scholarly journals Genome-scale metabolic modelling when changes in environmental conditions affect biomass composition

2021 ◽  
Vol 17 (5) ◽  
pp. e1008528
Author(s):  
Christian Schulz ◽  
Tjasa Kumelj ◽  
Emil Karlsen ◽  
Eivind Almaas
Keyword(s):  
Environmental Conditions ◽  
Model Performance ◽  
Optimal Growth ◽  
Metabolic Model ◽  
Biomass Composition ◽  
Metabolic Modelling ◽  
Composition Variation ◽  
Nutrient Environment ◽  
Genome Scale

Genome-scale metabolic modeling is an important tool in the study of metabolism by enhancing the collation of knowledge, interpretation of data, and prediction of metabolic capabilities. A frequent assumption in the use of genome-scale models is that the in vivo organism is evolved for optimal growth, where growth is represented by flux through a biomass objective function (BOF). While the specific composition of the BOF is crucial, its formulation is often inherited from similar organisms due to the experimental challenges associated with its proper determination. A cell’s macro-molecular composition is not fixed and it responds to changes in environmental conditions. As a consequence, initiatives for the high-fidelity determination of cellular biomass composition have been launched. Thus, there is a need for a mathematical and computational framework capable of using multiple measurements of cellular biomass composition in different environments. Here, we propose two different computational approaches for directly addressing this challenge: Biomass Trade-off Weighting (BTW) and Higher-dimensional-plane InterPolation (HIP). In lieu of experimental data on biomass composition-variation in response to changing nutrient environment, we assess the properties of BTW and HIP using three hypothetical, yet biologically plausible, BOFs for the Escherichia coli genome-scale metabolic model iML1515. We find that the BTW and HIP formulations have a significant impact on model performance and phenotypes. Furthermore, the BTW method generates larger growth rates in all environments when compared to HIP. Using acetate secretion and the respiratory quotient as proxies for phenotypic changes, we find marked differences between the methods as HIP generates BOFs more similar to a reference BOF than BTW. We conclude that the presented methods constitute a conceptual step in developing genome-scale metabolic modelling approaches capable of addressing the inherent dependence of cellular biomass composition on nutrient environments.

scholarly journals Genome-scale metabolic modelling when changes in environmental conditions affect biomass composition

2020 ◽  
Author(s):  
Christian Schulz ◽  
Tjaša Kumelj ◽  
Emil Karlsen ◽  
Eivind Almaas
Keyword(s):  
Environmental Conditions ◽  
Environmental Changes ◽  
Model Performance ◽  
Optimal Growth ◽  
Metabolic Model ◽  
Biomass Composition ◽  
Phenotypic Traits ◽  
Metabolic Modelling ◽  
Genome Scale

AbstractGenome-scale metabolic modeling is an important tool in understanding metabolism, by enhancing collation of knowledge, interpretation of data, and prediction of metabolic capabilities. A central assumption in the construction and use of genome-scale models is that the in vivo organism is evolved for optimal growth, where growth is represented by flux through a biomass objective function (BOF). While the specific composition of the BOF is crucial, its formulation is often inherited from similar organisms due to the experimental challenges associated with its proper determination.However, a cell’s macro-molecular composition is not fixed and it responds to changes in environmental conditions. As a consequence, initiatives for the high-fidelity determination of cellular biomass composition have been launched. Thus, there is a need for a mathematical and computational framework capable of using multiple measurements of cellular biomass composition in different environments. Here, we propose two different computational approaches for directly addressing this challenge: Biomass Trade-off Weighting (BTW) and Higher-dimensional-plane InterPolation (HIP).In lieu of experimental data on biomass composition-variation in response to changing nutrient environment, we assess the properties of BTW and HIP using three hypothetical, yet biologically plausible, BOFs for the Escherichia coli genome-scale metabolic model iML1515. We find that the BTW and HIP formulations have a significant impact on model performance and phenotypes. Furthermore, the BTW method generates larger growth rates in all environments when compared to HIP. Using acetate secretion and the respiratory quotient as proxies for phenotypic changes, we find marked differences between the methods as HIP generates BOFs more similar to a reference BOF than BTW. We conclude that the presented methods constitute a first conceptual step in developing genome-scale metabolic modelling approaches capable of addressing the inherent dependence of cellular biomass composition on nutrient environments.Author summaryChanges in the environment promote changes in an organism’s metabolism. To achieve balanced growth states for near-optimal function, cells respond through metabolic rearrangements, which may influence the biosynthesis of metabolic precursors for building a cell’s molecular constituents. Therefore, it is necessary to take the dependence of biomass composition on environmental conditions into consideration. While measuring the biomass composition for some environments is possible, and should be done, it cannot be completed for all possible environments.In this work, we propose two main approaches, BTW and HIP, for addressing the challenge of estimating biomass composition in response to environmental changes. We evaluate the phenotypic consequences of BTW and HIP by characterizing their effect on growth, secretion potential, respiratory efficiency, and gene essentiality of a cell.Our work constitutes a first conceptual step in accounting for the influence of growth conditions on biomass composition, and in turn the biomass composition’s effect on metabolic phenotypic traits, within constraint-based modelling. As such, we believe it will improve the relevance of constraint-based methods in metabolic engineering and drug discovery, since the biosynthetic potential of microbes for generating industrially relevant products or drugs often is closely linked to their biomass composition.

scholarly journals Modelling Gene-Protein-Reaction Associations on an FPGA

2019 ◽  
Author(s):  
Macauley Coggins
Keyword(s):  
Metabolic Model ◽  
Logic Device ◽  
Description Language ◽  
Metabolic Modelling ◽  
Quartus Ii ◽  
Hardware Description ◽  
Design Software ◽  
Genome Scale ◽  
The Relationship ◽  
Protein Reaction

Genome-Scale metabolic models have proven to be incredibly useful.Allowing researchers to model cellular functionality based upon gene expression. However as the number of genes and reactions increases it can become computationally demanding. The first step in genome-scale metabolic modelling is to model the relationship between genes and reactions in the form of Gene-Protein-Reaction Associations (GPRA). In this research we have developed a way to model GPRAs on an Altera Cyclone II FPGA using Quartus II programmable logic device design software and the VHDL hardware description language. The model consisting of 7 genes and 7 reactions was implemented using 7 combinational functions and 14 I/O pins. This model will be the first step towards creating a full genome scale metabolic model on FPGA devices which we will be fully investigating in future studies.

scholarly journals High-quality genome-scale metabolic model of Aurantiochytrium sp. T66

2020 ◽  
Author(s):  
Vetle Simensen ◽  
André Voigt ◽  
Eivind Almaas
Keyword(s):  
Primary Source ◽  
Metabolic Model ◽  
Cellular Growth ◽  
Fish Oils ◽  
Growth Data ◽  
High Quality ◽  
Cell Factories ◽  
Genome Scale ◽  
High Quality Genome

AbstractThe long-chain, ω-3 polyunsaturated fatty acids (PUFAs) eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) are essential for humans and animals, including marine fish species. Presently, the primary source of these PUFAs is fish oils. As the global production of fish oils appears to be reaching its limits, alternative sources of high-quality ω-3 PUFAs is paramount to support the growing aquaculture industry. Thraustochytrids are a group of heterotrophic protists able to synthesize and accrue large amounts of essential ω-3 PUFAs, including EPA and DHA. Thus, the thraustochytrids are prime candidates to solve the increasing demand for ω-3 PUFAs using microbial cell factories. However, a systems-level understanding of their metabolic shift from cellular growth into lipid accumulation is, to a large extent, unclear. Here, we reconstructed a high-quality genome-scale metabolic model of the thraustochytrid Aurantiochytrium sp. T66 termed iVS1191. Through iterative rounds of model refinement and extensive manual curation, we significantly enhanced the metabolic scope and coverage of the reconstruction from that of previously published models, making considerable improvements with stoichiometric consistency, metabolic connectivity, and model annotations. We show that iVS1191 is highly consistent with experimental growth data, reproducing in vivo growth phenotypes as well as specific growth rates on minimal carbon media. The availability of iVS1191 provides a solid framework for further developing our understanding of T66’s metabolic properties, as well as exploring metabolic engineering and process-optimization strategies in silico for increased ω-3 PUFA production.

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

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.

scholarly journals Modelling Gene-Protein-Reaction Associations on an FPGA

2019 ◽  
Author(s):  
Macauley Coggins
Keyword(s):  
Metabolic Model ◽  
Logic Device ◽  
Description Language ◽  
Metabolic Modelling ◽  
Quartus Ii ◽  
Hardware Description ◽  
Design Software ◽  
Genome Scale ◽  
The Relationship ◽  
Protein Reaction

AbstractGenome-Scale metabolic models have proven to be incredibly useful. Allowing researchers to model cellular functionality based upon gene expression. However as the number of genes and reactions increases it can become computationally demanding. The first step in genome-scale metabolic modelling is to model the relationship between genes and reactions in the form of Gene-Protein-Reaction Associations (GPRA). In this research we have developed a way to model GPRAs on an Altera Cyclone II FPGA using Quartus II programmable logic device design software and the VHDL hardware description language. The model consisting of 7 genes and 7 reactions was implemented using 7 combinational functions and 14 I/O pins. This model will be the first step towards creating a full genome scale metabolic model on FPGA devices which we will be fully investigating in future studies.

scholarly journals An integrated computational and experimental study to elucidate Staphylococcus aureus metabolism

2019 ◽  
Author(s):  
Mohammad Mazharul Islam ◽  
Vinai C. Thomas ◽  
Matthew Van Beek ◽  
Jong-Sam Ahn ◽  
Abdulelah A. Alqarzaee ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
Environmental Changes ◽  
Model Performance ◽  
Metabolic Model ◽  
Exchange Reactions ◽  
Chemical Balance ◽  
Manual Curation ◽  
Specific Regulation ◽  
Reaction Stoichiometry

AbstractStaphylococcus aureus is a metabolically versatile pathogen that colonizes nearly all organs of the human body. A detailed and comprehensive knowledge of staphylococcal metabolism is essential to understanding its pathogenesis. To this end, we have reconstructed and experimentally validated an updated and enhanced genome-scale metabolic model of S. aureus USA300_FPR3757. The model combined genome annotation data, reaction stoichiometry, and regulation information from biochemical databases and previous strain-specific models. Reactions in the model were checked and fixed to ensure chemical balance and thermodynamic consistency. To further refine the model, growth assessment of 1920 non-essential mutants from the Nebraska Transposon Mutant Library was performed and metabolite excretion profiles of important mutants in carbon and nitrogen metabolism were determined. The growth and no-growth inconsistencies between the model predictions and in vivo essentiality data were resolved using extensive manual curation based on optimization-based reconciliation algorithms. Upon intensive curation and refinements, the model contains 840 metabolic genes, 1442 metabolites, and 1566 reactions including transport and exchange reactions. To improve the accuracy and predictability of the model to environmental changes, condition-specific regulation information curated from the existing knowledgebase was incorporated. These critical additions improved the model performance significantly in capturing gene essentiality, substrate utilization, and metabolite production capabilities and increased the ability to generate model-based discoveries of therapeutic significance. Use of this highly curated model will enhance the functional utility of omics data and, therefore, serve as a resource to support future investigations of S. aureus and to augment staphylococcal research worldwide.

scholarly journals Integrative Gene Expression and Metabolic Analysis tool IgemRNA

2021 ◽  
Author(s):  
Kristina Grausa ◽  
Karlis Pleiko ◽  
Ivars Mozga ◽  
Agris Pentjuss
Keyword(s):  
Transcriptome Analysis ◽  
Network Connectivity ◽  
Enrichment Analysis ◽  
Metabolic Model ◽  
Gene Set Enrichment Analysis ◽  
Analysis Tool ◽  
Transcriptome Data ◽  
Metabolic Modelling ◽  
Analysis Methods ◽  
Genome Scale

Genome scale metabolic modelling is widely used technique to research metabolism impacts on organism properties. Additional omics data integration enables a more precise genotype-phenotype analysis for biotechnology, medicine and life sciences. Transcriptome data amounts rapidly increase each year. Many transcriptome analysis tools with integrated genome scale metabolic modelling are proposed. But these tools have own restrictions, compatibility issues and the necessity of previous experience and advanced user skills. We have analysed and classified published tools, summarized possible transcriptome pre-processing, and analysis methods and implemented them in the new transcriptome analysis tool IgemRNA. Tool novelty is the possibility of transcriptomics data pre-processing approach, analysis of transcriptome with or without genome scale metabolic models and different thresholding and gene mapping approach availability. In comparison with usual Gene set enrichment analysis methods, IgemRNA options provide additional transcriptome data validation, where minimal metabolic network connectivity and flux requirements are met. IgemRNA allows to process transcriptome datasets, compare data between different phenotypes, execute multiple analysis and data filtering functions. All this is done via graphical user interface. IgemRNA is compatible with Cobra Toolbox 3.0 and uses some of its functions for genome scale metabolic model optimization tasks. IgemRNA is open access software available at https://github.com/BigDataInSilicoBiologyGroup/IgemRNA.

Genome-Scale Metabolic Model as a Virtual Platform to Reveal the Environmental Contribution of Methanogens

2017 ◽  
Vol 6 (2) ◽  
pp. 149-160 ◽  
Author(s):  
P. Chellapandi ◽  
M. Bharathi ◽  
R. Prathiviraj ◽  
R. Sasikala ◽  
M. Vikraman
Keyword(s):  
Metabolic Model ◽  
Environmental Contribution ◽  
Virtual Platform ◽  
Genome Scale
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