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author = {Smith, Lucian and Malik-Sheriff, Rahuman S. and Nguyen, Tung V. N. and Hermjakob, Henning and Karr, Jonathan and Shaikh, Bilal and Drescher, Logan and Moraru, Ion I. and Schaff, James C. and Agmon, Eran and Patrie, Alexander A. and Blinov, Michael L. and Hellerstein, Joseph L. and May, Elebeoba E. and Nickerson, David P. and Gennari, John H. and Sauro, Herbert M.},
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journal = {bioRxiv},
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title = {Using {SED-ML} for reproducible curation: Verifying {BioModels} across multiple simulation engines},
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year = {2025},
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abstract = {The BioModels Repository contains over 1000 manually curated mechanistic models drawn from published literature, most of which are encoded in the Systems Biology Markup Language (SBML). This community-based standard formally specifies each model, but does not describe the computational experimental conditions to run a simulation. Therefore, it can be challenging to reproduce any given figure or result from a publication with an SBML model alone. The Simulation Experiment Description Markup Language (SED-ML) provides a solution: a standard way to specify exactly how to run a specific experiment that corresponds to a specific figure or result. BioModels was established years before SED-ML, and both systems evolved over time, both in content and acceptance. Hence, only about half of the entries in BioModels contained SED-ML files, and these files reflected the version of SED-ML that was available at the time. Additionally, almost all of these SED-ML files had at least one minor mistake that made them invalid. To make these models and their results more reproducible, we report here on our work updating, correcting and providing new SED-ML files for 1055 curated mechanistic models in BioModels. In addition, because SED-ML is implementation-independent, it can be used for verification, demonstrating that results hold across multiple simulation engines. Here, we use a wrapper architecture for interpreting SED-ML, and report verification results across five different ODE-based biosimulation engines. Our work with SED-ML and the BioModels collection aims to improve the utility of these models by making them more reproducible and credible.Author summary Reproducing computationally-derived scientific results seems like it should be straightforward, but is often elusive. Code is lost, file formats change, and knowledge of what was done is only partially recorded and/or forgotten. Model repositories such as BioModels address this failing in the Systems Biology domain by encoding models in a standard format that can reproduce a figure from the paper from which it was drawn. Here, we delved into the BioModels repository to ensure that every curated model additionally contained instructions on what to do with that model, and then tested those instructions on a variety of simulation platforms. Not only did this improve the BioModels repository itself, but also improved the infrastructure necessary to run these validation comparisons in the future.Author contributions LS: Writing, Conceptualization, Data Curation, Investigation, Methodology, Project Administration, Software, Validation. RMS: Reading, Writing, Data Curation, Methodology TN: Reading, Data Curation, Methodology HH: Reading JK: Conceptualization, Data Curation, Investigation, Methodology, Software. BS: Software LD: Software IIM: Reading, Conceptualization, Funding JCS: Software, Methodology EA: Reading, Writing AAP: Software MLB: Reading, Writing JH: Writing, Methodology EM: Reading, Writing DPN: Reading, Writing, Methodology JG: Reading, Writing, Methodology HMS: Reading, Writing, FundingCompeting Interest StatementThe authors have declared no competing interest.},
author = {Noordijk, Ben and Reinders, Marcel and van Dijk, Aalt D.J. and de Ridder, Dick},
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journal = {bioRxiv},
@@ -1637,6 +1622,77 @@ @Article{PhilippsSch2025
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publisher = {Springer Science and Business Media LLC},
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}
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@Article{SmithMal2025a,
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author = {Smith, Lucian P. and Malik-Sheriff, Rahuman S. and Nguyen, Tung V. N. and Hermjakob, Henning and Karr, Jonathan and Shaikh, Bilal and Drescher, Logan and Moraru, Ion I. and Schaff, James C. and Agmon, Eran and Patrie, Alexander A. and Blinov, Michael L. and Hellerstein, Joseph L. and May, Elebeoba E. and Nickerson, David P. and Gennari, John H. and Sauro, Herbert M.},
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journal = {PLOS Computational Biology},
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title = {Verification and reproducible curation of the {BioModels} repository},
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year = {2025},
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month = {12},
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number = {12},
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pages = {1-18},
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volume = {21},
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abstract = {The BioModels Repository contains over 1000 manually curated mechanistic models from published literature, most often encoded in the Systems Biology Markup Language (SBML). This community-based standard formally specifies each model, but does not describe the computational experimental conditions to run a simulation and collect data. Therefore, it can be challenging to reproduce any figure or result from a publication with an SBML model alone. The Simulation Experiment Description Markup Language (SED-ML) provides a solution: a standard way to specify exactly how to run an experiment corresponding to a specific figure or result. BioModels was established years before SED-ML, and both systems evolved over time, both in content and acceptance. Hence, only about half of the entries in BioModels contained SED-ML files, and these files reflected the version of SED-ML that was available at the time. Additionally, almost all of these SED-ML files had at least one minor mistake that made them impossible to run. To make these models and their results more reproducible, we report here on our work updating, correcting and generating new SED-ML files for 1055 curated mechanistic models in BioModels. In addition, because SED-ML is implementation-independent, it can be used for verification, demonstrating that results hold across multiple simulation engines. We tested, corrected, and improved over 450 existing SED-ML files in the BioModels database, and created basic files for the rest of the entries. Then, we used a wrapper architecture for interpreting SED-ML, and report verification results across five different ODE-based biosimulation engines, after further improving the models, the wrappers, and the engines themselves. Our work with SED-ML and the BioModels collection aims to improve the utility of these models by making them more reproducible and credible. Improved reproducibility means these models are now even more fit for re-use, such as in new investigations and as components of multiscale models.},
author = {Callenbach, Aaron and Dore{\v s}i{\'c}, Domagoj and D{\"u}ster, Robert and Nakonecnij, Vanessa and Dudkin, Erika and Geyer, Matthias and Hasenauer, Jan},
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journal = {bioRxiv},
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title = {Quantitative modeling of {P-TEFb} mediated {CTD} phosphorylation identifies local cooperativity},
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year = {2025},
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abstract = {Fine-tuned regulation of RNA polymerase II (Pol II) activity is essential for accurate gene expression. A key layer of this regulation involves phosphorylation of Pol II{\textquoteright}s C-terminal domain (CTD), a repetitive heptapeptide tail that coordinates transcription and RNA-processing factors. The kinase P-TEFb plays a major role in this process, yet its precise phosphorylation mechanism remains unclear. Previous in vitro studies have suggested a distributive mode of action based largely on qualitative inspection of mass spectrometry data rather than quantitative analysis. Here, we use mathematical modeling of CTD phosphorylation to explore whether local context, such as neighboring phosphorylations or directional biases, affects PTEFb activity on the CTD. Our results indicate that P-TEFb acts distributively but with pronounced local cooperativity: repeats adjacent to phosphorylated sites are modified at higher rates. We find no evidence for directional bias, although the limited positional resolution of the data precludes a definitive conclusion. These results identify local context as an important factor in P-TEFb-mediated CTD phosphorylation and establish a quantitative modeling framework for dissecting multi-site modification dynamics.Competing Interest StatementThe authors have declared no competing interest.Deutsche Forschungsgemeinschaft, https://ror.org/018mejw64, EXC 2047-390685813, EXC 2151-390873048European Research Council, GA number 101126146, Advanced Grant NalpACTUniversity of Bonn, https://ror.org/041nas322, Schlegel Professorship of Jan Hasenauer},
author = {Jakob Vanhoefer and Antonia Körner and Domagoj Doresic and Jan Hasenauer and Dilan Pathirana},
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title = {Scalable branch-and-bound model selection with non-monotonic criteria including {AIC}, {BIC} and {Mallows's Cp}},
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year = {2025},
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archiveprefix = {arXiv},
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creationdate = {2025-12-18T19:54:15},
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eprint = {2512.12221},
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modificationdate = {2025-12-18T19:55:33},
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primaryclass = {q-bio.QM},
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url = {https://arxiv.org/abs/2512.12221},
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}
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@Misc{ArrudaBra2025,
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author = {Jonas Arruda and Niels Bracher and Ullrich Köthe and Jan Hasenauer and Stefan T. Radev},
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title = {Diffusion Models in Simulation-Based Inference: A Tutorial Review},
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year = {2025},
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archiveprefix = {arXiv},
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creationdate = {2026-01-02T08:59:47},
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eprint = {2512.20685},
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modificationdate = {2026-01-02T08:59:47},
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primaryclass = {stat.ML},
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url = {https://arxiv.org/abs/2512.20685},
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}
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@Article{LakrisenkoIse2026,
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author = {Lakrisenko, Polina and Isensee, J{\"o}rg and Hucho, Tim and Weindl, Daniel and Hasenauer, Jan},
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journal = {bioRxiv},
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title = {A mechanistic model of protein kinase {A} dynamics under pro- and anti-nociceptive inputs},
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year = {2026},
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abstract = {Protein kinase A (PKA) is a central integrator of nociceptive signaling, yet a quantitative account of how pro- and anti-nociceptive inputs shape its dynamics remains incomplete. Here, we develop a mechanistic model of PKA activity in nociceptive neurons that explicitly links receptor activation to downstream kinase regulation. Using time-course and dose-response measurements, we infer unknown process parameters and quantify parameter and prediction uncertainties to ensure robust conclusions. The model captures the activation of PKA by serotonin and forskolin and its suppression by opioids. We show how the model can be used for the assessment of alternative circuit topologies, and demonstrate that receptor context and stimulation history reconfigure PKA responsiveness, providing testable predictions for opioid modulation under clinically relevant dosing. This framework offers a principled basis for integrating PKA with broader pain-signaling networks, supports rational exploration of combination therapies, and establishes a general strategy for disentangling neuromodulatory control of kinase activity.Author summary Pain perception is modulated by a complex network of signaling pathways activated by different receptors with opposing effects. A key player in this process is protein kinase A (PKA), whose regulation by both serotonin and opioid receptors is not yet fully understood. In this study, we developed a mathematical model to investigate how these opposing signals affect PKA activity in sensory neurons. After estimating the unknown model parameters from a comprehensive dataset, we were able to quantitatively analyze the dynamic behavior of the system and use it for comparison of alternative circuit topologies. Our model provides a valuable tool for integrating diverse molecular interactions involved in pain processing and could help guide future efforts to develop better treatments for chronic pain and reduce opioid tolerance.Competing Interest StatementThe authors have declared no competing interest.},
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