scholarly journals RMetD2: a tool for integration of relative transcriptomics data into Genome-scale metabolic models

2019 ◽  
Author(s):  
Cheng Zhang ◽  
Sunjae Lee ◽  
Gholamreza Bidkhori ◽  
Rui Benfeitas ◽  
Alen Lovric ◽  
...  
Keyword(s):  
Case Studies ◽  
State Of The Art ◽  
Differentially Expressed ◽  
Systems Medicine ◽  
Transcriptomics Data ◽  
Altered Metabolism ◽  
Metabolic Models ◽  
Genome Scale ◽  
Potential Use ◽  
Single Set

AbstractRelative Metabolic Differences version 2 (RMetD2) is a tool for integration of differentially expressed (DE) genes into genome-scale metabolic models (GEMs) for revealing the altered metabolism between two biological conditions. This method provides a robust evaluation of the metabolism by using flux ranges instead of a single set of flux distributions. RMetD2 classifies reactions into three different groups, namely up-regulated, down-regulated and unchanged, which enables systematic interpretation of the metabolic differences between two different conditions. We employed this method in three different case studies using mice and human datasets, and compared it with state-of-the-art methods used for studying condition-specific metabolic differences using GEMs. We observed that RMetD2 is capable of capturing experimentally-observed features that are missed by other methods, highlighting its potential use in biotechnology and systems medicine applications. RMetD2 is implemented in Matlab and it is available without any limitation at https://sourceforge.net/projects/rmetd.

scholarly journals Consistency, Inconsistency and Ambiguity of Metabolite Names in Biochemical Databases Used for Genome Scale Metabolic Modelling

2018 ◽  
Author(s):  
Nhung Pham ◽  
Ruben Van Heck ◽  
Jesse van Dam ◽  
Peter Schaap ◽  
Edoardo Saccenti ◽  
...  
Keyword(s):  
Systems Medicine ◽  
Biochemical Reactions ◽  
Metabolic Modelling ◽  
Research Areas ◽  
Limit Model ◽  
Metabolic Models ◽  
Genome Scale ◽  
Manual Verification

Genome scale metabolic models (GEMs) are manually curated repositories describing the metabolic capabilities of an organism. GEMs have been successfully used in different research areas, ranging from systems medicine to biotechnology. However, the different naming conventions (namespaces) of databases used to build GEMs limit model reusability and prevent the integration of existing models. This problem is known in the GEM community but its extent has not been analyzed in depth. In this study, we investigate the name ambiguity and the multiplicity of non-systematic identifiers and we highlight the (in)consistency in their use in eleven biochemical databases of biochemical reactions and the problems that arise when mapping between different namespaces and databases. We found that such inconsistencies can be as high as 83.1%, thus emphasizing the need for strategies to deal with these issues. Currently, manual verification of the mappings appears to be the only solution to remove inconsistencies when combining models. Finally, we discuss several possible approaches to facilitate (future) unambiguous mapping.

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 gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Johannes Zimmermann ◽  
Christoph Kaleta ◽  
Silvio Waschina
Keyword(s):  
Experimental Data ◽  
Metabolic Pathways ◽  
Scientific Literature ◽  
State Of The Art ◽  
Gap Filling ◽  
Fermentation Products ◽  
Metabolic Interactions ◽  
Metabolic Models ◽  
Genome Scale ◽  
Carbon Source Utilisation

AbstractGenome-scale metabolic models of microorganisms are powerful frameworks to predict phenotypes from an organism’s genotype. While manual reconstructions are laborious, automated reconstructions often fail to recapitulate known metabolic processes. Here we present (https://github.com/jotech/gapseq), a new tool to predict metabolic pathways and automatically reconstruct microbial metabolic models using a curated reaction database and a novel gap-filling algorithm. On the basis of scientific literature and experimental data for 14,931 bacterial phenotypes, we demonstrate that gapseq outperforms state-of-the-art tools in predicting enzyme activity, carbon source utilisation, fermentation products, and metabolic interactions within microbial communities.

scholarly journals TEX-FBA: A constraint-based method for integrating gene expression, thermodynamics, and metabolomics data into genome-scale metabolic models

2019 ◽  
Author(s):  
Vikash Pandey ◽  
Daniel Hernandez Gardiol ◽  
Anush Chiappino-Pepe ◽  
Vassily Hatzimanikatis
Keyword(s):  
Gene Expression ◽  
Experimental Data ◽  
Solution Space ◽  
Metabolic Model ◽  
Scale Models ◽  
Different Types ◽  
Transcriptomics Data ◽  
Metabolic Reactions ◽  
Metabolic Models ◽  
Genome Scale

AbstractA large number of genome-scale models of cellular metabolism are available for various organisms. These models include all known metabolic reactions based on the genome annotation. However, the reactions that are active are dependent on the cellular metabolic function or environmental condition. Constraint-based methods that integrate condition-specific transcriptomics data into models have been used extensively to investigate condition-specific metabolism. Here, we present a method (TEX-FBA) for modeling condition-specific metabolism that combines transcriptomics and reaction thermodynamics data to generate a thermodynamically-feasible condition-specific metabolic model. TEX-FBA is an extension of thermodynamic-based flux balance analysis (TFA), which allows the simultaneous integration of different stages of experimental data (e.g., absolute gene expression, metabolite concentrations, thermodynamic data, and fluxomics) and the identification of alternative metabolic states that maximize consistency between gene expression levels and condition-specific reaction fluxes. We applied TEX-FBA to a genome-scale metabolic model ofEscherichia coliby integrating available condition-specific experimental data and found a marked reduction in the flux solution space. Our analysis revealed a marked correlation between actual gene expression profile and experimental flux measurements compared to the one obtained from a randomly generated gene expression profile. We identified additional essential reactions from the membrane lipid and folate metabolism when we integrated transcriptomics data of the given condition on the top of metabolomics and thermodynamics data. These results show TEX-FBA is a promising new approach to study condition-specific metabolism when different types of experimental data are available.Author summaryCells utilize nutrients via biochemical reactions that are controlled by enzymes and synthesize required compounds for their survival and growth. Genome-scale models of metabolism representing these complex reaction networks have been reconstructed for a wide variety of organisms ranging from bacteria to human cells. These models comprise all possible biochemical reactions in a cell, but cells choose only a subset of reactions for their immediate needs and functions. Usually, these models allow for a large flux solution space and one can integrate experimental data in order to reduce it and potentially predict the physiology for a specific condition. We developed a method for integrating different types of omics data, such as fluxomics, transcriptomics, metabolomics into genome-scale metabolic models that reduces the flux solution space. Using gene expression data, the algorithm maximizes the consistency between the predicted and experimental flux for the reactions and predicts biologically relevant flux ranges for the remaining reactions in the network. This method is useful for determining fluxes of metabolic reactions with reduced uncertainty and suitable for performing context- and condition-specific analysis in metabolic models using different types of experimental data.

scholarly journals Consistency, Inconsistency, and Ambiguity of Metabolite Names in Biochemical Databases Used for Genome-Scale Metabolic Modelling

Metabolites ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 28 ◽  
Author(s):  
Nhung Pham ◽  
Ruben van Heck ◽  
Jesse van Dam ◽  
Peter Schaap ◽  
Edoardo Saccenti ◽  
...  
Keyword(s):  
Systems Medicine ◽  
Biochemical Reactions ◽  
Metabolic Modelling ◽  
Research Areas ◽  
Limit Model ◽  
Metabolic Models ◽  
Genome Scale ◽  
Manual Verification

Genome-scale metabolic models (GEMs) are manually curated repositories describing the metabolic capabilities of an organism. GEMs have been successfully used in different research areas, ranging from systems medicine to biotechnology. However, the different naming conventions (namespaces) of databases used to build GEMs limit model reusability and prevent the integration of existing models. This problem is known in the GEM community, but its extent has not been analyzed in depth. In this study, we investigate the name ambiguity and the multiplicity of non-systematic identifiers and we highlight the (in)consistency in their use in 11 biochemical databases of biochemical reactions and the problems that arise when mapping between different namespaces and databases. We found that such inconsistencies can be as high as 83.1%, thus emphasizing the need for strategies to deal with these issues. Currently, manual verification of the mappings appears to be the only solution to remove inconsistencies when combining models. Finally, we discuss several possible approaches to facilitate (future) unambiguous mapping.

scholarly journals Reconstruction of consensus tissue-specific metabolic models

2018 ◽  
Author(s):  
Sara Correia ◽  
Bruno Costa ◽  
Miguel Rocha
Keyword(s):  
Cell Line ◽  
Case Studies ◽  
Cancer Metabolism ◽  
Cell Types ◽  
Tissue Cell ◽  
Tissue Specific ◽  
Metabolic Phenotypes ◽  
Distinct Cell ◽  
Metabolic Models ◽  
Genome Scale

AbstractGenome-Scale Metabolic Models have shown promising results in biomedical applications, such as understanding cancer metabolism and drug discovery. However, to take full advantage of these models there is the need to address the representation and simulation of the metabolic phenotypes of distinct cell types. With this aim, several algorithms have been recently proposed to reconstruct tissue-specific metabolic models based on available data. Here, the most promising were implemented and used to reconstruct models for two case studies, using omics data from distinct sources. The set of obtained models were compared and analyzed, being shown they are highly variable and that no combination of algorithm and data source can achieve models with acceptable phenotype predictions. We propose an algorithm to achieve a consensus model from the set of models available for a given tissue/cell line, and to improve it given functional data (e.g. known metabolic tasks). The results show that the resulting models are more accurate, both considering the prediction of known metabolic phenotypes and of experimental data not used in the model construction. Two case studies used for model validation consider healthy hepatocytes and a glioblastoma cell line. The open-source implementation of the algorithms is provided, together with the models built, in a software container, allowing full reproducibility, and representing by itself a contribution for the community.

SEM-Based Nanoprobing on 40, 32 and 28 nm CMOS Devices Challenges for Semiconductor Failure Analysis

2013 ◽  
Author(s):  
Erik Paul ◽  
Holger Herzog ◽  
Sören Jansen ◽  
Christian Hobert ◽  
Eckhard Langer
Keyword(s):  
Electron Microscope ◽  
High Resolution ◽  
Scanning Electron Microscope ◽  
Failure Analysis ◽  
Case Studies ◽  
State Of The Art ◽  
Root Cause ◽  
Highly Efficient ◽  
Technology Nodes ◽  
Scanning Electron

Abstract This paper presents an effective device-level failure analysis (FA) method which uses a high-resolution low-kV Scanning Electron Microscope (SEM) in combination with an integrated state-of-the-art nanomanipulator to locate and characterize single defects in failing CMOS devices. The presented case studies utilize several FA-techniques in combination with SEM-based nanoprobing for nanometer node technologies and demonstrate how these methods are used to investigate the root cause of IC device failures. The methodology represents a highly-efficient physical failure analysis flow for 28nm and larger technology nodes.

State of the Art on Carbonic Anhydrase Modulators for Biomedical Purposes

2019 ◽  
Vol 26 (15) ◽  
pp. 2558-2573 ◽  
Author(s):  
Murat Bozdag ◽  
Abdulmalik Saleh Alfawaz Altamimi ◽  
Daniela Vullo ◽  
Claudiu T. Supuran ◽  
Fabrizio Carta
Keyword(s):  
Central Nervous System ◽  
Nervous System ◽  
State Of The Art ◽  
Cellular Level ◽  
Hydration Reaction ◽  
Carbonic Anhydrases ◽  
Crucial Importance ◽  
Learning Impairments ◽  
Current Review ◽  
Potential Use

The current review is intended to highlight recent advances in the search of new and effective modulators of the metalloenzymes Carbonic Anhydrases (CAs, EC 4.2.1.1) expressed in humans (h). CAs reversibly catalyze the CO2 hydration reaction, which is of crucial importance in the regulation of a plethora of fundamental processes at cellular level as well as in complex organisms. The first section of this review will be dedicated to compounds acting as activators of the hCAs (CAAs) and their promising effects on central nervous system affecting pathologies mainly characterized from memory and learning impairments. The second part will focus on the emerging chemical classes acting as hCA inhibitors (CAIs) and their potential use for the treatment of diseases.

scholarly journals Revealing the Metabolic Alterations during Biofilm Development of Burkholderia cenocepacia Based on Genome-Scale Metabolic Modeling

Metabolites ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 221
Author(s):  
Ozlem Altay ◽  
Cheng Zhang ◽  
Hasan Turkez ◽  
Jens Nielsen ◽  
Mathias Uhlén ◽  
...  
Keyword(s):  
Systems Biology ◽  
Alternative Pathway ◽  
Growth Conditions ◽  
Pentose Phosphate ◽  
Burkholderia Cenocepacia ◽  
Bacterial Cells ◽  
Metabolic Remodeling ◽  
Transcriptomics Data ◽  
Biofilm Development ◽  
Genome Scale

Burkholderia cenocepacia is among the important pathogens isolated from cystic fibrosis (CF) patients. It has attracted considerable attention because of its capacity to evade host immune defenses during chronic infection. Advances in systems biology methodologies have led to the emergence of methods that integrate experimental transcriptomics data and genome-scale metabolic models (GEMs). Here, we integrated transcriptomics data of bacterial cells grown on exponential and biofilm conditions into a manually curated GEM of B. cenocepacia. We observed substantial differences in pathway response to different growth conditions and alternative pathway susceptibility to extracellular nutrient availability. For instance, we found that blockage of the reactions was vital through the lipid biosynthesis pathways in the exponential phase and the absence of microenvironmental lysine and tryptophan are essential for survival. During biofilm development, bacteria mostly had conserved lipid metabolism but altered pathway activities associated with several amino acids and pentose phosphate pathways. Furthermore, conversion of serine to pyruvate and 2,5-dioxopentanoate synthesis are also identified as potential targets for metabolic remodeling during biofilm development. Altogether, our integrative systems biology analysis revealed the interactions between the bacteria and its microenvironment and enabled the discovery of antimicrobial targets for biofilm-related diseases.

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