Troppo - A Python Framework for the Reconstruction of Context-Specific Metabolic Models

Ferreira, Jorge; Vieira, Vítor; Gomes, Jorge; Correia, Sara; Rocha, Miguel

doi:10.1007/978-3-030-23873-5_18

Cited by 10 publications

(13 citation statements)

References 23 publications

(31 reference statements)

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“…The Troppo (Ferreira et al, 2020) python package was used to integrate the transcriptomics data in the metabolic model, originating tissue-specific models.…”

Section: Methodsmentioning

confidence: 99%

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The first multi-tissue diel genome-scale metabolic model of a woody plant highlights suberin biosynthesis pathways inQuercus suber

Cunha

Silva

Chaves

et al. 2021

Preprint

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In 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.

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“…The Troppo (Ferreira et al, 2020) python package was used to integrate the transcriptomics data in the metabolic model, originating tissue-specific models.…”

Section: Methodsmentioning

confidence: 99%

“…Transcriptomics data were integrated into the generic model using troppo (Ferreira et al, 2020) to obtain tissue-specific models. Leaf, Inner bark, Reproduction or traumatic Phellogen, and Virgin…”

Section: Tissue-specific Modelsmentioning

confidence: 99%

The first multi-tissue diel genome-scale metabolic model of a woody plant highlights suberin biosynthesis pathways inQuercus suber

Cunha

Silva

Chaves

et al. 2021

Preprint

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“…Finally, the Quantum Yield and Assimilation Quotient predicted by the model are in accordance with values reported in literature. Another highlight is converting the generic model, reconstructed in merlin , into tissue-specific and multitissue models, corroborating the scalability and usability of merlin models using Troppo (66).…”

Section: Resultsmentioning

confidence: 65%

merlinv4.0: an updated platform for the reconstruction of high-quality genome-scale metabolic models

Capela

Lagoa

Rodrigues

et al. 2021

Preprint

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Genome-scale metabolic models have been recognized as useful tools for better understanding living organism's metabolism. Merlin (https://merlin-sysbio.org/) is an open-source and user-friendly resource that hastens these models' reconstruction process, conjugating manual, and automatic procedures, while leveraging user's expertise with a curation-oriented graphical interface. An updated and redesigned version of merlin is herein presented. Since 2015, several features were implemented in merlin, along with profound changes in the software architecture, operating flow, and graphical interface. The current version (4.0) includes the implementation of novel algorithms and third-party tools for genome functional annotation, draft assembly, model refinement, and curation. Such updates led to an increase in the user-base, resulting in multiple published works including genome metabolic (re-)annotation and model reconstruction of multiple (lower and higher) eukaryotes and prokaryotes.

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“…The remaining parts of the context-specific model reconstruction have also been implemented in several components of troppo. Both fastCORE and tINIT algorithms used in this work were run using in-house implementations, which had been validated in a previous work [47]. The EFMGapfill approach is a novel addition to this software package and was implemented using an in-house implementation of the k-shortest EFM enumeration already available as part of cobamp [49].…”

Section: Software Availabilitymentioning

confidence: 99%

“…A significant part of our model reconstruction pipeline has been implemented using the troppo framework [47], developed in-house but freely available for the community.…”

Section: Software Availabilitymentioning

confidence: 99%

A pipeline for the reconstruction and evaluation of context-specific human metabolic models at a large-scale

Vieira

Ferreira

Rocha

2021

Preprint

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Constraint-based (CB) metabolic models provide a mathematical framework and scaffold for in silico cell metabolism analysis and manipulation. In the past decade, significant efforts have been done to model human metabolism, enabled by the increased availability of multi-omics datasets and curated genome-scale reconstructions, as well as the development of several algorithms for context-specific model (CSM) reconstruction. Although CSM reconstruction has revealed insights on the deregulated metabolism of several pathologies, the process of reconstructing representative models of human tissues still lacks benchmarks and appropriate integrated software frameworks, since many tools required for this process are still disperse across various software platforms, some of which are proprietary. In this work, we address this challenge by assembling a scalable CSM reconstruction pipeline capable of integrating transcriptomics data in CB models. We combined omics preprocessing methods inspired by previous efforts with in-house implementations of existing CSM algorithms and new model refinement and validation routines, all implemented in the Troppo Python-based open-source framework. The pipeline was validated with multi-omics datasets from the Cancer Cell Line Encyclopedia (CCLE), also including reference fluxomics measurements for the MCF7 cell line. We reconstructed over 6000 models based on the Human-GEM template model for 733 cell lines featured in the CCLE, using MCF7 models as reference to find the best parameter combinations. These reference models outperform earlier studies using the same template by comparing gene essentiality and fluxomics experiments. We also analysed the heterogeneity of breast cancer cell lines, identifying key changes in metabolism related to cancer aggressiveness. Despite the many challenges in CB modelling, we demonstrate using our pipeline that combining transcriptomics data in metabolic models can be used to investigate key metabolic shifts. Significant limitations were found on these models ability for reliable quantitative flux prediction, thus motivating further work in genome-wide phenotype prediction

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Troppo - A Python Framework for the Reconstruction of Context-Specific Metabolic Models

Cited by 10 publications

References 23 publications

The first multi-tissue diel genome-scale metabolic model of a woody plant highlights suberin biosynthesis pathways inQuercus suber

The first multi-tissue diel genome-scale metabolic model of a woody plant highlights suberin biosynthesis pathways inQuercus suber

merlinv4.0: an updated platform for the reconstruction of high-quality genome-scale metabolic models

A pipeline for the reconstruction and evaluation of context-specific human metabolic models at a large-scale

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