Troppo - A Python framework for the reconstruction of context-specific metabolic models

The surge in high-throughput technology availability for molecular biology has enabled the development of powerful predictive tools for use in many applications, including (but not limited to) the diagnosis and treatment of human diseases such as cancer. Genome-scale metabolic models have shown some promise in clearing a path towards precise and personalized medicine, although some challenges still persist. The integration of omics data and subsequent creation of context-specific models for specific cells/tissues still poses a significant hurdle, and most current tools for this purpose have been implemented using proprietary software. Here, we present a new software tool developed in Python, troppo - Tissue-specific RecOnstruction and Phenotype Prediction using Omics data, implementing a large variety of context-specific reconstruction algorithms. Our framework and workflow are modular, which facilitates the development of newer algorithms or omics data sources.This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2019 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. The authors also thank the PhD scholarships funded by national funds through Fundacao para a Ciencia e Tecnologia, with references: SFRH/BD/133248/2017 (J.F.), SFRH/BD/118657/2016 (V.V.).info:eu-repo/semantics/publishedVersio

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

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.The authors thank the PhD scholarships co-funded by national funds and the European Social Fund through the Portuguese Foundation for Science and Technology (FCT), with references: SFRH/BD/118657/2016 (V.V.), SFRH/BD/133248/ 2017 (J.F.). This study was also supported by the FCT under the scope of the strategic funding of UIDB/04469/2020 unit and by LABBELS - Associate Laboratory in Biotechnology, Bioengineering and Microelectromechnaical Systems, LA/P/0029/2020.info:eu-repo/semantics/publishedVersio

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

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.Author summary Genome-scale models of human metabolism are promising tools capable of contextualising large omics datasets within a framework that enables analysis and manipulation of metabolic phenotypes. Despite various successes in applying these methods to provide mechanistic hypotheses for deregulated metabolism in disease, there is no standardized workflow to extract these models using existing methods and the tools required to do so are mostly implemented using proprietary software.We have assembled a generic pipeline to extract and validate context-specific metabolic models using multi-omics datasets and implemented it using the troppo framework. We first validate our pipeline using MCF7 cell line models and assess their ability to predict lethal gene knockouts as well as flux activity using multi-omics data. We also demonstrate how this approach can be generalized for large-scale transcriptomics datasets and used to generate insights on the metabolic heterogeneity of cancer and relevant features for other data mining approaches. The pipeline is available as part of an open-source framework that is generic for a variety of applications.Competing Interest StatementThe authors have declared no competing interest.The authors thank the PhD scholarships co-funded by national funds and the European Social Fund through the Portuguese Foundation for Science and Technology (FCT), with references: SFRH/BD/118657/2016 (V.V.), SFRH/BD/133248/2017 (J.F.). This study was also supported by the FCT under the scope of the strategic funding of UIDB/04469/2020 unit.info:eu-repo/semantics/publishedVersio

A model validation pipeline for healthy tissue genome-scale metabolic models

Dissertação de mestrado em BioinformáticaNos últimos anos, os métodos de alto rendimento disponibilizaram dados ómicos referentes a várias camadas da organização biológica, permitindo a integração do conhecimento de componentes individuais em modelos complexos, como modelos metabólicos à escala genómica (GSMMs). Estes podem ser analisados por métodos de modelação baseada em restrições(CBM), que facilitam abordagens preditivas in silico. Os modelos metabólicos humanos têm sido usados para estudar tecidos saudáveis e as suas doenças metabólicas associadas, como obesidade, diabetes e cancro. Modelos humanos genéricos podem ser integrados com dados contextuais por meio de algoritmos de reconstrução, com vista a produzir modelos metabólicos contextualizados (CSMs), que são normalmente melhores a capturar a variação entre diferentes tecidos e tipos de células. Como o corpo humano contém uma grande variedade de tecidos e tipos de células, os CSMs são frequentemente adotados como um meio de obter modelos metabólicos mais precisos de tecido humano saudável. No entanto, ao contrário de modelos de microrganismos e cancro, que acomodam vários métodos de validação, como a comparação de fluxos in silico ou de previsões de genes essenciais com dados experimentais, os métodos de validação facilmente aplicáveis a CSMs de tecido humano saudável podem ser mais limitados. Consequentemente, apesar de esforços continuados para atualizar os modelos humanos genéricos e algoritmos de reconstrução para extrair CSMs de alta qualidade, a sua validação continua a ser uma preocupação. Este trabalho apresenta uma pipeline para a extração e validação básica de CSMs de tecidos humanos normais derivados da integração de dados transcriptómicos com um modelo humano genérico. Todos os CSMs foram extraídos do modelo genérico Human-GEM publicado recentemente por Robinson et al. (2020), usando o package Troppo em Python e nos algoritmos de reconstrução fastCORE e tINIT nele implementados. Os CSMs extraídos correspondem a 11 tecidos saudáveis disponíveis no conjunto de dados GTEx v8. Antes da extração, métodos de aprendizagem máquina foram aplicados à seleção de um limiar para conversão em gene scores. Os modelos de maior qualidade foram obtidos com um limite mínimo global aplicado diretamente aos dados ómicos. A estratégia de validação focou-se no número de tarefas metabólicas passadas como um indicador de desempenho. Por último, este trabalho é acompanhado por Jupyter Notebooks, que incluem um guia de extração de modelos para novos utilizadores.n the past few years, high-throughput experimental methods have made omics data available for several layers of biological organization, enabling the integration of knowledge from individual components into complex modelssuch as genome-scale metabolic models (GSMMs). These can be analysed by constraint based modelling (CBM) methods, which facilitate in silico predictive approaches. Human metabolic models have been used to study healthy human tissues and their associated metabolic diseases, such as obesity, diabetes, and cancer. Generic human models can be integrated with contextual data through reconstruction algorithms to produce context-specific models (CSMs), which are typically better at capturing the variation between different tissues and cell types. As the human body contains a multitude of tissues and cell types, CSMs are frequently adopted as a means to obtain accurate metabolic models of healthy human tissues. However, unlike microorganisms’ or cancer models, which allow several methods of validation such as the comparison of in silico fluxes or gene essentiality predictions to experimental data, the validation methods easily applicable to CSMs of healthy human tissue are more limited. Consequently, despite continued efforts to update generic human models and reconstruction algorithms to extract high quality CSMs, their validation remains a concern. This work presents a pipeline for the extraction and basic validation of CSMs of normal human tissues derived from the integration of transcriptomics data with a generic human model. All CSMs were extracted from the Human-GEM generic model recently published by Robinson et al. (2020), relied on the open-source Troppo Python package and in the fastCORE and tINIT reconstruction algorithms implemented therein. CSMs were extracted for 11 healthy tissues available in the GTEx v8 dataset. Prior to extraction, machine learning methods were applied to threshold selection for gene scores conversion. The highest quality models were obtained with a global threshold applied to the omics data directly. The CSM validation strategy focused on the total number of metabolic tasks passed as a performance indicator. Lastly, this work is accompanied by Jupyter Notebooks, which include a beginner friendly model extraction guide

MADRID: a pipeline for MetAbolic Drug Repurposing IDentification

Summary: Human metabolic pathways offer numerous therapeutic targets to treat complex diseases such as autoimmunity and cancers. Metabolic modeling can help predict potential drug targets using in silico gene or reaction perturbations. However, systematic analyses of metabolic models require the integration of different modeling methods. MADRID is an easy-to-use integrated pipeline for developing metabolic models, running simulation, investigating gene inhibition effect on reactions, identifying repurposable drugs, and in fine predicting drug targets. It can be installed as a Docker image and includes easy to use steps in a jupyter notebook. Availability and implementation: The source code of the MADRID pipeline and Docker image are available at https://github.com/HelikarLab/MADRID

merlin, an improved framework for the reconstruction of high-quality genome-scale metabolic models

Genome-scale metabolic models have been recognised as useful tools for better understanding living organisms metabolism. merlin (https://www.merlin-sysbio.org/) is an open-source and user-friendly resource that hastens the models reconstruction process, conjugating manual and automatic procedures, while leveraging the user's expertise with a curation-oriented graphical interface. An updated and redesigned version of merlin is herein presented. Since 2015, several features have been implemented in merlin, along with deep changes in the software architecture, operational 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 increased the user base, resulting in multiple published works, including genome metabolic (re-)annotations and model reconstructions of multiple (lower and higher) eukaryotes and prokaryotes. merlin version 4.0 is the only tool able to perform template based and de novo draft reconstructions, while achieving competitive performance compared to state-of-the art tools both for well and less-studied organisms.Centre of Biological Engineering (CEB, UMinho); Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UIDB/04469/2020 unit; this work is a result of the project 22231/01/SAICT/2016: Biodata.pt Infraestrutura Portuguesa de Dados Biologicos, supported by the ´ PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF); FCT for providing PhD scholarships [DFA/BD/08789/2021 J.C, DFA/BD/8076/2020 E.C., SFRH/BD/139198/2018 to F.C., SFRH/BD/131916/2017 R. Rodrigues]; FCT for the Assistant Research contract of Oscar Dias obtained under CEEC Individual 2018.info:eu-repo/semantics/publishedVersio

Complexity, Emergent Systems and Complex Biological Systems:\ud Complex Systems Theory and Biodynamics. [Edited book by I.C. Baianu, with listed contributors (2011)]

An overview is presented of System dynamics, the study of the behaviour of complex systems, Dynamical system in mathematics Dynamic programming in computer science and control theory, Complex systems biology, Neurodynamics and Psychodynamics.\u

Online learning of physics during a pandemic: A report from an academic experience in Italy

The arrival of the Sars-Cov II has opened a new window on teaching physics in academia. Frontal lectures have left space for online teaching, teachers have been faced with a new way of spreading knowledge, adapting contents and modalities of their courses. Students have faced up with a new way of learning physics, which relies on free access to materials and their informatics knowledge. We decided to investigate how online didactics has influenced students’ assessments, motivation, and satisfaction in learning physics during the pandemic in 2020. The research has involved bachelor (n = 53) and master (n = 27) students of the Physics Department at the University of Cagliari (N = 80, 47 male; 33 female). The MANOVA supported significant mean differences about gender and university level with higher values for girls and master students in almost all variables investigated. The path analysis showed that student-student, student-teacher interaction, and the organization of the courses significantly influenced satisfaction and motivation in learning physics. The results of this study can be used to improve the standards of teaching in physics at the University of Cagliar

2018 GREAT Day Program

SUNY Geneseo’s Twelfth Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1012/thumbnail.jp