scholarly journals GPRuler: Metabolic gene-protein-reaction rules automatic reconstruction

2021 ◽  
Vol 17 (11) ◽  
pp. e1009550
Author(s):  
Marzia Di Filippo ◽  
Chiara Damiani ◽  
Dario Pescini
Keyword(s):  
Metabolic Network ◽  
De Novo ◽  
Homo Sapiens ◽  
Metabolic Model ◽  
Boolean Logic ◽  
Small Scale ◽  
Biological Databases ◽  
Scale Level ◽  
Reconstruction Process ◽  
Protein Reaction

Metabolic network models are increasingly being used in health care and industry. As a consequence, many tools have been released to automate their reconstruction process de novo. In order to enable gene deletion simulations and integration of gene expression data, these networks must include gene-protein-reaction (GPR) rules, which describe with a Boolean logic relationships between the gene products (e.g., enzyme isoforms or subunits) associated with the catalysis of a given reaction. Nevertheless, the reconstruction of GPRs still remains a largely manual and time consuming process. Aiming at fully automating the reconstruction process of GPRs for any organism, we propose the open-source python-based framework GPRuler. By mining text and data from 9 different biological databases, GPRuler can reconstruct GPRs starting either from just the name of the target organism or from an existing metabolic model. The performance of the developed tool is evaluated at small-scale level for a manually curated metabolic model, and at genome-scale level for three metabolic models related to Homo sapiens and Saccharomyces cerevisiae organisms. By exploiting these models as benchmarks, the proposed tool shown its ability to reproduce the original GPR rules with a high level of accuracy. In all the tested scenarios, after a manual investigation of the mismatches between the rules proposed by GPRuler and the original ones, the proposed approach revealed to be in many cases more accurate than the original models. By complementing existing tools for metabolic network reconstruction with the possibility to reconstruct GPRs quickly and with a few resources, GPRuler paves the way to the study of context-specific metabolic networks, representing the active portion of the complete network in given conditions, for organisms of industrial or biomedical interest that have not been characterized metabolically yet.

scholarly journals GPRuler: metabolic Gene-Protein-Reaction rules automatic reconstruction

2021 ◽  
Author(s):  
Marzia Di Filippo ◽  
Chiara Damiani ◽  
Dario Pescini
Keyword(s):  
Metabolic Network ◽  
De Novo ◽  
Homo Sapiens ◽  
Metabolic Model ◽  
Boolean Logic ◽  
Small Scale ◽  
Biological Databases ◽  
Scale Level ◽  
Reconstruction Process ◽  
Protein Reaction

AbstractBackgroundMetabolic network models are increasingly being used in health care and industry. As a consequence, many tools have been released to automate their reconstruction process de novo. In order to enable gene deletion simulations and integration of gene expression data, these networks must include gene-protein-reaction (GPR) rules, which describe with a Boolean logic relationships between the gene products (e.g., enzyme isoforms or subunits) associated with the catalysis of a given reaction. Nevertheless, the reconstruction of GPRs still remains a largely manual and time consuming process. Aiming at fully automating the reconstruction process of GPRs for any organism, we propose the open-source python-based framework GPRuler.ResultsBy mining text and data from 9 different biological databases, GPRuler can reconstruct GPRs starting either from just the name of the target organism or from an existing metabolic model. The performance of the developed tool is evaluated at small-scale level for a manually curated metabolic model, and at genome-scale level for three metabolic models related to Homo sapiens and Saccharomyces cerevisiae organisms. By exploiting these models as benchmarks, the proposed tool shown its ability to reproduce the original GPR rules with a high level of accuracy. In all the tested scenarios, after a manual investigation of the mismatches between the rules proposed by GPRuler and the original ones, the proposed approach revealed to be in many cases more accurate than the original models.ConclusionsBy complementing existing tools for metabolic network reconstruction with the possibility to reconstruct GPRs quickly and with a few resources, GPRuler paves the way to the study of context-specific metabolic networks, representing the active portion of the complete network in given conditions, for organisms of industrial or biomedical interest that have not been characterized metabolically yet.

scholarly journals Accurate long-read de novo assembly evaluation with Inspector

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yu Chen ◽  
Yixin Zhang ◽  
Amy Y. Wang ◽  
Min Gao ◽  
Zechen Chong
Keyword(s):  
Genome Assembly ◽  
De Novo Assembly ◽  
In Silico ◽  
Large Scale ◽  
De Novo ◽  
Small Scale ◽  
De Novo Genome Assembly ◽  
Consensus Sequences ◽  
Assembly Evaluation ◽  
Long Read

AbstractLong-read de novo genome assembly continues to advance rapidly. However, there is a lack of effective tools to accurately evaluate the assembly results, especially for structural errors. We present Inspector, a reference-free long-read de novo assembly evaluator which faithfully reports types of errors and their precise locations. Notably, Inspector can correct the assembly errors based on consensus sequences derived from raw reads covering erroneous regions. Based on in silico and long-read assembly results from multiple long-read data and assemblers, we demonstrate that in addition to providing generic metrics, Inspector can accurately identify both large-scale and small-scale assembly errors.

Studies on Processing of Aloe vera (L.) Burm.f. (Ghrit- kumari) Leaves

2016 ◽  
Vol 23 (1) ◽  
pp. 13-16
Author(s):  
Kaushal Kumar ◽  
S. Abbas
Keyword(s):  
Good Condition ◽  
Aloe Vera ◽  
Storage Condition ◽  
Cold Season ◽  
Market Potential ◽  
Small Scale ◽  
Pharmaceutical Companies ◽  
Scale Level ◽  
Processed Material ◽  
Natural Preservatives

Indeed, there are great market potential of Aloe vera (L.) Burm.f. (Ghrit-kumari) due to their medicinal and cosmetic values. However, the serious problems faced by farmers related to supply of harvested leaves materials after cultivation for industrial extraction and production of juice under a short and specific time of transportation to avoid spoil of materials and protect from decayed condition of leaves. Hence, the cultivation practices of the plant are not being popular although the utilization of leaves by pharmaceutical companies is gradually increasing. To solve the above problems, an analysis on extraction of pulp from leaves and preparation of juice in a small scale level has been carried out useful for the farmers and medicinal plant growers for processing of leaves at plantation site. It has been observed that after harvesting of leaves the boil or warm water is useful to process the leaves for pulp extraction in cold season. The standard percentage of activated charcoal observed in processing of pulp for removal of pigments varies from 12-15 gm/ kg according to pulp. The processing in homogenization, filtration and better preservation and stability of juice different ratio have been analyzed for keeping in good condition up to 180 days and above and the requirement of EDTA is 0.9-1.2gm/kg of pulp. The citric acid required 1.1-1.3 gm/kg for better and good condition of juice. The maintenances of PH of processed material various composition of different preservatives have been assessed. Effects of natural preservatives like lemon and honey to protect juice up to 30 days has been studied and found at least 10:5 ml/100ml respectively in closed container under good storage condition.

scholarly journals Periodic catastrophes over human evolutionary history are necessary to explain the forager population paradox

2019 ◽  
Vol 116 (26) ◽  
pp. 12758-12766 ◽  
Author(s):  
Michael D. Gurven ◽  
Raziel J. Davison
Keyword(s):  
Evolutionary History ◽  
Homo Sapiens ◽  
Vital Rate ◽  
Small Scale ◽  
Human Populations ◽  
Wild Chimpanzee ◽  
Population Fluctuations ◽  
Vital Rates ◽  
Zero Population Growth ◽  
Major Adjustment

The rapid growth of contemporary human foragers and steady decline of chimpanzees represent puzzling population paradoxes, as any species must exhibit near-stationary growth over much of their evolutionary history. We evaluate the conditions favoring zero population growth (ZPG) among 10 small-scale subsistence human populations and five wild chimpanzee groups according to four demographic scenarios: altered mean vital rates (i.e., fertility and mortality), vital rate stochasticity, vital rate covariance, and periodic catastrophes. Among most human populations, changing mean fertility or survivorship alone requires unprecedented alterations. Stochastic variance and covariance would similarly require major adjustment to achieve ZPG in most populations. Crashes could maintain ZPG in slow-growing populations but must be frequent and severe in fast-growing populations—more extreme than observed in the ethnographic record. A combination of vital rate alteration with catastrophes is the most realistic solution to the forager population paradox. ZPG in declining chimpanzees is more readily obtainable through reducing mortality and altering covariance. While some human populations may have hovered near ZPG under harsher conditions (e.g., violence or food shortage), modernHomo sapienswere equipped with the potential to rapidly colonize new habitats and likely experienced population fluctuations and local extinctions over evolutionary history.

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 Measurement of Dioxin Emissions from a Small-Scale Waste Incinerator in the Absence of Air Pollution Controls

2019 ◽  
Vol 16 (7) ◽  
pp. 1267 ◽  
Author(s):  
Gang Zhang ◽  
Xiangxuan Huang ◽  
Wenbo Liao ◽  
Shimin Kang ◽  
Mingzhong Ren ◽  
...  
Keyword(s):  
Air Pollution ◽  
Solid Waste ◽  
De Novo ◽  
Dominant Role ◽  
Small Scale ◽  
Waste Incinerator ◽  
Bag Filter ◽  
Mean Values ◽  
Carbon Injection ◽  
Waste Incinerators

Polychlorinated dibenzo-p-dioxin and dibenzofuran (PCDD/Fs) emissions from basic small-scale waste incinerators (SWI) may cause health risks in nearby people and are thus subject to stringent regulations. The aim of this study was to evaluate PCDD/F emission and reduction of a basic SWI in the absence of air pollution controls (APCs). The results indicated that the stack gas and fly ash presented average PCDD/F levels and emission factors of 3.6 ng international toxic equivalent (I-TEQ)/Nm3 and 189.31µg I-TEQ/t and 6.89 ng I-TEQ/g and 137.85µg I-TEQ/t, respectively, much higher than those from large municipal solid waste incinerators (MSWI). PCDD/Fs congener fingerprints indicated that de novo synthesis played a dominant role in the low-temperature post-combustion zone and increased the presence of high-chlorine substituted congeners. On the basis of the emission factor 327.24 µg I-TEQ/t-waste, approximately 3000 g I-TEQ dioxins might be generated in total through basic SWIs and open burning. After refitting an SWI by adding activated carbon injection with a bag filter (ACI+BG), the PCDD/F emissions decreased to mean values of 0.042 ng I-TEQ/Nm3, far below the standard of 0.1 ng I-TEQ/Nm3, and the removal efficiency reached 99.13% in terms of I-TEQ. Therefore, it is entirely feasible to considerably reduce PCDD/F emissions by refitting basic SWI, which is positive for the future development of rural solid waste (RSW (RSW) disposal by SWI.

scholarly journals A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Hongzhong Lu ◽  
Feiran Li ◽  
Benjamín J. Sánchez ◽  
Zhengming Zhu ◽  
Gang Li ◽  
...  
Keyword(s):  
Model Organism ◽  
Metabolic Model ◽  
Phenotypic Traits ◽  
Cell Factory ◽  
Scale Level ◽  
Single Nucleotide ◽  
Multi Scale ◽  
Yeast Strains ◽  
Single Nucleotide Variations ◽  
Genome Scale

Abstract Genome-scale metabolic models (GEMs) represent extensive knowledgebases that provide a platform for model simulations and integrative analysis of omics data. This study introduces Yeast8 and an associated ecosystem of models that represent a comprehensive computational resource for performing simulations of the metabolism of Saccharomyces cerevisiae––an important model organism and widely used cell-factory. Yeast8 tracks community development with version control, setting a standard for how GEMs can be continuously updated in a simple and reproducible way. We use Yeast8 to develop the derived models panYeast8 and coreYeast8, which in turn enable the reconstruction of GEMs for 1,011 different yeast strains. Through integration with enzyme constraints (ecYeast8) and protein 3D structures (proYeast8DB), Yeast8 further facilitates the exploration of yeast metabolism at a multi-scale level, enabling prediction of how single nucleotide variations translate to phenotypic traits.

scholarly journals Framework and resource for more than 11,000 gene-transcript-protein-reaction associations in human metabolism

2017 ◽  
Vol 114 (45) ◽  
pp. E9740-E9749 ◽  
Author(s):  
Jae Yong Ryu ◽  
Hyun Uk Kim ◽  
Sang Yup Lee
Keyword(s):  
Transcript Level ◽  
Metabolic Model ◽  
Protein Isoforms ◽  
Gene Transcript ◽  
Human Metabolism ◽  
Functional Roles ◽  
Metabolic Genes ◽  
Level Data ◽  
Genome Scale ◽  
Protein Reaction

Alternative splicing plays important roles in generating different transcripts from one gene, and consequently various protein isoforms. However, there has been no systematic approach that facilitates characterizing functional roles of protein isoforms in the context of the entire human metabolism. Here, we present a systematic framework for the generation of gene-transcript-protein-reaction associations (GeTPRA) in the human metabolism. The framework in this study generated 11,415 GeTPRA corresponding to 1,106 metabolic genes for both principal and nonprincipal transcripts (PTs and NPTs) of metabolic genes. The framework further evaluates GeTPRA, using a human genome-scale metabolic model (GEM) that is biochemically consistent and transcript-level data compatible, and subsequently updates the human GEM. A generic human GEM, Recon 2M.1, was developed for this purpose, and subsequently updated to Recon 2M.2 through the framework. Both PTs and NPTs of metabolic genes were considered in the framework based on prior analyses of 446 personal RNA-Seq data and 1,784 personal GEMs reconstructed using Recon 2M.1. The framework and the GeTPRA will contribute to better understanding human metabolism at the systems level and enable further medical applications.

Evolution of road risk disparities at small-scale level: Example of Belgium

2008 ◽  
Vol 39 (4) ◽  
pp. 417-427 ◽  
Author(s):  
Vojtech Eksler ◽  
Sylvain Lassarre
Keyword(s):  
Small Scale ◽  
Scale Level
Sign in / Sign up

Export Citation Format

Share Document