ArticlePeerJ2014
Prediction uncertainty assessment of a systems biology model requires a sample of the full probability distribution of its parameters.
Article in PeerJ, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed.
- Combining automated kinetic model selection with robust optimization and application to maximize cell-free production of UDP-GalNAc.Metabolic engineering communications · 2026Article
- A Reaction-Diffusion Model for Capturing Mass Loss and Microstructure Evolution in Enzymatic Degradation of Poly(Polymers · 2026Article
- Spectral expansion methods for prediction uncertainty quantification in systems biology.Frontiers in systems biology · 2024Article
- Predictive power of non-identifiable models.Scientific reports · 2023Article
- A Mathematical Model of Estradiol Production from Ultrasound Data for Bovine Ovarian Follicles.Cells · 2022Article
- Pre-existing Cell States Control Heterogeneity of Both EGFR and CXCR4 Signaling.Cellular and molecular bioengineering · 2021Article
- Virulence factor activity relationships (VFARs): a bioinformatics perspective.Environmental science. Processes & impacts · 2017Review
- Effect of Nickel and Cobalt on Methanogenic Enrichment Cultures and Role of Biogenic Sulfide in Metal Toxicity Attenuation.Frontiers in microbiology · 2017Article
- MRI characterization of temporal lobe epilepsy using rapidly measurable spatial indices with hemisphere asymmetries and gender features.Neuroradiology · 2015Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multi-parameter models in systems biology are typically 'sloppy': some parameters or combinations of parameters may be hard to estimate from data, whereas others are not. One might expect that parameter uncertainty automatically leads to uncertain predictions, but this is not the case. We illustrate this by showing that the prediction uncertainty of each of six sloppy models varies enormously among different predictions. Statistical approximations of parameter uncertainty may lead to dramatic errors in prediction uncertainty estimation. We argue that prediction uncertainty assessment must therefore be performed on a per-prediction basis using a full computational uncertainty analysis. In practice this is feasible by providing a model with a sample or ensemble representing the distribution of its parameters. Within a Bayesian framework, such a sample may be generated by a Markov Chain Monte Carlo (MCMC) algorithm that infers the parameter distribution based on experimental data. Matlab code for generating the sample (with the Differential Evolution Markov Chain sampler) and the subsequent uncertainty analysis using such a sample, is supplied as Supplemental Information.
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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.