scholarly journals Inspecting the Solution Space of Genome-Scale Metabolic Models

Metabolites ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 43
Author(s):  
Seyed Babak Loghmani ◽  
Nadine Veith ◽  
Sven Sahle ◽  
Frank T. Bergmann ◽  
Brett G. Olivier ◽  
...  
Keyword(s):  
Solution Space ◽  
Flux Variability Analysis ◽  
New Approach ◽  
Complementary Method ◽  
Kinetic Information ◽  
Integrative View ◽  
Scale Models ◽  
Balance Analysis ◽  
Metabolic Models ◽  
Genome Scale

Genome-scale metabolic models are frequently used in computational biology. They offer an integrative view on the metabolic network of an organism without the need to know kinetic information in detail. However, the huge solution space which comes with the analysis of genome-scale models by using, e.g., Flux Balance Analysis (FBA) poses a problem, since it is hard to thoroughly investigate and often only an arbitrarily selected individual flux distribution is discussed as an outcome of FBA. Here, we introduce a new approach to inspect the solution space and we compare it with other approaches, namely Flux Variability Analysis (FVA) and CoPE-FBA, using several different genome-scale models of lactic acid bacteria. We examine the extent to which different types of experimental data limit the solution space and how the robustness of the system increases as a result. We find that our new approach to inspect the solution space is a good complementary method that offers additional insights into the variance of biological phenotypes and can help to prevent wrong conclusions in the analysis of FBA results.

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

2019 ◽  
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.

Critical assessment of genome-scale metabolic models of Arabidopsis thaliana

2022 ◽  
Author(s):  
Javad Zamani ◽  
Sayed-Amir Marashi ◽  
Tahmineh Lohrasebi ◽  
Mohammad-Ali Malboobi ◽  
Esmail Foroozan
Keyword(s):  
Arabidopsis Thaliana ◽  
Flux Balance Analysis ◽  
Critical Assessment ◽  
Flux Balance ◽  
Balance Analysis ◽  
Metabolic Models ◽  
Living Organisms ◽  
Genome Scale

Genome-scale metabolic models (GSMMs) have enabled researchers to perform systems-level studies of living organisms. As a constraint-based technique, flux balance analysis (FBA) aids computation of reaction fluxes and prediction of...

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

2019 ◽  
Vol 9 (1) ◽  
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.

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

2019 ◽  
Author(s):  
Charles J Norsigian ◽  
Neha Pusarla ◽  
John Luke McConn ◽  
James T Yurkovich ◽  
Andreas Dräger ◽  
...  
Keyword(s):  
Phylogenetic Tree ◽  
Knowledge Base ◽  
The Past ◽  
Scale Models ◽  
Metabolic Models ◽  
New Community ◽  
Genome Annotations ◽  
Genome Scale ◽  
High Quality Genome ◽  
Strain Genome

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.

scholarly journals Towards scaling elementary flux mode computation

2019 ◽  
Vol 21 (6) ◽  
pp. 1875-1885
Author(s):  
Ehsan Ullah ◽  
Mona Yosafshahi ◽  
Soha Hassoun
Keyword(s):  
Pathway Analysis ◽  
Alternative Pathway ◽  
Solution Space ◽  
Computational Technique ◽  
Elementary Flux Mode ◽  
Scale Models ◽  
Network Compression ◽  
Genome Scale ◽  
The Impact ◽  
Flux Mode

Abstract While elementary flux mode (EFM) analysis is now recognized as a cornerstone computational technique for cellular pathway analysis and engineering, EFM application to genome-scale models remains computationally prohibitive. This article provides a review of aspects of EFM computation that elucidates bottlenecks in scaling EFM computation. First, algorithms for computing EFMs are reviewed. Next, the impact of redundant constraints, sensitivity to constraint ordering and network compression are evaluated. Then, the advantages and limitations of recent parallelization and GPU-based efforts are highlighted. The article then reviews alternative pathway analysis approaches that aim to reduce the EFM solution space. Despite advances in EFM computation, our review concludes that continued scaling of EFM computation is necessary to apply EFM to genome-scale models. Further, our review concludes that pathway analysis methods that target specific pathway properties can provide powerful alternatives to EFM analysis.

scholarly journals The ModelSEED Database for the integration of metabolic annotations and the reconstruction, comparison, and analysis of metabolic models for plants, fungi, and microbes

2020 ◽  
Author(s):  
Samuel M. D. Seaver ◽  
Filipe Liu ◽  
Qizhi Zhang ◽  
James Jeffryes ◽  
José P. Faria ◽  
...  
Keyword(s):  
Metabolic Pathways ◽  
Draft Genome ◽  
Biochemical Network ◽  
Plant Genomes ◽  
Flux Balance ◽  
Rosetta Stone ◽  
Balance Analysis ◽  
Comparison And Analysis ◽  
Metabolic Models ◽  
Genome Scale

ABSTRACTFor over ten years, ModelSEED has been a primary resource for the construction of draft genome-scale metabolic models based on annotated microbial or plant genomes. Now being released, the biochemistry database serves as the foundation of biochemical data underlying ModelSEED and KBase. The biochemistry database embodies several properties that, taken together, distinguish it from other published biochemistry resources by: (i) including compartmentalization, transport reactions, charged molecules and proton balancing on reactions;; (ii) being extensible by the user community, with all data stored in GitHub; and (iii) design as a biochemical “Rosetta Stone” to facilitate comparison and integration of annotations from many different tools and databases. The database was constructed by combining chemical data from many resources, applying standard transformations, identifying redundancies, and computing thermodynamic properties. The ModelSEED biochemistry is continually tested using flux balance analysis to ensure the biochemical network is modeling-ready and capable of simulating diverse phenotypes. Ontologies can be designed to aid in comparing and reconciling metabolic reconstructions that differ in how they represent various metabolic pathways. ModelSEED now includes 33,978 compounds and 36,645 reactions, available as a set of extensible files on GitHub, and available to search at https://modelseed.org and KBase.

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

2018 ◽  
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.

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

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.

scholarly journals Modeling the Differences in Biochemical Capabilities ofPseudomonasSpecies by Flux Balance Analysis: How Good Are Genome-Scale Metabolic Networks at Predicting the Differences?

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Parizad Babaei ◽  
Tahereh Ghasemi-Kahrizsangi ◽  
Sayed-Amir Marashi
Keyword(s):  
In Silico ◽  
Metabolic Networks ◽  
Closely Related Species ◽  
Systematic Analysis ◽  
Reconstruction Process ◽  
Flux Balance ◽  
Wide Range ◽  
Balance Analysis ◽  
Metabolic Models ◽  
Genome Scale

To date, several genome-scale metabolic networks have been reconstructed. These models cover a wide range of organisms, from bacteria to human. Such models have provided us with a framework for systematic analysis of metabolism. However, little effort has been put towards comparing biochemical capabilities of closely related species using their metabolic models. The accuracy of a model is highly dependent on the reconstruction process, as some errors may be included in the model during reconstruction. In this study, we investigated the ability of threePseudomonasmetabolic models to predict the biochemical differences, namely, iMO1086, iJP962, and iSB1139, which are related toP. aeruginosaPAO1,P. putidaKT2440, andP. fluorescensSBW25, respectively. We did a comprehensive literature search for previous works containing biochemically distinguishable traits over these species. Amongst more than 1700 articles, we chose a subset of them which included experimental results suitable forin silicosimulation. By simulating the conditions provided in the actual biological experiment, we performed case-dependent tests to compare thein silicoresults to the biological ones. We found out that iMO1086 and iJP962 were able to predict the experimental data and were much more accurate than iSB1139.

scholarly journals GEMtractor: Extracting Views into Genome-scale Metabolic Models

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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