scholarly journals Estimation of biomass composition from genomic and transcriptomic information

2016 ◽  
Vol 13 (2) ◽  
pp. 1-14 ◽  
Author(s):  
Sophia Santos ◽  
Isabel Rocha
Keyword(s):  
Experimental Data ◽  
Amino Acids ◽  
Metabolic Model ◽  
Good Alternative ◽  
Biomass Composition ◽  
Biomass Equation ◽  
Transcriptomic Data ◽  
Balance Analysis ◽  
User Friendly ◽  
Genome Information

SummaryGiven the great potential impact of the growing number of complete genome-scale metabolic network reconstructions of microorganisms, bioinformatics tools are needed to simplify and accelerate the course of knowledge in this field. One essential component of a genomescale metabolic model is its biomass equation, whose maximization is one of the most common objective functions used in Flux Balance Analysis formulations. Some components of biomass, such as amino acids and nucleotides, can be estimated from genome information, providing reliable data without the need of performing lab experiments. In this work a java tool is proposed that estimates microbial biomass composition in amino acids and nucleotides, from genome and transcriptomic information, using as input files sequences in FASTA format and files with transcriptomic data in the csv format. This application allows to obtain the results rapidly and is also a user-friendly tool for users with any or little background in informatics (http://darwin.di.uminho.pt/biomass/). The results obtained using this tool are fairly close to experimental data, showing that the estimation of amino acid and nucleotide compositions from genome information and from transcriptomic data is a good alternative when no experimental data is available.

scholarly journals Genome-scale metabolic reconstruction and metabolic versatility of an obligate methanotrophMethylococcus capsulatusstr. Bath

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6685 ◽  
Author(s):  
Ankit Gupta ◽  
Ahmad Ahmad ◽  
Dipesh Chothwe ◽  
Midhun K. Madhu ◽  
Shireesh Srivastava ◽  
...  
Keyword(s):  
Amino Acids ◽  
Nitrogen Sources ◽  
Metabolic Model ◽  
Metabolic Reconstruction ◽  
Methylococcus Capsulatus ◽  
Double Knockout ◽  
Metabolic Genes ◽  
Metabolic Versatility ◽  
Balance Analysis ◽  
Genome Scale

The increase in greenhouse gases with high global warming potential such as methane is a matter of concern and requires multifaceted efforts to reduce its emission and increase its mitigation from the environment. Microbes such as methanotrophs can assist in methane mitigation. To understand the metabolic capabilities of methanotrophs, a complete genome-scale metabolic model (GSMM) of an obligate methanotroph,Methylococcus capsulatusstr. Bath was reconstructed. The model contains 535 genes, 899 reactions and 865 metabolites and is namediMC535. The predictive potential of the model was validated using previously-reported experimental data. The model predicted the Entner–Duodoroff pathway to be essential for the growth of this bacterium, whereas the Embden–Meyerhof–Parnas pathway was found non-essential. The performance of the model was simulated on various carbon and nitrogen sources and found thatM. capsulatuscan grow on amino acids. The analysis of network topology of the model identified that six amino acids were in the top-ranked metabolic hubs. Using flux balance analysis, 29% of the metabolic genes were predicted to be essential, and 76 double knockout combinations involving 92 unique genes were predicted to be lethal. In conclusion, we have reconstructed a GSMM of a methanotrophMethylococcus capsulatusstr. Bath. This is the first high quality GSMM of a Methylococcus strain which can serve as an important resource for further strain-specific models of the Methylococcus genus, as well as identifying the biotechnological potential ofM. capsulatusBath.

scholarly journals The Metano Modeling Toolbox MMTB: An Intuitive, Web-Based Toolbox Introduced by Two Use Cases

Metabolites ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 113
Author(s):  
Julia Koblitz ◽  
Sabine Will ◽  
S. Riemer ◽  
Thomas Ulas ◽  
Meina Neumann-Schaal ◽  
...  
Keyword(s):  
Metabolic Networks ◽  
Point Of View ◽  
Use Cases ◽  
Mathematical Methods ◽  
State Behavior ◽  
Web Based ◽  
Research Fields ◽  
Balance Analysis ◽  
Metabolic Models ◽  
User Friendly

Genome-scale metabolic models are of high interest in a number of different research fields. Flux balance analysis (FBA) and other mathematical methods allow the prediction of the steady-state behavior of metabolic networks under different environmental conditions. However, many existing applications for flux optimizations do not provide a metabolite-centric view on fluxes. Metano is a standalone, open-source toolbox for the analysis and refinement of metabolic models. While flux distributions in metabolic networks are predominantly analyzed from a reaction-centric point of view, the Metano methods of split-ratio analysis and metabolite flux minimization also allow a metabolite-centric view on flux distributions. In addition, we present MMTB (Metano Modeling Toolbox), a web-based toolbox for metabolic modeling including a user-friendly interface to Metano methods. MMTB assists during bottom-up construction of metabolic models by integrating reaction and enzymatic annotation data from different databases. Furthermore, MMTB is especially designed for non-experienced users by providing an intuitive interface to the most commonly used modeling methods and offering novel visualizations. Additionally, MMTB allows users to upload their models, which can in turn be explored and analyzed by the community. We introduce MMTB by two use cases, involving a published model of Corynebacterium glutamicum and a newly created model of Phaeobacter inhibens.

scholarly journals Detection of Colistin Resistance in Carbapenem Resistant Enterobacteriaceae by Reference Broth Microdilution and Comparative Evaluation of Three Other Methods

2021 ◽  
Author(s):  
Punyatoya Kar ◽  
Bijayini Behera ◽  
Srujana Mohanty ◽  
Jayanti Jena ◽  
Ashoka Mahapatra
Keyword(s):  
Susceptibility Testing ◽  
Good Alternative ◽  
Broth Microdilution ◽  
Major Error ◽  
Colistin Resistance ◽  
Carbapenem Resistant ◽  
Agar Dilution ◽  
Moderate Sensitivity ◽  
Phenotypic Methods ◽  
User Friendly

Abstract Objective Challenges in susceptibility testing of colistin along with increase in the prevalence of colistin-resistant carbapenemase-producing Enterobacteriaceae (CRE) pathogens needs addressal. Evaluation of user-friendly methods is necessary as an alternative to broth microdilution (BMD), the reference susceptibility testing method, for routine implementation in diagnostic clinical microbiology laboratories. Genotypic detection of the plasmid-mediated colistin resistance is also needed for infection control purposes. Materials and Methods Colistin susceptibility of 200 nonduplicate clinical CRE isolates from December 2017 to June 2019 was determined by BMD, agar dilution (AD), E test, and rapid polymyxin NP test and interpreted as per the European Committee on Antimicrobial Susceptibility Testing. The results of AD, E test, and NP test were compared with that of BMD, considering minimal inhibitory concentration (MIC) ≤ 2 µg/mL as susceptible and > 2 µg/mL as resistant. Presence of any plasmid-mediated colistin resistance (mcr-1 and 2) was evaluated in 27 colistin-resistant CRE isolates by polymerase chain reaction. Statistical Analysis Performance of different phenotypic methods was analyzed by comparing MIC results of AD and E test with that of reference BMD method. Agreement between BMD and the other two methods was expressed in terms of categorical agreement and essential agreement. Errors were expressed as very major error (VME: false-susceptible) and major error (ME: false-resistance) by AD/E test. VME and ME of 3% disagreement were considered unacceptable. Results Colistin resistance was found in 27 (13.5%) isolates by BMD method. The VME rates of both AD (11%) and E test (37%) could not meet the Clinical and Laboratory Standards Institute recommendation (< 3% VME rate is acceptable) as alternative tests to the reference BMD. Colistin NP test showed sensitivity and specificity of 85% and 98%, respectively. The percentage discordant result in NP test was highest in Enterobacter spp. (17%). None of the 27 colistin resistant isolates showed presence of mcr-1 and mcr-2 genes. Conclusion High VME rate in AD and E tests precludes their use as alternatives to BMD for colistin susceptibility testing. NP test with moderate sensitivity but excellent specificity can be a good alternative for testing colistin susceptibility in CRE isolates, except in Enterobacter spp. Absence of mcr-1 and mcr-2 gene necessitates the exploration of other mechanisms of colistin resistance.

scholarly journals Physicochemical and metabolic constraints for thermodynamics-based stoichiometric modelling under mesophilic growth conditions

2020 ◽  
Author(s):  
Claudio Tomi-Andrino ◽  
Rupert Norman ◽  
Thomas Millat ◽  
Philippe Soucaille ◽  
Klaus Winzer ◽  
...  
Keyword(s):  
Experimental Data ◽  
Metabolic Flux Analysis ◽  
In Silico ◽  
Physicochemical Parameters ◽  
Metabolic Flux ◽  
Living Systems ◽  
Flux Analysis ◽  
Flux Balance ◽  
Metabolic Fluxes ◽  
Balance Analysis

AbstractMetabolic engineering in the post-genomic era is characterised by the development of new methods for metabolomics and fluxomics, supported by the integration of genetic engineering tools and mathematical modelling. Particularly, constraint-based stoichiometric models have been widely studied: (i) flux balance analysis (FBA) (in silico), and (ii) metabolic flux analysis (MFA) (in vivo). Recent studies have enabled the incorporation of thermodynamics and metabolomics data to improve the predictive capabilities of these approaches. However, an in-depth comparison and evaluation of these methods is lacking. This study presents a thorough analysis of two different in silico methods tested against experimental data (metabolomics and 13C-MFA) for the mesophile Escherichia coli. In particular, a modified version of the recently published matTFA toolbox was created, providing a broader range of physicochemical parameters. Validating against experimental data allowed the determination of the best physicochemical parameters to perform the TFA (Thermodynamics-based Flux Analysis). An analysis of flux pattern changes in the central carbon metabolism between 13C-MFA and TFA highlighted the limited capabilities of both approaches for elucidating the anaplerotic fluxes. In addition, a method based on centrality measures was suggested to identify important metabolites that (if quantified) would allow to further constrain the TFA. Finally, this study emphasised the need for standardisation in the fluxomics community: novel approaches are frequently released but a thorough comparison with currently accepted methods is not always performed.Author summaryBiotechnology has benefitted from the development of high throughput methods characterising living systems at different levels (e.g. concerning genes or proteins), allowing the industrial production of chemical commodities. Recently, focus has been placed on determining reaction rates (or metabolic fluxes) in the metabolic network of certain microorganisms, in order to identify bottlenecks hindering their exploitation. Two main approaches are commonly used, termed metabolic flux analysis (MFA) and flux balance analysis (FBA), based on measuring and estimating fluxes, respectively. While the influence of thermodynamics in living systems was accepted several decades ago, its application to study biochemical networks has only recently been enabled. In this sense, a multitude of different approaches constraining well-established modelling methods with thermodynamics has been suggested. However, physicochemical parameters are generally not properly adjusted to the experimental conditions, which might affect their predictive capabilities. In this study, we have explored the reliability of currently available tools by investigating the impact of varying said parameters in the simulation of metabolic fluxes and metabolite concentration values. Additionally, our in-depth analysis allowed us to highlight limitations and potential solutions that should be considered in future studies.

Transcriptomics Analysis using Galaxy and other Online Servers for Rheumatoid Arthritis

2021 ◽  
Vol 9 (VII) ◽  
pp. 459-466
Author(s):  
Sourabh Parmar
Keyword(s):  
Rheumatoid Arthritis ◽  
Data Mining ◽  
Gene Ontology ◽  
Complex Disease ◽  
Differential Expression Analysis ◽  
Biological Data ◽  
Systemic Autoimmune Disease ◽  
Transcriptomic Data ◽  
Pathway Enrichment ◽  
User Friendly

Researchers use transcriptomics analyses for biological data mining, interpretation, and presentation. Galaxy-based tools are utilized to analyze various complex disease transcriptomic data to understand the pathogenesis of the disease, which are user-friendly. This work provides simple methods for differential expression analysis and analysis of these results in gene ontology and pathway enrichment tools like David, WebGestalt. This method is very effective in better analysis and understanding the transcriptomic data. Transcriptomics analysis has been made on rheumatoid arthritis sra data. Rheumatoid arthritis (RA) is a systemic autoimmune disease. T cells and autoantibodies mediate the pathogenesis. This article discusses the genes which are differentially expressed between the healthy (n=50) and diseased (n=51) and the functions of those genes in the pathogenesis of RA.

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 FastMM: an efficient toolbox for personalized constraint-based metabolic modeling

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Gong-Hua Li ◽  
Shaoxing Dai ◽  
Feifei Han ◽  
Wenxing Li ◽  
Jingfei Huang ◽  
...  
Keyword(s):  
Flux Balance Analysis ◽  
Large Scale ◽  
Computing Time ◽  
Metabolic Modeling ◽  
The Cancer Genome Atlas ◽  
Flux Balance ◽  
Mcmc Sampling ◽  
Genome Wide ◽  
Balance Analysis ◽  
User Friendly

Abstract Background Constraint-based metabolic modeling has been applied to understand metabolism related disease mechanisms, to predict potential new drug targets and anti-metabolites, and to identify biomarkers of complex diseases. Although the state-of-art modeling toolbox, COBRA 3.0, is powerful, it requires substantial computing time conducting flux balance analysis, knockout analysis, and Markov Chain Monte Carlo (MCMC) sampling, which may limit its application in large scale genome-wide analysis. Results Here, we rewrote the underlying code of COBRA 3.0 using C/C++, and developed a toolbox, termed FastMM, to effectively conduct constraint-based metabolic modeling. The results showed that FastMM is 2~400 times faster than COBRA 3.0 in performing flux balance analysis and knockout analysis and returns consistent outputs. When applied to MCMC sampling, FastMM is 8 times faster than COBRA 3.0. FastMM is also faster than some efficient metabolic modeling applications, such as Cobrapy and Fast-SL. In addition, we developed a Matlab/Octave interface for fast metabolic modeling. This interface was fully compatible with COBRA 3.0, enabling users to easily perform complex applications for metabolic modeling. For example, users who do not have deep constraint-based metabolic model knowledge can just type one command in Matlab/Octave to perform personalized metabolic modeling. Users can also use the advance and multiple threading parameters for complex metabolic modeling. Thus, we provided an efficient and user-friendly solution to perform large scale genome-wide metabolic modeling. For example, FastMM can be applied to the modeling of individual cancer metabolic profiles of hundreds to thousands of samples in the Cancer Genome Atlas (TCGA). Conclusion FastMM is an efficient and user-friendly toolbox for large-scale personalized constraint-based metabolic modeling. It can serve as a complementary and invaluable improvement to the existing functionalities in COBRA 3.0. FastMM is under GPL license and can be freely available at GitHub site: https://github.com/GonghuaLi/FastMM.

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