BioSimulators: a central registry of simulation engines and services for recommending specific tools.

Computational models have great potential to accelerate bioscience, bioengineering, and medicine. However, it remains challenging to reproduce and reuse simulations, in part, because the numerous formats and methods for simulating various subsystems and scales remain siloed by different software tools. For example, each tool must be executed through a distinct interface. To help investigators find and use simulation tools, we developed BioSimulators (https://biosimulators.org), a central registry of the capabilities of simulation tools and consistent Python, command-line and containerized interfaces to each version of each tool. The foundation of BioSimulators is standards, such as CellML, SBML, SED-ML and the COMBINE archive format, and validation tools for simulation projects and simulation tools that ensure these standards are used consistently. To help modelers find tools for particular projects, we have also used the registry to develop recommendation services. We anticipate that BioSimulators will help modelers exchange, reproduce, and combine simulations.

Reproducibility in systems biology modelling.

Reproducibility of scientific results is a key element of science and credibility. The lack of reproducibility across many scientific fields has emerged as an important concern. In this piece, we assess mathematical model reproducibility and propose a scorecard for improving reproducibility in this field.

BioModels Parameters: a treasure trove of parameter values from published systems biology models.

MOTIVATION: One of the major bottlenecks in building systems biology models is identification and estimation of model parameters for model calibration. Searching for model parameters from published literature and models is an essential, yet laborious task. RESULTS: We have developed a new service, BioModels Parameters, to facilitate search and retrieval of parameter values from the Systems Biology Markup Language models stored in BioModels. Modellers can now directly search for a model entity (e.g. a protein or drug) to retrieve the rate equations describing it; the associated parameter values (e.g. degradation rate, production rate, Kcat, Michaelis-Menten constant, etc.) and the initial concentrations. Currently, BioModels Parameters contains entries from over 84,000 reactions and 60 different taxa with cross-references. The retrieved rate equations and parameters can be used for scanning parameter ranges, model fitting and model extension. Thus, BioModels Parameters will be a valuable service for systems biology modellers. AVAILABILITY AND IMPLEMENTATION: The data are accessible via web interface and API. BioModels Parameters is free to use and is publicly available at https://www.ebi.ac.uk/biomodels/parameterSearch. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Spectroscopic Studies of Mössbauer, Infrared, and Laser-Induced Luminescence for Classifying Rare-Earth Minerals Enriched in Iron-Rich Deposits.

Rare-earth (RE) phosphates often appear as an accessory phase in igneous or metamorphic rocks; however, these rocks are composed of myriad chemical elements and nuclides that interfere with the qualitative or quantitative analyses of the RE phosphates over a range of concentrations in the absence of a pretreatment. In addition, the limit of each analytical methodology constrains the approach as well as the usefulness of the results in geoscience applications. Here, we report the specific mineral characterization of RE-containing ores from Yen Phu mine, Vietnam, using a range of state-of-the-art spectroscopic techniques in conjunction with microscopy: Mössbauer spectroscopy, infrared microspectroscopy, time-resolved laser-induced fluorescence spectroscopy (TRLFS), and scanning electron microscopy with energy-dispersive X-ray spectroscopy. Because the distribution of each element in the deposit differs, such combinatorial works are necessary and could lead to more plausible answers to questions surrounding the point of origin of RE elements. The results of our Mössbauer spectroscopic analysis indicate that the three ores sampled at different locations all contain magnetite-like, hematite-like, and iron(III) salts other than hematite. In addition, we confirmed the presence of phosphate around the grain boundary in the magnetite-like mineral phase by infrared microspectroscopic analysis. The present analytical findings of trace amounts of europium(III) using TRLFS suggest that the europium ions generate identical luminescence spectra despite being embedded in three different matrices of iron minerals. This demonstration highlights the benefits of combinatorial spectroscopic analyses to gain insights into the effects of the environment of REs on their solid-state chemistry and shows the potential utility of TRLFS as a resource mining tool. Further applications of this approach in the analytical screening of rocks and minerals are feasible.

BioModels-15 years of sharing computational models in life science.

Computational modelling has become increasingly common in life science research. To provide a platform to support universal sharing, easy accessibility and model reproducibility, BioModels (https://www.ebi.ac.uk/biomodels/), a repository for mathematical models, was established in 2005. The current BioModels platform allows submission of models encoded in diverse modelling formats, including SBML, CellML, PharmML, COMBINE archive, MATLAB, Mathematica, R, Python or C++. The models submitted to BioModels are curated to verify the computational representation of the biological process and the reproducibility of the simulation results in the reference publication. The curation also involves encoding models in standard formats and annotation with controlled vocabularies following MIRIAM (minimal information required in the annotation of biochemical models) guidelines. BioModels now accepts large-scale submission of auto-generated computational models. With gradual growth in content over 15 years, BioModels currently hosts about 2000 models from the published literature. With about 800 curated models, BioModels has become the world's largest repository of curated models and emerged as the third most used data resource after PubMed and Google Scholar among the scientists who use modelling in their research. Thus, BioModels benefits modellers by providing access to reliable and semantically enriched curated models in standard formats that are easy to share, reproduce and reuse.

BioModels: expanding horizons to include more modelling approaches and formats.

BioModels serves as a central repository of mathematical models representing biological processes. It offers a platform to make mathematical models easily shareable across the systems modelling community, thereby supporting model reuse. To facilitate hosting a broader range of model formats derived from diverse modelling approaches and tools, a new infrastructure for BioModels has been developed that is available at http://www.ebi.ac.uk/biomodels. This new system allows submitting and sharing of a wide range of models with improved support for formats other than SBML. It also offers a version-control backed environment in which authors and curators can work collaboratively to curate models. This article summarises the features available in the current system and discusses the potential benefit they offer to the users over the previous system. In summary, the new portal broadens the scope of models accepted in BioModels and supports collaborative model curation which is crucial for model reproducibility and sharing.

Fate and transport of perfluoro- and polyfluoroalkyl substances including perfluorooctane sulfonamides in a managed urban water body.

Transport and fate of perfluoro- and polyfluoroalkyl substances (PFASs) in an urban water body that receives mainly urban runoff was investigated. Water, suspended solids, and sediment samples were collected during the monsoon (wet) and inter-monsoon (dry) season at different sites and depths. Samples were analyzed for C7 to C12 perfluoroalkyl carboxylate homologues (PFCAs) (PFHpA, PFOA, PFNA, PFDA, PFUnA, PFDoA), perfluorohexane, perfluorooctane, and 6:2-fluorotelomer sulfonate (PFHxS, PFOS, and 6:2FtS, respectively), perfluorooctane sulfonamide (FOSA), N-ethyl FOSA (sulfluramid), N-ethyl sulfonamidoethanol (N-EtFOSE), and N-methyl and N-ethyl sulfonamidoacetic acid (N-EtFOSAA and N-MeFOSAA, respectively). Concentrations in wet samples were only slightly higher. The sum total PFAS (ΣPFAS) concentrations dissolved in the aqueous phase and sorbed to suspended solids (SS) ranged from 107 to 253 ng/L and 11 to 158 ng/L, respectively. PFOA, PFOS, PFNA, PFHxS, and PFDA contributed most (approximately 90 %) to the dissolved ΣPFASs. N-EtFOSA dominated the particulate PFAS burden in wet samples. K D values of PFOA and PFOS calculated from paired SS and water concentrations varied widely (1.4 to 13.7 and 1.9 to 98.9 for PFOA and PFOS, respectively). Field derived K D was significantly higher than laboratory K D suggesting hydrophobic PFASs sorbed to SS resist desorption. The ΣPFAS concentrations in the top sedimentary layer ranged from 8 to 42 µg/kg and indicated preferential accumulation of the strongly sorbing long-chain PFASs. The occurrence of the metabolites N-MeFOSAA, N-EtFOSAA and FOSA in the water column and sediments may have resulted from biological or photochemical transformations of perfluorooctane sulfonamide precursors while the absence of FOSA, N-EtFOSA and 6:2FtS in sediments was consistent with biotransformation.