summaryCells utilize nutrients via biochemical reactions that are controlled by enzymes and synthesize required compounds for their survival and growth. Genome-scale models of metabolism representing these complex reaction networks have been reconstructed for a wide variety of organisms ranging from bacteria to human cells. These models comprise all possible biochemical reactions in a cell, but cells choose only a subset of reactions for their immediate needs and functions. Usually, these models allow for a large flux solution space and one can integrate experimental data in order to reduce it and potentially predict the physiology for a specific condition. We developed a method for integrating different types of omics data, such as fluxomics, transcriptomics, metabolomics into genome-scale metabolic models that reduces the flux solution space. Using gene expression data, the algorithm maximizes the consistency between the predicted and experimental flux for the reactions and predicts biologically relevant flux ranges for the remaining reactions in the network. This method is useful for determining fluxes of metabolic reactions with reduced uncertainty and suitable for performing context- and condition-specific analysis in metabolic models using different types of experimental data.

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GEMtractor: extracting views into genome-scale metabolic models

Bioinformatics ◽

10.1093/bioinformatics/btaa068 ◽

2020 ◽

Vol 36 (10) ◽

pp. 3281-3282

Author(s):

Martin Scharm ◽

Olaf Wolkenhauer ◽

Mahdi Jalili ◽

Ali Salehzadeh-Yazdi

Keyword(s):

Topological Analysis ◽

Web Based ◽

Scale Models ◽

Multipartite Graphs ◽

Metabolic Models ◽

Genome Scale

Abstract Summary Computational metabolic models typically encode for graphs of species, reactions and enzymes. Comparing genome-scale models through topological analysis of multipartite graphs is challenging. However, in many practical cases it is not necessary to compare the full networks. The GEMtractor is a web-based tool to trim models encoded in SBML. It can be used to extract subnetworks, for example focusing on reaction- and enzyme-centric views into the model. Availability and implementation The GEMtractor is licensed under the terms of GPLv3 and developed at github.com/binfalse/GEMtractor—a public version is available at sbi.uni-rostock.de/gemtractor.

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