ArticlePLoS computational biology2014
Model selection in systems biology depends on experimental design.
Article in PLoS computational biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- The impact of experimental designs & system sloppiness on the personalisation process: A cardiovascular perspective.PloS one · 2025Article
- Review
- A Continuation Technique for Maximum Likelihood Estimators in Biological Models.Bulletin of mathematical biology · 2023Article
- Studying stochastic systems biology of the cell with single-cell genomics data.bioRxiv : the preprint server for biology · 2023Article
- Applications of Boolean modeling to study the dynamics of a complex disease and therapeutics responses.Frontiers in bioinformatics · 2023Article
- Interpretable and tractable models of transcriptional noise for the rational design of single-molecule quantification experiments.Nature communications · 2022Article
- Boolean modelling as a logic-based dynamic approach in systems medicine.Computational and structural biotechnology journal · 2022Review
- Cancer Explant Models.Current topics in microbiology and immunology · 2021Article
- Optimal experimental design for mathematical models of haematopoiesis.Journal of the Royal Society, Interface · 2021Article
- A matter of time: Using dynamics and theory to uncover mechanisms of transcriptional bursting.Current opinion in cell biology · 2020Review
- Multi-model and network inference based on ensemble estimates: avoiding the madness of crowds.Journal of the Royal Society, Interface · 2020Article
- Symmetry structures in dynamic models of biochemical systems.Journal of the Royal Society, Interface · 2020Article
- Computational models of melanoma.Theoretical biology & medical modelling · 2020Review
- Medusa: Software to build and analyze ensembles of genome-scale metabolic network reconstructions.PLoS computational biology · 2020Article
- Computational optimization of associative learning experiments.PLoS computational biology · 2020Article
- Parameter inference in dynamical systems with co-dimension 1 bifurcations.Royal Society open science · 2019Article
- Optimal Quantification of Contact Inhibition in Cell Populations.Biophysical journal · 2017Article
- How to deal with parameters for whole-cell modelling.Journal of the Royal Society, Interface · 2017Review
- Control mechanisms for stochastic biochemical systems via computation of reachable sets.Royal Society open science · 2017Article
- Predicting network modules of cell cycle regulators using relative protein abundance statistics.BMC systems biology · 2017Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Experimental design attempts to maximise the information available for modelling tasks. An optimal experiment allows the inferred models or parameters to be chosen with the highest expected degree of confidence. If the true system is faithfully reproduced by one of the models, the merit of this approach is clear - we simply wish to identify it and the true parameters with the most certainty. However, in the more realistic situation where all models are incorrect or incomplete, the interpretation of model selection outcomes and the role of experimental design needs to be examined more carefully. Using a novel experimental design and model selection framework for stochastic state-space models, we perform high-throughput in-silico analyses on families of gene regulatory cascade models, to show that the selected model can depend on the experiment performed. We observe that experimental design thus makes confidence a criterion for model choice, but that this does not necessarily correlate with a model's predictive power or correctness. Finally, in the special case of linear ordinary differential equation (ODE) models, we explore how wrong a model has to be before it influences the conclusions of a model selection analysis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.