ArticleJournal of the Royal Society, Interface2017
A Bayesian approach to estimating hidden variables as well as missing and wrong molecular interactions in ordinary differential equation-based mathematical models.
Article in Journal of the Royal Society, Interface, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 20 citations in OpenAlex.
- Towards an interpretable deep learning model of cancer.NPJ precision oncology · 2025Review
- Artificial neural networks enable genome-scale simulations of intracellular signaling.Nature communications · 2022Article
- A Bayesian Approach to Extracting Kinetic Information from Artificial Enzymatic Networks.Analytical chemistry · 2022Article
- Numerical method for parameter inference of systems of nonlinear ordinary differential equations with partial observations.Royal Society open science · 2021Article
- 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
- Full observability and estimation of unknown inputs, states and parameters of nonlinear biological models.Journal of the Royal Society, Interface · 2019Article
- Computational Methods for Estimating Molecular System from Membrane Potential Recordings in Nerve Growth Cone.Scientific reports · 2018Article
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3 authors at 2 institutions in 1 country.
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Abstract
Ordinary differential equations (ODEs) are a popular approach to quantitatively model molecular networks based on biological knowledge. However, such knowledge is typically restricted. Wrongly modelled biological mechanisms as well as relevant external influence factors that are not included into the model are likely to manifest in major discrepancies between model predictions and experimental data. Finding the exact reasons for such observed discrepancies can be quite challenging in practice. In order to address this issue, we suggest a Bayesian approach to estimate hidden influences in ODE-based models. The method can distinguish between exogenous and endogenous hidden influences. Thus, we can detect wrongly specified as well as missed molecular interactions in the model. We demonstrate the performance of our Bayesian dynamic elastic-net with several ordinary differential equation models from the literature, such as human JAK-STAT signalling, information processing at the erythropoietin receptor, isomerization of liquid
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