ArticleRoyal Society open science2019
Parameter inference in dynamical systems with co-dimension 1 bifurcations.
Article in Royal Society open science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Approximate Bayesian computation for inferring Waddington landscapes from single-cell data.Royal Society open science · 2024Article
- Review
- Inferring kinetic parameters of oscillatory gene regulation from single cell time-series data.Journal of the Royal Society, Interface · 2021Article
- Global search metaheuristics for neural mass model calibration.Imaging neuroscience (Cambridge, Mass.)Article
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2 authors.
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Abstract
Dynamical systems with intricate behaviour are all-pervasive in biology. Many of the most interesting biological processes indicate the presence of bifurcations, i.e. phenomena where a small change in a system parameter causes qualitatively different behaviour. Bifurcation theory has become a rich field of research in its own right and evaluating the bifurcation behaviour of a given dynamical system can be challenging. An even greater challenge, however, is to learn the bifurcation structure of dynamical systems from data, where the precise model structure is not known. Here, we study one aspects of this problem: the practical implications that the presence of bifurcations has on our ability to infer model parameters and initial conditions from empirical data; we focus on the canonical co-dimension 1 bifurcations and provide a comprehensive analysis of how dynamics, and our ability to infer kinetic parameters are linked. The picture thus emerging is surprisingly nuanced and suggests that identification of the qualitative dynamics-the bifurcation diagram-should precede any attempt at inferring kinetic parameters.
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