ArticleBMC systems biology2018
Bayesian uncertainty analysis for complex systems biology models: emulation, global parameter searches and evaluation of gene functions.
Article in BMC systems biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 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
- Dose as a fundamental organizing principle in physiology: Implications for mechanism, disease, and precision medicine.Journal of precision medicine (Amsterdam, Netherlands) · 2026Article
- A model for the human fetal ventricular myocyte electrophysiology.PLoS computational biology · 2026Article
- Systematic structural discrepancy assessment for computer models.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Emulating computer models with high-dimensional count output.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- The Constrained Disorder Principle Overcomes the Challenges of Methods for Assessing Uncertainty in Biological Systems.Journal of personalized medicine · 2024Review
- Reverse engineering morphogenesis through Bayesian optimization of physics-based models.NPJ systems biology and applications · 2024Article
- Incorporating temporal information during feature engineering bolsters emulation of spatio-temporal emergence.Bioinformatics (Oxford, England) · 2024Article
- Bayesian inference for fitting cardiac models to experiments: estimating parameter distributions using Hamiltonian Monte Carlo and approximate Bayesian computation.Medical & biological engineering & computing · 2023Article
- The importance of mechanical conditions in the testing of excitation abnormalities in a population of electro-mechanical models of human ventricular cardiomyocytes.Frontiers in physiology · 2023Article
- Bayesian emulation and history matching of JUNE.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
- Optimization and Scale-Up of Fermentation Processes Driven by Models.Bioengineering (Basel, Switzerland) · 2022Review
- Complex model calibration through emulation, a worked example for a stochastic epidemic model.Epidemics · 2022Article
- Ab initio predictions link the neutron skin ofNature physics · 2022Article
- June: open-source individual-based epidemiology simulation.Royal Society open science · 2021Article
- The importance of uncertainty quantification in model reproducibility.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2021Article
- Focus on biosensors: Looking through the lens of quantitative biology.Quantitative plant biology · 2021Review
- Predicting left ventricular contractile function via Gaussian process emulation in aortic-banded rats.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2020Article
- Bayesian correlation is a robust gene similarity measure for single-cell RNA-seq data.NAR genomics and bioinformatics · 2020Article
- Computer-Aided Whole-Cell Design: Taking a Holistic Approach by Integrating Synthetic With Systems Biology.Frontiers in bioengineering and biotechnology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundMany mathematical models have now been employed across every area of systems biology. These models increasingly involve large numbers of unknown parameters, have complex structure which can result in substantial evaluation time relative to the needs of the analysis, and need to be compared to observed data of various forms. The correct analysis of such models usually requires a global parameter search, over a high dimensional parameter space, that incorporates and respects the most important sources of uncertainty. This can be an extremely difficult task, but it is essential for any meaningful inference or prediction to be made about any biological system. It hence represents a fundamental challenge for the whole of systems biology.
methodsBayesian statistical methodology for the uncertainty analysis of complex models is introduced, which is designed to address the high dimensional global parameter search problem. Bayesian emulators that mimic the systems biology model but which are extremely fast to evaluate are embeded within an iterative history match: an efficient method to search high dimensional spaces within a more formal statistical setting, while incorporating major sources of uncertainty.
resultsThe approach is demonstrated via application to a model of hormonal crosstalk in Arabidopsis root development, which has 32 rate parameters, for which we identify the sets of rate parameter values that lead to acceptable matches between model output and observed trend data. The multiple insights into the model's structure that this analysis provides are discussed. The methodology is applied to a second related model, and the biological consequences of the resulting comparison, including the evaluation of gene functions, are described.
conclusionsBayesian uncertainty analysis for complex models using both emulators and history matching is shown to be a powerful technique that can greatly aid the study of a large class of systems biology models. It both provides insight into model behaviour and identifies the sets of rate parameters of interest.
Indexed as
Identifiers
What OpenQuestion holds
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.