Data Management in Computational Systems Biology: Exploring Standards, Tools, Databases, and Packaging Best Practices

Stanford, Natalie J.; Scharm, Martin; Dobson, Paul D.; Golebiewski, Martin; Hucka, Michael; Kothamachu, Varun B.; Nickerson, David; Owen, Stuart; Pahle, Jürgen; Wittig, Ulrike; Waltemath, Dagmar; Goble, Carole; Mendes, Pedro; Snoep, Jacky L.

doi:10.1007/978-1-4939-9736-7_17

Cited by 7 publications

(6 citation statements)

References 122 publications

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“…FAIRDOM-SEEK's 'Search' includes an external search in the BioModels repository. It integrates the BiVeS tool for describing differences between model versions (Scharm et al, 2016). Integration of the tools JWS online and COPASI (Mendes et al, 2009) enables users to run simulations of SBML models directly in the system.…”

Section: Data and Metadatamentioning

confidence: 99%

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Systems Biology in ELIXIR: modelling in the spotlight

Martins dos Santos,

Anton,

Szomolay

et al. 2024

F1000Res

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In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR’s future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.

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Section: Data and Metadatamentioning

confidence: 99%

“…Ploemen et al, 1997;Sharma et al, 2020]. It is also possible to include PBPK models as part of broader, multiscale, systems biology models (Sluka et al, 2016). PBPK modelling is also a key element of a safe-by-design approach for material development (for instance in the VHP4Safety EU project).…”

Section: Industrial Embeddingmentioning

confidence: 99%

Systems Biology in ELIXIR: modelling in the spotlight

Martins dos Santos,

Anton,

Szomolay

et al. 2024

F1000Res

Self Cite

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“…Furthermore, software tools and repositories used in the modeling domain are introduced. More information on standards in systems biology is given in articles by Waltemath and Wolkenhauer [252] and Stanford et al [253].…”

Section: Microbiome Modeling Requires Standards Software and Reposito...mentioning

confidence: 99%

Microbiome Modeling: A Beginner’s Guide

Lange,

Kranert,

Krüger

et al. 2024

Preprint

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Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with themselves, their environment, and hosts via metabolites and signaling molecules. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metagenomics methods, and modeling formalisms. Furthermore, the review explains model building, examples of microbiome model applications for prediction, optimization, and control. It concludes with guidelines, software, and repositories for modeling. Each section provides entry-level information, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.

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“…Another challenge is managing metadata [ 45 , 46 ], especially for complex, large, multisite, heterogeneous datasets [ 47 ]. In an ideal scenario, all metadata related to acquired datasets would be readily accessible and sufficient for data sharing.…”

Section: Introductionmentioning

confidence: 99%

Data management strategy for a collaborative research center

et al. 2022

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The importance of effective research data management (RDM) strategies to support the generation of Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience data grows with each advance in data acquisition techniques and research methods. To maximize the impact of diverse research strategies, multidisciplinary, large-scale neuroscience research consortia face a number of unsolved challenges in RDM. While open science principles are largely accepted, it is practically difficult for researchers to prioritize RDM over other pressing demands. The implementation of a coherent, executable RDM plan for consortia spanning animal, human, and clinical studies is becoming increasingly challenging. Here, we present an RDM strategy implemented for the Heidelberg Collaborative Research Consortium. Our consortium combines basic and clinical research in diverse populations (animals and humans) and produces highly heterogeneous and multimodal research data (e.g., neurophysiology, neuroimaging, genetics, behavior). We present a concrete strategy for initiating early-stage RDM and FAIR data generation for large-scale collaborative research consortia, with a focus on sustainable solutions that incentivize incremental RDM while respecting research-specific requirements.

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Data Management in Computational Systems Biology: Exploring Standards, Tools, Databases, and Packaging Best Practices

Cited by 7 publications

References 122 publications

Systems Biology in ELIXIR: modelling in the spotlight

Systems Biology in ELIXIR: modelling in the spotlight

Microbiome Modeling: A Beginner’s Guide

Data management strategy for a collaborative research center

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