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

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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iEC7871 Quercus suber model: the first multi-tissue diel cycle genome-scale metabolic model of a woody tree

10.1101/2021.03.09.434537 ◽

2021 ◽

Author(s):

Emanuel Cunha ◽

Miguel Silva ◽

Ines Chaves ◽

Huseyin Demirci ◽

Davide Lagoa ◽

...

Keyword(s):

Metabolic Model ◽

Tissue Level ◽

Quercus Suber ◽

Biomass Composition ◽

Improve Model ◽

Transcriptomics Data ◽

Oak Tree ◽

Metabolic Models ◽

Genome Scale ◽

Metabolic Behaviour

AbstractIn the last decade, genome-scale metabolic models have been increasingly used to study plant metabolic behaviour at the tissue and multi-tissue level in different environmental conditions. Quercus suber (Q. suber), also known as the cork oak tree, is one of the most important forest communities of the Mediterranean/Iberian region. In this work, we present the genome-scale metabolic model of the Q. suber (iEC7871), the first of a woody plant. The metabolic model comprises 7871 genes, 6230 reactions, and 6481 metabolites across eight compartments. Transcriptomics data was integrated into the model to obtain tissue-specific models for the leaf, inner bark, and phellogen. Each tissue’s biomass composition was determined to improve model accuracy and merged into a diel multi-tissue metabolic model to predict interactions among the three tissues at the light and dark phases. The metabolic models were also used to analyze the pathways associated with the synthesis of suberin monomers. Nevertheless, the models developed in this work can provide insights about other aspects of the metabolism of Q. suber, such as its secondary metabolism and cork formation.

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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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Inspecting the Solution Space of Genome-Scale Metabolic Models

Metabolites ◽

10.3390/metabo12010043 ◽

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.

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Integrating thermodynamic and enzymatic constraints into genome-scale metabolic models

10.1101/2020.11.30.403519 ◽

2020 ◽

Author(s):

Xue Yang ◽

Zhitao Mao ◽

Xin Zhao ◽

Ruoyu Wang ◽

Peiji Zhang ◽

...

Keyword(s):

Solution Space ◽

Network Models ◽

Metabolic Model ◽

Cellular Growth ◽

Carbamoyl Phosphate ◽

Modeling Framework ◽

Application Potential ◽

Metabolic Models ◽

Thermodynamic Feasibility ◽

Genome Scale

AbstractStoichiometric genome-scale metabolic network models (GEMs) have been widely used to predict metabolic phenotypes. In addition to stoichiometric ratios, other constraints such as enzyme availability and thermodynamic feasibility can also limit the phenotype solution space. Extended GEM models considering either enzymatic or thermodynamic constraints have been shown to improve prediction accuracy. In this paper, we propose a novel method that integrates both enzymatic and thermodynamic constraints in a single Pyomo modeling framework (ETGEMs). We applied this method to construct the EcoETM, the E. coli metabolic model iML1515 with enzymatic and thermodynamic constraints. Using this model, we calculated the optimal pathways for cellular growth and the production of 22 metabolites. When comparing the results with those of iML1515 and models with one of the two constraints, we observed that many thermodynamically unfavorable and/or high enzyme cost pathways were excluded from EcoETM. For example, the synthesis pathway of carbamoyl-phosphate (Cbp) from iML1515 is both thermodynamically unfavorable and enzymatically costly. After introducing the new constraints, the production pathways and yields of several Cbp-derived products (e.g. L-arginine, orotate) calculated using EcoETM were more realistic. The results of this study demonstrate the great application potential of metabolic models with multiple constraints for pathway analysis and phenotype predication.

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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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BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data

PLoS Computational Biology ◽

10.1371/journal.pcbi.1006971 ◽

2019 ◽

Vol 15 (4) ◽

pp. e1006971 ◽

Cited By ~ 19

Author(s):

Jean-Christophe Lachance ◽

Colton J. Lloyd ◽

Jonathan M. Monk ◽

Laurence Yang ◽

Anand V. Sastry ◽

...

Keyword(s):

Experimental Data ◽

Objective Functions ◽

Metabolic Models ◽

Genome Scale

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Manually curated genome-scale reconstruction of the metabolic network of Bacillus megaterium DSM319

Scientific Reports ◽

10.1038/s41598-019-55041-w ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 3

Author(s):

Javad Aminian-Dehkordi ◽

Seyyed Mohammad Mousavi ◽

Arezou Jafari ◽

Ivan Mijakovic ◽

Sayed-Amir Marashi

Keyword(s):

Experimental Data ◽

Metabolic Network ◽

Bacillus Megaterium ◽

In Silico ◽

Metabolic Model ◽

Growth Behavior ◽

Industrial Biotechnology ◽

Flux Analysis ◽

A Genome ◽

Genome Scale

AbstractBacillus megaterium is a microorganism widely used in industrial biotechnology for production of enzymes and recombinant proteins, as well as in bioleaching processes. Precise understanding of its metabolism is essential for designing engineering strategies to further optimize B. megaterium for biotechnology applications. Here, we present a genome-scale metabolic model for B. megaterium DSM319, iJA1121, which is a result of a metabolic network reconciliation process. The model includes 1709 reactions, 1349 metabolites, and 1121 genes. Based on multiple-genome alignments and available genome-scale metabolic models for other Bacillus species, we constructed a draft network using an automated approach followed by manual curation. The refinements were performed using a gap-filling process. Constraint-based modeling was used to scrutinize network features. Phenotyping assays were performed in order to validate the growth behavior of the model using different substrates. To verify the model accuracy, experimental data reported in the literature (growth behavior patterns, metabolite production capabilities, metabolic flux analysis using 13C glucose and formaldehyde inhibitory effect) were confronted with model predictions. This indicated a very good agreement between in silico results and experimental data. For example, our in silico study of fatty acid biosynthesis and lipid accumulation in B. megaterium highlighted the importance of adopting appropriate carbon sources for fermentation purposes. We conclude that the genome-scale metabolic model iJA1121 represents a useful tool for systems analysis and furthers our understanding of the metabolism of B. megaterium.

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

Nucleic Acids Research ◽

10.1093/nar/gkz1054 ◽

2019 ◽

Cited By ~ 7

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.

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A metabolic reconstruction of Lactobacillus reuteri JCM 1112 and analysis of its potential as a cell factory

Microbial Cell Factories ◽

10.1186/s12934-019-1229-3 ◽

2019 ◽

Vol 18 (1) ◽

Cited By ~ 2

Author(s):

Thordis Kristjansdottir ◽

Elleke F. Bosma ◽

Filipe Branco dos Santos ◽

Emre Özdemir ◽

Markus J. Herrgård ◽

...

Keyword(s):

Experimental Data ◽

Metabolic Engineering ◽

Growth Rates ◽

Lactobacillus Reuteri ◽

Metabolic Model ◽

Metabolic Reconstruction ◽

Cell Factory ◽

A Genome ◽

A Cell ◽

Genome Scale

Abstract Background Lactobacillus reuteri is a heterofermentative Lactic Acid Bacterium (LAB) that is commonly used for food fermentations and probiotic purposes. Due to its robust properties, it is also increasingly considered for use as a cell factory. It produces several industrially important compounds such as 1,3-propanediol and reuterin natively, but for cell factory purposes, developing improved strategies for engineering and fermentation optimization is crucial. Genome-scale metabolic models can be highly beneficial in guiding rational metabolic engineering. Reconstructing a reliable and a quantitatively accurate metabolic model requires extensive manual curation and incorporation of experimental data. Results A genome-scale metabolic model of L. reuteri JCM 1112T was reconstructed and the resulting model, Lreuteri_530, was validated and tested with experimental data. Several knowledge gaps in the metabolism were identified and resolved during this process, including presence/absence of glycolytic genes. Flux distribution between the two glycolytic pathways, the phosphoketolase and Embden–Meyerhof–Parnas pathways, varies considerably between LAB species and strains. As these pathways result in different energy yields, it is important to include strain-specific utilization of these pathways in the model. We determined experimentally that the Embden–Meyerhof–Parnas pathway carried at most 7% of the total glycolytic flux. Predicted growth rates from Lreuteri_530 were in good agreement with experimentally determined values. To further validate the prediction accuracy of Lreuteri_530, the predicted effects of glycerol addition and adhE gene knock-out, which results in impaired ethanol production, were compared to in vivo data. Examination of both growth rates and uptake- and secretion rates of the main metabolites in central metabolism demonstrated that the model was able to accurately predict the experimentally observed effects. Lastly, the potential of L. reuteri as a cell factory was investigated, resulting in a number of general metabolic engineering strategies. Conclusion We have constructed a manually curated genome-scale metabolic model of L. reuteri JCM 1112T that has been experimentally parameterized and validated and can accurately predict metabolic behavior of this important platform cell factory.

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Towards scaling elementary flux mode computation

Briefings in Bioinformatics ◽

10.1093/bib/bbz094 ◽

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.

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