Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.
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http://dx.doi.org/10.15252/msb.20199110 | DOI Listing |
World J Surg
January 2025
Department of Human Structure and Repair, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium.
Background: Chest tube insertions (CTI) have a high complication rate, warranting a dedicated Simulation-Based Mastery Learning (SBML) curriculum to acquire technical skills. This randomized controlled trial compares residents' skills in CTI after completing a SBML curriculum with those enrolled in a traditional residency program.
Methods: Junior residents were baseline tested on cognitive and technical skills (Thiel bodies) before randomization into an intervention and control group.
Brief Bioinform
September 2024
Laboratory of Systems and Synthetic Biology (SSB), Wageningen University and Research, Agrotechnology and Food Sciences, Stippeneng 4, 6708 WE Wageningen, The Netherlands.
Systems biology aims to understand living organisms through mathematically modeling their behaviors at different organizational levels, ranging from molecules to populations. Modeling involves several steps, from determining the model purpose to developing the mathematical model, implementing it computationally, simulating the model's behavior, evaluating, and refining the model. Importantly, model simulation results must be reproducible, ensuring that other researchers can obtain the same results after writing the code de novo and/or using different software tools.
View Article and Find Full Text PDFJ Gastroenterol Hepatol
December 2024
Department of Dermatology and Venereology, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia.
Background And Aim: The simulation-based mastery learning (SBML) method holds promise for improving the efficiency and effectiveness of endoscopy training. However, further study is required to establish its advantages over the traditional method. We aim to prospectively compare outcomes between gastrointestinal endoscopy trainees taught using SBML and those trained using conventional apprenticeship methods for upper endoscopy.
View Article and Find Full Text PDFBioinform Adv
October 2024
Institute for Applied Mathematics (IAC) "Mauro Picone", National Research Council (CNR), Rome 00185, Italy.
Summary: We present , an R package able to perform , , and data extraction from Systems Biology Markup Language (SBML) documents (up to Level 3) in tabular data structures (i.e. R dataframes) to easily access and handle the richness of the biological information.
View Article and Find Full Text PDFBioinformatics
August 2024
Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Física, Buenos Aires 1426, Argentina.
Motivation: Chemical reaction networks (CRNs) play a pivotal role in diverse fields such as systems biology, biochemistry, chemical engineering, and epidemiology. High-level definitions of CRNs enables to use various simulation approaches, including deterministic and stochastic methods, from the same model. However, existing Python tools for simulation of CRN typically wrap external C/C++ libraries for model definition, translation into equations and/or numerically solving them, limiting their extensibility and integration with the broader Python ecosystem.
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