ArticleBioinformatics (Oxford, England)2019
Benchmark problems for dynamic modeling of intracellular processes.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 1 of them a synthesis that pooled it.
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43 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Within-host dynamics of antiviral treatment with remdesivir for SARS-CoV-2 infection.Journal of the Royal Society, Interface · 2024Article
- Efficient parameter estimation for ODE models of cellular processes using semi-quantitative data.Bioinformatics (Oxford, England) · 2024Article
- A mathematical model for the within-host (re)infection dynamics of SARS-CoV-2.Mathematical biosciences · 2024Article
- Posterior marginalization accelerates Bayesian inference for dynamical models of biological processes.iScience · 2023Article
- Uncovering specific mechanisms across cell types in dynamical models.PLoS computational biology · 2023Article
- Profile-Wise Analysis: A profile likelihood-based workflow for identifiability analysis, estimation, and prediction with mechanistic mathematical models.PLoS computational biology · 2023Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationDynamic models are used in systems biology to study and understand cellular processes like gene regulation or signal transduction. Frequently, ordinary differential equation (ODE) models are used to model the time and dose dependency of the abundances of molecular compounds as well as interactions and translocations. A multitude of computational approaches, e.g. for parameter estimation or uncertainty analysis have been developed within recent years. However, many of these approaches lack proper testing in application settings because a comprehensive set of benchmark problems is yet missing.
resultsWe present a collection of 20 benchmark problems in order to evaluate new and existing methodologies, where an ODE model with corresponding experimental data is referred to as problem. In addition to the equations of the dynamical system, the benchmark collection provides observation functions as well as assumptions about measurement noise distributions and parameters. The presented benchmark models comprise problems of different size, complexity and numerical demands. Important characteristics of the models and methodological requirements are summarized, estimated parameters are provided, and some example studies were performed for illustrating the capabilities of the presented benchmark collection. AVAILABILITY AND IMPLEMENTATION: The models are provided in several standardized formats, including an easy-to-use human readable form and machine-readable SBML files. The data is provided as Excel sheets. All files are available at https://github.com/Benchmarking-Initiative/Benchmark-Models, including step-by-step explanations and MATLAB code to process and simulate the models. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.