ArticleScientific reports2016
Learning (from) the errors of a systems biology model.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Multiscale Responsive Kinetic Modeling: Quantifying Biomolecular Reaction Flux under Varying Electrochemical Conditions.Journal of chemical theory and computation · 2025Article
- Robust parameter estimation and identifiability analysis with hybrid neural ordinary differential equations in computational biology.NPJ systems biology and applications · 2024Article
- Multiscale Responsive Kinetic Modeling: Quantifying Biomolecular Reaction Flux under Varying Electrochemical Conditions.bioRxiv : the preprint server for biology · 2024Article
- What happened to the predicted COVID-19-induced suicide epidemic, and why?The Australian and New Zealand journal of psychiatry · 2023Article
- Artificial neural networks enable genome-scale simulations of intracellular signaling.Nature communications · 2022Article
- Development of a hybrid model for a partially known intracellular signaling pathway through correction term estimation and neural network modeling.PLoS computational biology · 2020Article
- Systems biology informed deep learning for inferring parameters and hidden dynamics.PLoS computational biology · 2020Article
- Searching for Errors in Models of Complex Dynamic Systems.Frontiers in physiology · 2020Article
- Identification of a time-varying intracellular signalling model through data clustering and parameter selection: application to NF-[inline-formula removed]B signalling pathway induced by LPS in the presence of BFA.IET systems biology · 2019Article
- Full observability and estimation of unknown inputs, states and parameters of nonlinear biological models.Journal of the Royal Society, Interface · 2019Article
- Metabolomics applied to maternal and perinatal health: a review of new frontiers with a translation potential.Clinics (Sao Paulo, Brazil) · 2019Review
- An engineering design approach to systems biology.Integrative biology : quantitative biosciences from nano to macro · 2017Review
- A Bayesian approach to estimating hidden variables as well as missing and wrong molecular interactions in ordinary differential equation-based mathematical models.Journal of the Royal Society, Interface · 2017Article
- Mechanistic modeling of mitosis: Insights from three collaborative case studies.Seminars in cell & developmental biologyReview
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Mathematical modelling is a labour intensive process involving several iterations of testing on real data and manual model modifications. In biology, the domain knowledge guiding model development is in many cases itself incomplete and uncertain. A major problem in this context is that biological systems are open. Missed or unknown external influences as well as erroneous interactions in the model could thus lead to severely misleading results. Here we introduce the dynamic elastic-net, a data driven mathematical method which automatically detects such model errors in ordinary differential equation (ODE) models. We demonstrate for real and simulated data, how the dynamic elastic-net approach can be used to automatically (i) reconstruct the error signal, (ii) identify the target variables of model error, and (iii) reconstruct the true system state even for incomplete or preliminary models. Our work provides a systematic computational method facilitating modelling of open biological systems under uncertain knowledge.
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