Systems biology metabolic modeling assistant: an ontology-based tool for the integration of metabolic data in kinetic modeling

Reyes-Palomares, Armando; Montañez, Raúl; Real-Chicharro, Alejando; Chniber, Othmane; Kerzazi, Amine; Navas‐Delgado, Ismael; Medina, Miguel Ángel; Aldana‐Montes, José F.; Sánchez‐Jiménez, Francisca

doi:10.1093/bioinformatics/btp061

Cited by 16 publications

(7 citation statements)

References 9 publications

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“…The entry also summarizes the PTS predictions and homology computations, including BLASTP, PSI-BLAST, multialignments and phylogenetic trees. Finally, enzymes entries are linked through the ontology-based mediator used by SBMM Assistant (www.sbmm.uma.es) (25) to the reference databases Brenda, CheBI and Sabio-RK, which provide updated kinetic and biochemical information for peroxisomal enzymes. This assistant works by recognizing the respective EC codes from Gene Bank and/or Ensemble and subsequently fetches the relevant data.…”

Section: Resultsmentioning

confidence: 99%

PeroxisomeDB 2.0: an integrative view of the global peroxisomal metabolome

Schlüter

Real-Chicharro

Gabaldón

et al. 2009

Nucleic Acids Research

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Peroxisomes are essential organelles that play a key role in redox signalling and lipid homeostasis. They contain a highly diverse enzymatic network among different species, mirroring the varied metabolic needs of the organisms. The previous PeroxisomeDB version organized the peroxisomal proteome of humans and Saccharomyces cerevisiae based on genetic and functional information into metabolic categories with a special focus on peroxisomal disease. The new release (http://www.peroxisomeDB.org) adds peroxisomal proteins from 35 newly sequenced eukaryotic genomes including fungi, yeasts, plants and lower eukaryotes. We searched these genomes for a core ensemble of 139 peroxisomal protein families and identified 2706 putative peroxisomal protein homologs. Approximately 37% of the identified homologs contained putative peroxisome targeting signals (PTS). To help develop understanding of the evolutionary relationships among peroxisomal proteins, the new database includes phylogenetic trees for 2386 of the peroxisomal proteins. Additional new features are provided, such as a tool to capture kinetic information from Brenda, CheBI and Sabio-RK databases and more than 1400 selected bibliographic references. PeroxisomeDB 2.0 is a freely available, highly interactive functional genomics platform that offers an extensive view on the peroxisomal metabolome across lineages, thus facilitating comparative genomics and systems analysis of the organelle.

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Section: Resultsmentioning

confidence: 99%

PeroxisomeDB 2.0: an integrative view of the global peroxisomal metabolome

Schlüter

Real-Chicharro

Gabaldón

et al. 2009

Nucleic Acids Research

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“…An ever-increasing number of biocomputational tools are made available to help researchers in the task of metabolic modeling. They include BioPP (Viswanathan et al, 2007), CellDesigner, (Funahashi et al, 2003, COPASI (Hoops et al, 2006), Payao (Matsuoka et al, 2010, sycamore (Weidermann et al, 2008), SBMM Assistant (Reyes-Palomares et al, 2009) and WikiPathways (Kutmon et al, 2016), among many others. There are also several useful databases containing valuable data regarding metabolic pathways, such as UNIPROT, KEGG, CHEBI, BRENDA and SABIO-RK.…”

Section: Tools and Data Sourcesmentioning

confidence: 99%

Mathematical modeling of cancer metabolism

Medina

2018

Critical Reviews in Oncology/Hematology

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Systemic approaches are needed and useful for the study of the very complex issue of cancer. Modeling has a central position in these systemic approaches. Metabolic reprogramming is nowadays acknowledged as an essential hallmark of cancer. Mathematical modeling could contribute to a better understanding of cancer metabolic reprogramming and to identify new potential ways of therapeutic intervention. Herein, I review several alternative approaches to metabolic modeling and their current and future impact in oncology.

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“…3. Mathematical representation of a metabolic reconstructing network: This step could be done automatically using different kinds of software [125]. In addition, system boundaries are defined and some constraints are simulated.…”

Section: How Genome-scale Metabolic Models Can Be Generatedmentioning

confidence: 99%

Integration of Metabolic Knowledge for Genome‐Scale Metabolic Reconstruction

Masoudi‐Nejad

Salehzadeh‐Yazdi

Akbari‐Birgani

et al. 2013

Biological Knowledge Discovery Handbook

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INTRODUCTIONGenome-scale metabolic reconstruction is the ultimate goal of system biologists for finding out the genotype-phenotype relationship. For achieving this purpose, we need enough knowledge about the metabolic pathways and genome annotation. During the present study, we aim to describe the foundational concepts, central to omics data, model formulation, history, method, and applications of metabolic network reconstruction as well as some related sources. In Section 45.2, we describe omics data and high-throughput technologies for gaining these data. In section 45.3, we present different ways for metabolic network modeling. In section 45.4, we summarize the history of genome-scale modeling as one of the most important methods for metabolic network modeling. In sections 45.5 and 45.6, we elucidate how genome-scale metabolic models could be generated and what their applications are. Finally, in section 45.7, we review biochemical pathways and genome annotation databases. OMICs ERAInnovative omics technologies such as genomics, transcriptomics, proteomics, and metabolomics facilitate a strategy toward the simultaneous analysis of the large number of genes, transcripts, proteins, and metabolites. Therefore, a huge volume of data has generated about the make-up of cells and their behavior at various cellular levels and different environmental conditions, which enable us to reconstruct genome-scale biomolecular networks (e.g., transcriptional regulatory networks, interactomic networks, and metabolic networks)Knowledge Discovery Handbook: Preprocessing, Mining, and Postprocessing of Biological Data, First Edition. Edited by Mourad Elloumi and Albert Y. Zomaya.

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Systems biology metabolic modeling assistant: an ontology-based tool for the integration of metabolic data in kinetic modeling

Abstract: SBMM Assistant for academic use is freely available at http://www.sbmm.uma.es.

Cited by 16 publications

References 9 publications

PeroxisomeDB 2.0: an integrative view of the global peroxisomal metabolome

PeroxisomeDB 2.0: an integrative view of the global peroxisomal metabolome

Mathematical modeling of cancer metabolism

Integration of Metabolic Knowledge for Genome‐Scale Metabolic Reconstruction

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