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Presentation to the Society of Toxicology 62nd Annual Meeting and ToxExpo March 2023 Search for CCTE records in EPA’s Science Inventory by typing in the title at this link. https://cfpub.epa.gov/si/si_public_search_results.cfm?advSearch=true&showCriteria=2&keyword=CCTE&TIMSType=&TIMSSubTypeID=&epaNumber=&ombCat=Any&dateBeginPublishedPresented=07/01/2017&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&DEID=&personName=&personID=&role=Any&journalName=&journalID=&publisherName=&publisherID=&sortBy=pubDate&count=25

Additional file 16: Fig. S7. Pseudo-time evolution of variables during simulations. The sequence of variable updating over each time steps after introducing a perturbation from the healthy state was saved as a timeseries and plotted. It shows the discrete behavior of the simulation and that a steady state is reached way before the duration of the perturbation (1000 time steps) is reached in such a way that maintain the perturbation longer would not change the output of the simulation once the perturbation is released.

Additional file 3: Table S2. List of variables and mouse genes correspondence. All mathematical variables and the corresponding node in the network have names written in upper cases and do not reflect the official human or mouse nomenclatures. To relate those variables to actual genes more easily, we provide this table of correspondence. A related mouse gene name and NCBI ID is indicated for each variable. Nevertheless, this is not exhaustive since some variables represent a group of factors or a family of ligands rather than a single factor.

Additional file 7: Table S3. Validation of transcriptional data-inferred regulatory interactions. Transcriptional interactions were inferred from the merged OA dataset. Inference was run with three algorithms and only interactions that were present in the results of the three algorithms (GENIE3, ARACNE, TIGRESS) were considered as additions to the model. An interaction was considered present for one algorithm if it scored higher than a threshold defined as the difference between the mean and standard deviation of all scores (Additional file 6, Data S2 ). Inferred interactions were validated whenever possible by looking for binding sites of the source transcription factor in the enhancer region of the target gene with GeneCards. The complete list of GeneHancer IDs provided by GeneCards are reported in the last column. ‘NA’ indicate that no binding site was found. The exact gene names and gene IDs that were queried in GeneCards are reported in the first columns.

Additional file 5: Fig. S1. Individual dataset clustering and heatmap. The microarray sub-datasets, used in the merged dataset, were also investigated individually. They were pre-processed (i.e. processing steps before merging and correcting for batch effect) in the same way as the merged dataset but the unsupervised clustering analysis was done on each sub-dataset separately in order to compare the biological or OA related information content before and after the data merging. The heatmaps show the expression profile of the same list of genes of interest than for the merged dataset (see Additional file 4, Data S1.). These expression datasets were submitted to unsupervised clustering with the Euclidean distance method and the Complete aggregation method in R thanks to the heatmap3 function from the Github repository https://github.com/obigriffith/biostar-tutorials/tree/master/Heatmaps . The headers indicate the GEO accession numbers of the 6 original datasets. Samples labeled as ‘WT’, for wild type, are in green, samples labeled as ‘OA’, for osteoarthritis, in red. The pre-labelling is the same as for the merged dataset (see Additional file 4, Data S1). When applicable, a grey scale indicate the time points (w stands for weeks, in the legend). For some datasets (e.g. GSE26475, GSE33656, GSE53857) the OA and the WT samples are well separated in different clusters, while for other the separation is not so clear. For instance, in GSE45793 the variance due the time point 6weeks is greater than the OA induced variance, while for weeks 1 and 2 OA and WT samples are well separated due to the OA condition.

Additional file 2. Supplementary computational method. Equations, mathematical framework and justification of deviation from the general rule in the equations.

The purpose of the document is to provide recommendations on the formatting, organization, and content of reports describing in-silico modelling and results in the field of computational fluid dynamics (CFD) and finite element analysis (FEA) for medical device regulatory submissions. Besides that, those recommendations also aim at helping clear and transparent communication about computational studies in interdisciplinary teams and at improving reproducibility of studies. This document has been built on published recommendations of various organizations concerned with modelling and simulations and V&V activities for health products, such as the Committee on Credible Practice of Modelling & Simulation in Healthcare, V&V sub-Committees of the American Society of Mechanical Engineers (ASME), the Food and Drug Administration (FDA), the European Medicines Agency (EMA) and the International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH). In addition, even though they are not specific to human health and health products, recommendations from NASA have also been accounted for as they provide a high-quality framework for CM&S in potentially high-risk applications1,2. The structure of the document has been built on the FDA guidance for reporting computational modeling studies in medical device submissions3, mainly., SIMCor (In-Silico testing and validation of Cardiovascular IMplantable devices) has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 101017578.

This document was developed to provide a standard operating procedure (SOP) on clinical data processing for creating virtual cohorts for in-silico models. The SOP is designed for SIMCor partners as well as for the scientific community. Hence, it formulates procedures and workflows for the processing of clinical data (in particular, imaging data) in the view of generating virtual cohorts. Given the heterogeneity of data, general recommendations are provided, rather than precise steps, with specific illustrations based on the SIMCor use case: transcatheter aortic valve implantation (TAVI). Standards and guidelines for data quality, data formats and data exchange were applied, when they existed. Confronting the current workflow with the evolving practice as the project continues may lead to updates, thus subsequent iterations are foreseen., SIMCor (In-Silico testing and validation of Cardiovascular IMplantable devices) has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 101017578.

Additional file 1. Primer sequences for gene expression analysis.

Additional file 2. Description of the modelling workflow with the in silico regulatory network model of chondrocyte differentiation and full simulation results.