2,241 research outputs found
An algorithm to assemble gene-protein-reaction associations for genome-scale metabolic model reconstruction
The considerable growth in the number of sequenced genomes
and recent advances in Bioinformatics and Systems Biology fields have
provided several genome-scale metabolic models (GSMs) that have been
used to provide phenotype simulation methods. Given their importance
in biomedical research and biotechnology applications (e.g. in Metabolic
Engineering efforts), several workflows and computational platforms have
been proposed for GSM reconstruction. One of the challenges of these
methods is related to the assignment of gene-protein-reaction (GPR) associations
that allow to add transcriptional/ translational information
to GSMs, a task typically addressed through manual literature curation.
This work proposes a novel algorithm to create a set of GPR rules, based
on the integration of the information provided by the genome annotation
with information on protein composition and function (protein complexes,
sub-units, iso-enzymes, etc.) provided by the UniProt database.
The methods are validated by using two state-of-the-art models for E.
coli and S. cerevisiae, with competitive results.The work is partially funded by ERDF - European Regional Development Fund through the COMPETE Programme ( operational programme for competitiveness) and by National Funds through the FCT ( Portuguese Foundation for Science and Tech- nology) within projects ref. COMPETE FCOMP-01-0124-FEDER-015079 and PEst-OE/EEI/UI0752/2011
Genome-wide semi-automated annotation of transporter systems
Usually, transport reactions are added to genome-scale metabolic models (GSMMs) based on experimental data and literature. This approach does not allow associating specific genes with transport reactions, which impairs the ability of the model to predict effects of gene deletions. Novel methods for systematic genome-wide transporter functional annotation and their integration into GSMMs are therefore necessary. In this work, an automatic system to detect and classify all potential membrane transport proteins for a given genome and integrate the related reactions into GSMMs is proposed, based on the identification and classification of genes that encode transmembrane proteins. The Transport Reactions Annotation and Generation (TRIAGE) tool identifies the metabolites transported by each transmembrane protein and its transporter family. The localization of the carriers is also predicted and, consequently, their action is confined to a given membrane. The integration of the data provided by TRIAGE with highly curated models allowed the identification of new transport reactions. TRIAGE is included in the new release of merlin, a software tool previously developed by the authors, which expedites the GSMM reconstruction processes.This work was partially supported by a PhD grant (SFRH /BD/47307/2008) and by the ERDF—European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness), and National Funds through the FCT within the projects FCOMP-01-0124-FEDER-009707 (HeliSysBio—molecular Systems Biology in Helicobacter pylori) and PTDC/EIAEIA/115176/2009. The authors would also like to thank the FCT Strategic Project PEst-OE/EQB/LA0023/2013 and the Projects “BioInd - Biotechnology and Bioengineering for improved Industrial and Agro-Food processes”, REF. NORTE-07-0124-FEDER-000028 and “PEM – Metabolic Engineering Platform”, project number 23060 , both cofunded by the Programa Operacional Regional do Norte (ON.2 – O Novo Norte), QREN, FEDER.info:eu-repo/semantics/publishedVersio
A review of methods for the reconstruction and analysis of integrated genome-scale models of metabolism and regulation
The current survey aims to describe the main methodologies for extending the reconstruction and analysis of genome-scale metabolic models and phenotype simulation with Flux Balance Analysis mathematical frameworks, via the integration of Transcriptional Regulatory Networks and/or gene expression data. Although the surveyed methods are aimed at improving phenotype simulations obtained from these models, the perspective of reconstructing integrated genome-scale models of metabolism and gene expression for diverse prokaryotes is still an open challenge.This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UIDB/04 469/2020 unit and BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020 -Programa Operacional Regional do Norte. Fernando Cruz holds a doctoral fellowship (SFRH/BD/139198/2018) funded by the FCT. This study was supported by the European Commission through project SHIKIFACTORY100 -Modular cell factories for the production of 100 compounds from the shikimate pathway (Reference 814408). The submitted manuscript has been created by UChicago Argonne, LLC as Operator of Argonne National Laboratory (`Argonne') under Contract No. DE-AC02-06CH11357 with the U.S. Department of Energy. The U.S. Government retains for itself, and others acting on its behalf, a paid-up, nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan.info:eu-repo/semantics/publishedVersio
Developing methods for the context-specific reconstruction of metabolic models of cancer cells
Dissertação de mestrado em BioinformáticaThe recent advances in genome sequencing technologies and other high-throughput methodologies
allowed the identification and quantification of individual cell components. These efforts led to the
development of genome-scale metabolic models (GSMMs), not only for humans but also for several
other organisms. These models have been used to predict cellular metabolic phenotypes under a
variety of physiological conditions and contexts, proving to be useful in tasks such as drug discovery,
biomarker identification and interactions between hosts and pathogens. Therefore, these models
provide a useful tool for targeting diseases such as cancer, Alzheimer or tuberculosis.
However, the usefulness of GSSMs is highly dependent on their capabilities to predict phenotypes
in the array of different cell types that compose the human body, making the development of
tissue/context-specific models mandatory. To address this issue, several methods have been
proposed to integrate omics data, such as transcriptomics or proteomics, to improve the phenotype
prediction abilities of GSSMs. Despite these efforts, these methods still have some limitations. In most
cases, their usage is locked behind commercially licensed software platforms, or not available in a
user-friendly fashion, thus restricting their use to users with programming or command-line
knowledge.
In this work, an open-source tool was developed for the reconstruction of tissue/context-specific
models based on a generic template GSMM and the integration of omics data. The Tissue-Specific
Model Reconstruction (TSM-Rec) tool was developed under the Python programming language and
features the FASTCORE algorithm for the reconstruction of tissue/context-specific metabolic models.
Its functionalities include the loading of omics data from a variety of omics databases, a set of filtering
and transformation methods to adjust the data for integration with a template metabolic model, and
finally the reconstruction of tissue/context-specific metabolic models.
To evaluate the functionality of the developed tool, a cancer related case-study was carried. Using
omics data from 314 glioma patients, the TSM-Rec tool was used to reconstruct metabolic models of
different grade gliomas. A total of three models were generated, corresponding to grade II, III and IV
gliomas. These models were analysed regarding their differences and similarities in reactions and
pathways. This comparison highlighted biological processes common to all glioma grades, and
pathways that are more prominent in each glioma model. The results show that the tool developed
during this work can be useful for the reconstruction of cancer metabolic models, in a search for
insights into cancer metabolism and possible approaches towards drug-target discovery.Os avanços recentes nas tecnologias de sequenciação de genomas e noutras metodologias
experimentais de alto rendimento permitiram a identificação e quantificação dos diversos
componentes celulares. Estes esforços levaram ao desenvolvimento de Modelos Metabólicos à Escala
Genómica (MMEG) não só de humanos, mas também de diversos organismos. Estes modelos têm
sido utilizados para a previsão de fenótipos metabólicos sob uma variedade de contextos e condições
fisiológicas, mostrando a sua utilidade em áreas como a descoberta de fármacos, a identificação de
biomarcadores ou interações entre hóspede e patógeno. Desta forma, estes modelos revelam-se
ferramentas úteis para o estudo de doenças como o cancro, Alzheimer ou a tuberculose.
Contudo, a utilidade dos MMEG está altamente dependente das suas capacidades de previsão
de fenótipos nos diversostipos celulares que compõem o corpo humano, tornando o desenvolvimento
de modelos específicos de tecidos uma tarefa obrigatória. Para resolver este problema, vários
métodos têm proposto a integração de dados ómicos como os de transcriptómica ou proteómica
para melhorar as capacidades preditivas dos MMEG. Apesar disso, estes métodos ainda sofrem de
algumas limitações. Na maioria dos casos o seu uso está confinado a plataformas ou softwares com
licenças comerciais, ou não está disponível numa ferramenta de fácil uso, limitando a sua utilização
a utilizadores com conhecimentos de programação ou de linha de comandos.
Neste trabalho, foi desenvolvida uma ferramenta de acesso livre para a reconstrução de modelos
metabólicos específicos para tecidos tendo por base um MMEG genérico e a integração de dados
ómicos. A ferramenta TSM-Rec (Tissue-Specific Model Reconstruction), foi desenvolvida na linguagem
de programação Python e recorre ao algoritmo FASTCORE para efetuar a reconstrução de modelos
metabólicos específicos. As suas funcionalidades permitem a leitura de dados ómicos de diversas
bases de dados ómicas, a filtragem e transformação dos mesmos para permitir a sua integração
com um modelo metabólico genérico e por fim, a reconstrução de modelos metabólicos específicos.
De forma a avaliar o funcionamento da ferramenta desenvolvida, esta foi aplicada num caso de
estudo de cancro. Recorrendo a dados ómicos de 314 pacientes com glioma, usou-se a ferramenta
TSM-Rec para a reconstrução de modelos metabólicos de gliomas de diferentes graus. No total, foram
desenvolvidos três modelos correspondentes a gliomas de grau II, grau III e grau IV. Estes modelos
foram analisados no sentido de perceber as diferenças e as similaridades entre as reações e as vias
metabólicas envolvidas em cada um dos modelos. Esta comparação permitiu isolar processos
biológicos comuns a todos os graus de glioma, assim como vias metabólicas que se destacam em cada um dos graus. Os resultados obtidos demonstram que a ferramenta desenvolvida pode ser útil
para a reconstrução de modelos metabólicos de cancro, na procura de um melhor conhecimento do
metabolismo do cancro e possíveis abordagens para a descoberta de fármacos
MetRxn: a knowledgebase of metabolites and reactions spanning metabolic models and databases
<p>Abstract</p> <p>Background</p> <p>Increasingly, metabolite and reaction information is organized in the form of genome-scale metabolic reconstructions that describe the reaction stoichiometry, directionality, and gene to protein to reaction associations. A key bottleneck in the pace of reconstruction of new, high-quality metabolic models is the inability to directly make use of metabolite/reaction information from biological databases or other models due to incompatibilities in content representation (i.e., metabolites with multiple names across databases and models), stoichiometric errors such as elemental or charge imbalances, and incomplete atomistic detail (e.g., use of generic R-group or non-explicit specification of stereo-specificity).</p> <p>Description</p> <p>MetRxn is a knowledgebase that includes standardized metabolite and reaction descriptions by integrating information from BRENDA, KEGG, MetaCyc, Reactome.org and 44 metabolic models into a single unified data set. All metabolite entries have matched synonyms, resolved protonation states, and are linked to unique structures. All reaction entries are elementally and charge balanced. This is accomplished through the use of a workflow of lexicographic, phonetic, and structural comparison algorithms. MetRxn allows for the download of standardized versions of existing genome-scale metabolic models and the use of metabolic information for the rapid reconstruction of new ones.</p> <p>Conclusions</p> <p>The standardization in description allows for the direct comparison of the metabolite and reaction content between metabolic models and databases and the exhaustive prospecting of pathways for biotechnological production. This ever-growing dataset currently consists of over 76,000 metabolites participating in more than 72,000 reactions (including unresolved entries). MetRxn is hosted on a web-based platform that uses relational database models (MySQL).</p
Blueprint: descrição da complexidade da regulação metabólica através da reconstrução de modelos metabólicos e regulatórios integrados
Tese de doutoramento em Biomedical EngineeringUm modelo metabólico consegue prever o fenótipo de um organismo. No entanto, estes modelos
podem obter previsões incorretas, pois alguns processos metabólicos são controlados por mecanismos
reguladores. Assim, várias metodologias foram desenvolvidas para melhorar os modelos metabólicos
através da integração de redes regulatórias. Todavia, a reconstrução de modelos regulatórios e metabólicos à escala genómica para diversos organismos apresenta diversos desafios.
Neste trabalho, propõe-se o desenvolvimento de diversas ferramentas para a reconstrução e análise
de modelos metabólicos e regulatórios à escala genómica. Em primeiro lugar, descreve-se o Biological
networks constraint-based In Silico Optimization (BioISO), uma nova ferramenta para auxiliar a curação
manual de modelos metabólicos. O BioISO usa um algoritmo de relação recursiva para orientar as previsões de fenótipo. Assim, esta ferramenta pode reduzir o número de artefatos em modelos metabólicos,
diminuindo a possibilidade de obter erros durante a fase de curação.
Na segunda parte deste trabalho, desenvolveu-se um repositório de redes regulatórias para procariontes que permite suportar a sua integração em modelos metabólicos. O Prokaryotic Transcriptional
Regulatory Network Database (ProTReND) inclui diversas ferramentas para extrair e processar informação regulatória de recursos externos. Esta ferramenta contém um sistema de integração de dados que
converte dados dispersos de regulação em redes regulatórias integradas. Além disso, o ProTReND dispõe
de uma aplicação que permite o acesso total aos dados regulatórios.
Finalmente, desenvolveu-se uma ferramenta computacional no MEWpy para simular e analisar modelos regulatórios e metabólicos. Esta ferramenta permite ler um modelo metabólico e/ou rede regulatória,
em diversos formatos. Esta estrutura consegue construir um modelo regulatório e metabólico integrado
usando as interações regulatórias e as ligações entre genes e proteínas codificadas no modelo metabólico e na rede regulatória. Além disso, esta estrutura suporta vários métodos de previsão de fenótipo
implementados especificamente para a análise de modelos regulatórios-metabólicos.Genome-Scale Metabolic (GEM) models can predict the phenotypic behavior of organisms. However,
these models can lead to incorrect predictions, as certain metabolic processes are controlled by regulatory
mechanisms. Accordingly, many methodologies have been developed to extend the reconstruction and
analysis of GEM models via the integration of Transcriptional Regulatory Network (TRN)s. Nevertheless,
the perspective of reconstructing integrated genome-scale regulatory and metabolic models for diverse
prokaryotes is still an open challenge.
In this work, we propose several tools to assist the reconstruction and analysis of regulatory and
metabolic models. We start by describing BioISO, a novel tool to assist the manual curation of GEM
models. BioISO uses a recursive relation-like algorithm and Flux Balance Analysis (FBA) to evaluate and
guide debugging of in silico phenotype predictions. Hence, this tool can reduce the number of artifacts in
GEM models, decreasing the burdens of model refinement and curation.
A state-of-the-art repository of TRNs for prokaryotes was implemented to support the reconstruction
and integration of TRNs into GEM models. The ProTReND repository comprehends several tools to extract
and process regulatory information available in several resources. More importantly, this repository contains a data integration system to unify the regulatory data into standardized TRNs at the genome scale.
In addition, ProTReND contains a web application with full access to the regulatory data.
Finally, we have developed a new modeling framework to define, simulate and analyze GEnome-scale
Regulatory and Metabolic (GERM) models in MEWpy. The GERM model framework can read a GEM
model, as well as a TRN from different file formats. This framework assembles a GERM model using
the regulatory interactions and Genes-Proteins-Reactions (GPR) rules encoded into the GEM model and
TRN. In addition, this modeling framework supports several methods of phenotype prediction designed
for regulatory-metabolic models.I would like to thank Fundação para a Ciência e Tecnologia for the Ph.D. studentship I was awarded
with (SFRH/BD/139198/2018)
Can the Genomics of the Gut Microbiota in Stool Samples be Analyzed by MERLIN?
Metagenomics is important in studying microorganisms that live in microbial communities, particularly those that inhabit the human body. For example, the gut microbiota is a community of microorganisms inhabit the human gut and interact with humans via secondary metabolites. Secondary metabolites produced by the gut microbiota are extremely important and are used as precursors for a variety of human needs, such as short chain fatty acids (SCFA). There have been reports of functional secondary metabolites produced by various gut microbiota, but none have yet been used on the Merlin platform. In this article, we'll look at how the Merlin platform can analyze the gut microbiota community. Metabolic Models Reconstruction using Genome-Scale Information (MERLIN) is a bioinformatics tool that can analyze the functional microbial community as well as the taxonomy of these bacteria
Genome-scale metabolic model of the human pathogen Candida albicans: a promising platform for drug target prediction
Candida albicans is one of the most impactful fungal pathogens and the most common cause of invasive candidiasis, which is associated with very high mortality rates. With the rise in the frequency of multidrug-resistant clinical isolates, the identification of new drug targets and new drugs is crucial in overcoming the increase in therapeutic failure. In this study, the first validated genome-scale metabolic model for Candida albicans, iRV781, is presented. The model consists of 1221 reactions, 926 metabolites, 781 genes, and four compartments. This model was reconstructed using the open-source software tool merlin 4.0.2. It is provided in the well-established systems biology markup language (SBML) format, thus, being usable in most metabolic engineering platforms, such as OptFlux or COBRA. The model was validated, proving accurate when predicting the capability of utilizing different carbon and nitrogen sources when compared to experimental data. Finally, this genome-scale metabolic reconstruction was tested as a platform for the identification of drug targets, through the comparison between known drug targets and the prediction of gene essentiality in conditions mimicking the human host. Altogether, this model provides a promising platform for global elucidation of the metabolic potential of C. albicans, possibly guiding the identification of new drug targets to tackle human candidiasis.“Fundação para a Ciência e a Tecnologia” (FCT) [Contract PTDC
/BII-BIO/28216/2017] and by Programa Operacional Regional de Lisboa 2020 [LISBOA-01-0145-FEDER-022231],
through the Biodata.pt Research Infrastructure. Funding received by iBB-Institute for Bioengineering and
Biosciences from FCT [Contract UIDB/04565/2020]info:eu-repo/semantics/publishedVersio
An insight towards food-related microbial sets through metabolic modelling and functional analysis
The dietary food digestion depends on the human gastrointestinal tract, where host cells and gut microbes mutually interact. This interplay may also mediate host metabolism, as shown by microbial-derived secondary bile acids, needed for receptor signalling. Microbes are also crucial in the production of fermented foods, such as wine and dairy. Kefir is fermented milk processed by the symbiotic community of bacteria and yeasts. One such species is a yeast Kluyveromyces marxianus. Its thermotolerance is a desired trait in biotechnology since it may reduce the cooling demands during cultivation.The systems biology tools allow analysing various size microbial communities under the different functional scope. For example, the homology prediction tools can give detailed functional insights when working with metagenomics data. The whole-cell metabolic processes can be summarised in genome-scale metabolic models (GEMs), which enable to predict the metabolic capabilities and allow for the integration of omics data.The work shown in this thesis includes i) in silico analysis of food-related microbes; ii) the development of GEMs and RAVEN. With a focus on bile acid metabolism, hundreds of human gut microbes were annotated based on metagenomics data, thereby suggesting the differences in the potential for bile acid processing between healthy and diseased subjects. These findings may be exploitable once aiming to restore the bile acid metabolism for the patients having inflammatory bowel disease. Also, the metabolism of yeast K. marxianus was characterised in genome-scale. Two K. marxianus strains from kefir grains were isolated, sequenced, assembled, and functionally annotated. They were compared with the other ten strains, providing the core and dispensable physiological features for K. marxianus. Furthermore, the first GEM for K. marxianus, namely iSM996, was reconstructed. It was integrated with transcriptomics data to predict its metabolic capabilities in rich medium and high-temperature conditions. The results might be useful to optimise strain-specific medium for high-temperature applications. The final paper comprises the efforts to improve the usability for RAVEN, a toolbox for GEM reconstruction and analysis. Altogether the outcomes of this thesis suggest the potential applications for medicine and industrial biotechnology, which may be facilitated by the newly upgraded RAVEN toolbox
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