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

Abstract For over 10 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/biochem and KBase.

Download Full-text

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

10.1101/2020.03.31.018663 ◽

2020 ◽

Cited By ~ 1

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.

Download Full-text

Critical assessment of genome-scale metabolic models of Arabidopsis thaliana

Molecular Omics ◽

10.1039/d1mo00351h ◽

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...

Download Full-text

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

The Scientific World JOURNAL ◽

10.1155/2014/416289 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 6

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.

Download Full-text

Metabolic engineering design to enhance (R,R)-2,3-butanediol production from glycerol in Bacillus subtilis based on flux balance analysis

Microbial Cell Factories ◽

10.1186/s12934-021-01688-y ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Nunthaphan Vikromvarasiri ◽

Tomokazu Shirai ◽

Akihiko Kondo

Keyword(s):

Flux Balance Analysis ◽

In Silico ◽

Metabolic Pathways ◽

In Silico Analysis ◽

Metabolic Model ◽

Fermentation Products ◽

Flux Balance ◽

Balance Analysis ◽

Chemical Products ◽

Genome Scale

Abstract Background Glycerol is a desirable alternative substrate for 2,3-butanediol (2,3-BD) production for sustainable development in biotechnological industries and non-food competitive feedstock. B. subtilis, a “generally recognized as safe” organism that is highly tolerant to fermentation products, is an ideal platform microorganism to engineer the pathways for the production of valuable bio-based chemicals, but it has never been engineered to improve 2,3-BD production from glycerol. In this study, we aimed to enhance 2,3-BD production from glycerol in B. subtilis through in silico analysis. Genome-scale metabolic model (GSM) simulations was used to design and develop the metabolic pathways of B. subtilis. Flux balance analysis (FBA) simulation was used to evaluate the effects of step-by-step gene knockouts to improve 2,3-BD production from glycerol in B. subtilis. Results B. subtilis was bioengineered to enhance 2,3-BD production from glycerol using FBA in a published GSM model of B. subtilis, iYO844. Four genes, ackA, pta, lctE, and mmgA, were knocked out step by step, and the effects thereof on 2,3-BD production were evaluated. While knockout of ackA and pta had no effect on 2,3-BD production, lctE knockout led to a substantial increase in 2,3-BD production. Moreover, 2,3-BD production was improved by mmgA knockout, which had never been investigated. In addition, comparisons between in silico simulations and fermentation profiles of all B. subtilis strains are presented in this study. Conclusions The strategy developed in this study, using in silico FBA combined with experimental validation, can be used to optimize metabolic pathways for enhanced 2,3-BD production from glycerol. It is expected to provide a novel platform for the bioengineering of strains to enhance the bioconversion of glycerol into other highly valuable chemical products.

Download Full-text

Understanding the host-microbe interactions using metabolic modeling

Microbiome ◽

10.1186/s40168-020-00955-1 ◽

2021 ◽

Vol 9 (1) ◽

Author(s):

Jack Jansma ◽

Sahar El Aidy

Keyword(s):

Human Health ◽

Flux Balance Analysis ◽

Bacterial Species ◽

Flux Balance ◽

Interaction Field ◽

In Silico Simulation ◽

Balance Analysis ◽

Microbe Interactions ◽

Host Microbe Interactions ◽

Genome Scale

AbstractThe human gut harbors an enormous number of symbiotic microbes, which is vital for human health. However, interactions within the complex microbiota community and between the microbiota and its host are challenging to elucidate, limiting development in the treatment for a variety of diseases associated with microbiota dysbiosis. Using in silico simulation methods based on flux balance analysis, those interactions can be better investigated. Flux balance analysis uses an annotated genome-scale reconstruction of a metabolic network to determine the distribution of metabolic fluxes that represent the complete metabolism of a bacterium in a certain metabolic environment such as the gut. Simulation of a set of bacterial species in a shared metabolic environment can enable the study of the effect of numerous perturbations, such as dietary changes or addition of a probiotic species in a personalized manner. This review aims to introduce to experimental biologists the possible applications of flux balance analysis in the host-microbiota interaction field and discusses its potential use to improve human health.

Download Full-text

Why does yeast ferment? A flux balance analysis study

Biochemical Society Transactions ◽

10.1042/bst0381225 ◽

2010 ◽

Vol 38 (5) ◽

pp. 1225-1229 ◽

Cited By ~ 23

Author(s):

Evangelos Simeonidis ◽

Ettore Murabito ◽

Kieran Smallbone ◽

Hans V. Westerhoff

Keyword(s):

Flux Balance Analysis ◽

Energy Cost ◽

Yeast Fermentation ◽

Minimal Amount ◽

Aerobic Growth ◽

Analysis Study ◽

Flux Balance ◽

Balance Analysis ◽

Genome Scale ◽

Excess Glucose

Advances in biological techniques have led to the availability of genome-scale metabolic reconstructions for yeast. The size and complexity of such networks impose limits on what types of analyses one can perform. Constraint-based modelling overcomes some of these restrictions by using physicochemical constraints to describe the potential behaviour of an organism. FBA (flux balance analysis) highlights flux patterns through a network that serves to achieve a particular objective and requires a minimal amount of data to make quantitative inferences about network behaviour. Even though FBA is a powerful tool for system predictions, its general formulation sometimes results in unrealistic flux patterns. A typical example is fermentation in yeast: ethanol is produced during aerobic growth in excess glucose, but this pattern is not present in a typical FBA solution. In the present paper, we examine the issue of yeast fermentation against respiration during growth. We have studied a number of hypotheses from the modelling perspective, and novel formulations of the FBA approach have been tested. By making the observation that more respiration requires the synthesis of more mitochondria, an energy cost related to mitochondrial synthesis is added to the FBA formulation. Results, although still approximate, are closer to experimental observations than earlier FBA analyses, at least on the issue of fermentation.

Download Full-text

Accelerating flux balance calculations in genome-scale metabolic models by localizing the application of loopless constraints

Bioinformatics ◽

10.1093/bioinformatics/bty446 ◽

2018 ◽

Cited By ~ 2

Author(s):

Siu H J Chan ◽

Lin Wang ◽

Satyakam Dash ◽

Costas D Maranas

Keyword(s):

Flux Balance ◽

Metabolic Models ◽

Genome Scale

Download Full-text

Core Metabolism Shifts during Growth on Methanol versus Methane in the Methanotroph Methylomicrobium buryatense 5GB1

mBio ◽

10.1128/mbio.00406-19 ◽

2019 ◽

Vol 10 (2) ◽

Cited By ~ 7

Author(s):

Yanfen Fu ◽

Lian He ◽

Jennifer Reeve ◽

David A. C. Beck ◽

Mary E. Lidstrom

Keyword(s):

Flux Balance Analysis ◽

Metabolic Pathways ◽

Scale Model ◽

Obligate Methylotroph ◽

Content Type ◽

Advantages And Disadvantages ◽

The Core ◽

Flux Balance ◽

Balance Analysis ◽

Core Metabolism

ABSTRACT Methylomicrobium buryatense 5GB1 is an obligate methylotroph which grows on methane or methanol with similar growth rates. It has long been assumed that the core metabolic pathways must be similar on the two substrates, but recent studies of methane metabolism in this bacterium suggest that growth on methanol might have significant differences from growth on methane. In this study, both a targeted metabolomics approach and a 13C tracer approach were taken to understand core carbon metabolism in M. buryatense 5GB1 during growth on methanol and to determine whether such differences occur. Our results suggest a systematic shift of active core metabolism in which increased flux occurred through both the Entner-Doudoroff (ED) pathway and the partial serine cycle, while the tricarboxylic acid (TCA) cycle was incomplete, in contrast to growth on methane. Using the experimental results as constraints, we applied flux balance analysis to determine the metabolic flux phenotype of M. buryatense 5GB1 growing on methanol, and the results are consistent with predictions based on ATP and NADH changes. Transcriptomics analysis suggested that the changes in fluxes and metabolite levels represented results of posttranscriptional regulation. The combination of flux balance analysis of the genome-scale model and the flux ratio from 13C data changed the solution space for a better prediction of cell behavior and demonstrated the significant differences in physiology between growth on methane and growth on methanol. IMPORTANCE One-carbon compounds such as methane and methanol are of increasing interest as sustainable substrates for biological production of fuels and industrial chemicals. The bacteria that carry out these conversions have been studied for many decades, but gaps exist in our knowledge of their metabolic pathways. One such gap is the difference between growth on methane and growth on methanol. Understanding such metabolism is important, since each has advantages and disadvantages as a feedstock for production of chemicals and fuels. The significance of our research is in the demonstration that the metabolic network is substantially altered in each case and in the delineation of these changes. The resulting new insights into the core metabolism of this bacterium now provide an improved basis for future strain design.

Download Full-text