ArticleJournal of mathematical biology2020
Parameterization of mechanistic models from qualitative data using an efficient optimal scaling approach.
Article in Journal of mathematical biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Using PyBioNetFit to leverage qualitative and quantitative data in biological model parameterization and uncertainty quantification.Frontiers in immunology · 2026Article
- CrossLabFit: A novel framework for integrating qualitative and quantitative data across multiple labs for model calibration.PLoS computational biology · 2025Article
- Efficient parameter estimation for ODE models of cellular processes using semi-quantitative data.Bioinformatics (Oxford, England) · 2024Article
- Iterative design of training data to control intricate enzymatic reaction networks.Nature communications · 2024Article
- pyPESTO: a modular and scalable tool for parameter estimation for dynamic models.Bioinformatics (Oxford, England) · 2023Article
- Model certainty in cellular network-driven processes with missing data.PLoS computational biology · 2023Article
- A protocol for dynamic model calibration.Briefings in bioinformatics · 2022Article
- PEtab-Interoperable specification of parameter estimation problems in systems biology.PLoS computational biology · 2021Article
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3 authors.
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Abstract
Quantitative dynamical models facilitate the understanding of biological processes and the prediction of their dynamics. These models usually comprise unknown parameters, which have to be inferred from experimental data. For quantitative experimental data, there are several methods and software tools available. However, for qualitative data the available approaches are limited and computationally demanding. Here, we consider the optimal scaling method which has been developed in statistics for categorical data and has been applied to dynamical systems. This approach turns qualitative variables into quantitative ones, accounting for constraints on their relation. We derive a reduced formulation for the optimization problem defining the optimal scaling. The reduced formulation possesses the same optimal points as the established formulation but requires less degrees of freedom. Parameter estimation for dynamical models of cellular pathways revealed that the reduced formulation improves the robustness and convergence of optimizers. This resulted in substantially reduced computation times. We implemented the proposed approach in the open-source Python Parameter EStimation TOolbox (pyPESTO) to facilitate reuse and extension. The proposed approach enables efficient parameterization of quantitative dynamical models using qualitative data.
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