ArticleNPJ systems biology and applications2024
Robust parameter estimation and identifiability analysis with hybrid neural ordinary differential equations in computational biology.
Article in NPJ systems biology and applications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Universal differential equations for quantifying NF-κB-p53 signaling crosstalk.Bioinformatics (Oxford, England) · 2026Article
- A Hybrid Machine Learning Framework for Interpretable Kinetics of α-Tocopherol and Myricetin Synergism.Journal of food science · 2026Article
- A novel approach to quantify out-of-distribution uncertainty in Neural and Universal Differential Equations.NPJ systems biology and applications · 2026Article
- Physics-Informed Machine Learning in Biomedical Science and Engineering.Annual review of biomedical engineering · 2026Review
- The Potential of Digital Twins for Pediatric Rare Diseases.CPT: pharmacometrics & systems pharmacology · 2026Review
- Implementing physics-informed neural networks with deep learning for differential equations.Frontiers in artificial intelligence · 2026Article
- Plant responses to gaseous pollutants, biochemical and transcriptomic insights.Frontiers in plant science · 2026Review
- Building Hybrid Pharmacometric-Machine Learning Models in Oncology Drug Development: Current State and Recommendations.CPT: pharmacometrics & systems pharmacology · 2026Review
- Structural and practical identifiability of within-host models of virus dynamics - a review.Current opinion in systems biology · 2025Article
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5 authors.
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Abstract
Parameter estimation is one of the central challenges in computational biology. In this paper, we present an approach to estimate model parameters and assess their identifiability in cases where only partial knowledge of the system structure is available. The partially known model is embedded into a system of hybrid neural ordinary differential equations, with neural networks capturing unknown system components. Integrating neural networks into the model presents two main challenges: global exploration of the mechanistic parameter space during optimization and potential loss of parameter identifiability due to the neural network flexibility. To tackle these challenges, we treat biological parameters as hyperparameters, allowing for global search during hyperparameter tuning. We then conduct a posteriori identifiability analysis, extending a well-established method for mechanistic models. The pipeline performance is evaluated on three test cases designed to replicate real-world conditions, including noisy data and limited system observability.
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