scholarly journals BioISO: an objective-oriented application for assisting the curation of genome-scale metabolic models

2021 ◽  
Author(s):  
Fernando Cruz ◽  
João Capela ◽  
Eugénio C. Ferreira ◽  
Miguel Rocha ◽  
Oscar Dias
Keyword(s):  
Biological Networks ◽  
In Silico ◽  
Metabolic Model ◽  
Gap Filling ◽  
Search Spaces ◽  
Link Type ◽  
Manual Curation ◽  
Balance Analysis ◽  
Metabolic Models ◽  
Genome Scale

AbstractAs the reconstruction of Genome-Scale Metabolic Models becomes standard practice in systems biology, the number of organisms having at least one metabolic model at the genome-scale is peaking at an unprecedented scale. The automation of several laborious tasks, such as gap-finding and gap-filling, allowed to develop GSMMs for poorly described organisms. However, such models’ quality can be compromised by the automation of several steps, which may lead to erroneous phenotype simulations.The Biological networks constraint-based In Silico Optimization (BioISO) is a computational tool aimed at accelerating the reconstruction of Genome-Scale Metabolic Models. This tool facilitates the manual curation steps by reducing the large search spaces often met when debugging in silico biological models. BioISO uses a recursive relation-like algorithm and Flux Balance Analysis to evaluate and guide debugging of in silico phenotype simulations. The potential of BioISO to guide the debugging of model reconstructions was showcased using GSMMs available in literature and compared with the results of two other state-of-the-art gap-filling tools (Meneco and fastGapFill). Furthermore, BioISO was used as Meneco’s gap-finding algorithm to reduce the number of proposed solutions (reaction sets) for filling the gaps.BioISO was implemented as a webserver available at https://bioiso.bio.di.uminho.pt; and integrated into merlin as a plugin. BioISO’s implementation as a Python™ package can also be retrieved from https://github.com/BioSystemsUM/BioISO.

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 Drought Stress Responses in Context-Specific Genome-Scale Metabolic Models of Arabidopsis thaliana

Metabolites ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 159
Author(s):  
Ratklao Siriwach ◽  
Fumio Matsuda ◽  
Kentaro Yano ◽  
Masami Yokota Hirai
Keyword(s):  
Arabidopsis Thaliana ◽  
Drought Stress ◽  
Stress Responses ◽  
In Silico ◽  
Metabolic Model ◽  
Metabolic Reaction ◽  
A Genome ◽  
Balance Analysis ◽  
Context Specific ◽  
Genome Scale

Drought perturbs metabolism in plants and limits their growth. Because drought stress on crops affects their yields, understanding the complex adaptation mechanisms evolved by plants against drought will facilitate the development of drought-tolerant crops for agricultural use. In this study, we examined the metabolic pathways of Arabidopsis thaliana which respond to drought stress by omics-based in silico analyses. We proposed an analysis pipeline to understand metabolism under specific conditions based on a genome-scale metabolic model (GEM). Context-specific GEMs under drought and well-watered control conditions were reconstructed using transcriptome data and examined using metabolome data. The metabolic fluxes throughout the metabolic network were estimated by flux balance analysis using the context-specific GEMs. We used in silico methods to identify an important reaction contributing to biomass production and clarified metabolic reaction responses under drought stress by comparative analysis between drought and control conditions. This proposed pipeline can be applied in other studies to understand metabolic changes under specific conditions using Arabidopsis GEM or other available plant GEMs.

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

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.

scholarly journals Systematically gap-filling the genome-scale metabolic model of CHO cells

2020 ◽  
Author(s):  
Hamideh Fouladiha ◽  
Sayed-Amir Marashi ◽  
Shangzhong Li ◽  
Zerong Li ◽  
Helen O. Masson ◽  
...  
Keyword(s):  
Cho Cells ◽  
Predictive Power ◽  
Cell Metabolism ◽  
Chinese Hamster ◽  
Metabolic Model ◽  
Gap Filling ◽  
Cell Factories ◽  
New Genes ◽  
Metabolic Models ◽  
Genome Scale

AbstractObjectiveChinese hamster ovary (CHO) cells are the leading cell factories for producing recombinant proteins in the biopharmaceutical industry. In this regard, constraint-based metabolic models are useful platforms to perform computational analysis of cell metabolism. These models need to be regularly updated in order to include the latest biochemical data of the cells, and to increase their predictive power. Here, we provide an update to iCHO1766, the metabolic model of CHO cells.ResultsWe expanded the existing model of Chinese hamster metabolism with the help of four gap-filling approaches, leading to the addition of 773 new reactions and 335 new genes. We incorporated these into an updated genome-scale metabolic network model of CHO cells, named iCHO2101. In this updated model, the number of reactions and pathways capable of carrying flux is substantially increased.ConclusionsThe present CHO model is an important step towards more complete metabolic models of CHO cells.

scholarly journals Evaluation of a Genome-ScaleIn SilicoMetabolic Model for Geobacter metallireducens by Using Proteomic Data from a Field Biostimulation Experiment

2012 ◽  
Vol 78 (24) ◽  
pp. 8735-8742 ◽  
Author(s):  
Yilin Fang ◽  
Michael J. Wilkins ◽  
Steven B. Yabusaki ◽  
Mary S. Lipton ◽  
Philip E. Long
Keyword(s):  
Proteomic Analysis ◽  
In Silico ◽  
Field Experiments ◽  
Metabolic Model ◽  
Geobacter Metallireducens ◽  
Content Type ◽  
Proteomic Data ◽  
Subsurface Environment ◽  
A Genome ◽  
Genome Scale

ABSTRACTAccurately predicting the interactions between microbial metabolism and the physical subsurface environment is necessary to enhance subsurface energy development, soil and groundwater cleanup, and carbon management. This study was an initial attempt to confirm the metabolic functional roles within anin silicomodel using environmental proteomic data collected during field experiments. Shotgun global proteomics data collected during a subsurface biostimulation experiment were used to validate a genome-scale metabolic model ofGeobacter metallireducens—specifically, the ability of the metabolic model to predict metal reduction, biomass yield, and growth rate under dynamic field conditions. The constraint-basedin silicomodelof G. metallireducensrelates an annotated genome sequence to the physiological functions with 697 reactions controlled by 747 enzyme-coding genes. Proteomic analysis showed that 180 of the 637G. metallireducensproteins detected during the 2008 experiment were associated with specific metabolic reactions in thein silicomodel. When the field-calibrated Fe(III) terminal electron acceptor process reaction in a reactive transport model for the field experiments was replaced with the genome-scale model, the model predicted that the largest metabolic fluxes through thein silicomodel reactions generally correspond to the highest abundances of proteins that catalyze those reactions. Central metabolism predicted by the model agrees well with protein abundance profiles inferred from proteomic analysis. Model discrepancies with the proteomic data, such as the relatively low abundances of proteins associated with amino acid transport and metabolism, revealed pathways or flux constraints in thein silicomodel that could be updated to more accurately predict metabolic processes that occur in the subsurface environment.

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

2019 ◽  
Vol 9 (1) ◽  
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.

scholarly journals Biochemical Characteristics and a Genome-Scale Metabolic Model of an Indian Euryhaline Cyanobacterium with High Polyglucan Content

Metabolites ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 177 ◽  
Author(s):  
Ahmad Ahmad ◽  
Ruchi Pathania ◽  
Shireesh Srivastava
Keyword(s):  
Biomass Accumulation ◽  
Metabolic Model ◽  
Metabolic Characteristics ◽  
Protein Levels ◽  
A Genome ◽  
Lower Protein ◽  
Balance Analysis ◽  
Synechococcus Sp ◽  
The Difference ◽  
Genome Scale

Marine cyanobacteria are promising microbes to capture and convert atmospheric CO2 and light into biomass and valuable industrial bio-products. Yet, reports on metabolic characteristics of non-model cyanobacteria are scarce. In this report, we show that an Indian euryhaline Synechococcus sp. BDU 130192 has biomass accumulation comparable to a model marine cyanobacterium and contains approximately double the amount of total carbohydrates, but significantly lower protein levels compared to Synechococcus sp. PCC 7002 cells. Based on its annotated chromosomal genome sequence, we present a genome scale metabolic model (GSMM) of this cyanobacterium, which we have named as iSyn706. The model includes 706 genes, 908 reactions, and 900 metabolites. The difference in the flux balance analysis (FBA) predicted flux distributions between Synechococcus sp. PCC 7002 and Synechococcus sp. BDU130192 strains mimicked the differences in their biomass compositions. Model-predicted oxygen evolution rate for Synechococcus sp. BDU130192 was found to be close to the experimentally-measured value. The model was analyzed to determine the potential of the strain for the production of various industrially-useful products without affecting growth significantly. This model will be helpful to researchers interested in understanding the metabolism as well as to design metabolic engineering strategies for the production of industrially-relevant compounds.

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