M
Mikael Sunnåker
Researcher at Swiss Institute of Bioinformatics
Publications - 12
Citations - 795
Mikael Sunnåker is an academic researcher from Swiss Institute of Bioinformatics. The author has contributed to research in topics: Model selection & Systems biology. The author has an hindex of 9, co-authored 12 publications receiving 700 citations. Previous affiliations of Mikael Sunnåker include ETH Zurich.
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Journal ArticleDOI
Approximate Bayesian computation
Mikael Sunnåker,Alberto Giovanni Busetto,Elina Numminen,Jukka Corander,Matthieu Foll,Christophe Dessimoz,Christophe Dessimoz,Christophe Dessimoz +7 more
TL;DR: Approximate Bayesian computation (ABC) constitutes a class of computational methods rooted in Bayesian statistics that widen the realm of models for which statistical inference can be considered and exacerbates the challenges of parameter estimation and model selection.
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Near-optimal experimental design for model selection in systems biology
Alberto Giovanni Busetto,Alain Hauser,Gabriel Krummenacher,Mikael Sunnåker,Sotiris Dimopoulos,Cheng Soon Ong,Jörg Stelling,Joachim M. Buhmann +7 more
TL;DR: This study introduces an efficient method for experimental design aimed at selecting dynamical models from data that exhibits the best polynomial-complexity constant approximation factor, unless P = NP.
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Automatic Generation of Predictive Dynamic Models Reveals Nuclear Phosphorylation as the Key Msn2 Control Mechanism
Mikael Sunnåker,Mikael Sunnåker,Elías Zamora-Sillero,Reinhard Dechant,Christina Ludwig,Alberto Giovanni Busetto,Andreas Wagner,Andreas Wagner,Andreas Wagner,Joerg Stelling,Joerg Stelling +10 more
TL;DR: A computational method is described that incorporates all hypothetical mechanisms about the architecture of a biological system into a single model and automatically generates a set of simpler models compatible with observational data.
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A Method for Zooming of Nonlinear Models of Biochemical Systems
TL;DR: This paper introduces a novel method for reduction of biochemical models that is compatible with the concept of zooming, and extends the applicability of the method that was previously developed for zooming of linear biochemical models to nonlinear models.
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Efficient Characterization of Parametric Uncertainty of Complex (Bio)chemical Networks
TL;DR: This work proposes a deterministic computational interpolation scheme which identifies most significant expansion coefficients adaptively and is based on adaptive Smolyak interpolation of the parametric solution at judiciously and adaptively chosen points in parameter space.