ArticleProceedings of the National Academy of Sciences of the United States of America2012
Parameter-free model discrimination criterion based on steady-state coplanarity.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Verifiable biology.Journal of the Royal Society, Interface · 2023Review
- Algebra, Geometry and Topology of ERK Kinetics.Bulletin of mathematical biology · 2022Article
- Joining and decomposing reaction networks.Journal of mathematical biology · 2020Article
- CrossPlan: systematic planning of genetic crosses to validate mathematical models.Bioinformatics (Oxford, England) · 2018Article
- Numerical algebraic geometry for model selection and its application to the life sciences.Journal of the Royal Society, Interface · 2016Article
- Long-term dynamics of multisite phosphorylation.Molecular biology of the cell · 2016Article
- An analytical approach to bistable biological circuit discrimination using real algebraic geometry.Journal of the Royal Society, Interface · 2015Article
- Parameter-free methods distinguish Wnt pathway models and guide design of experiments.Proceedings of the National Academy of Sciences of the United States of America · 2015Article
- Experimental design schemes for learning Boolean network models.Bioinformatics (Oxford, England) · 2014Article
- Simplifying biochemical models with intermediate species.Journal of the Royal Society, Interface · 2013Article
- Models of signalling networks - what cell biologists can gain from them and give to them.Journal of cell science · 2013Review
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Authors and funding
4 authors.
Funding
Abstract
We introduce a procedure for deciding when a mass-action model is incompatible with observed steady-state data that does not require any parameter estimation. Thus, we avoid the difficulties of nonlinear optimization typically associated with methods based on parameter fitting. Instead, we borrow ideas from algebraic geometry to construct a transformation of the model variables such that any set of steady states of the model under that transformation lies on a common plane, irrespective of the values of the model parameters. Model rejection can then be performed by assessing the degree to which the transformed data deviate from coplanarity. We demonstrate our method by applying it to models of multisite phosphorylation and cell death signaling. Our framework offers a parameter-free perspective on the statistical model selection problem, which can complement conventional statistical methods in certain classes of problems where inference has to be based on steady-state data and the model structures allow for suitable algebraic relationships among the steady-state solutions.
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Registered trials
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