ArticleMathematical biosciences2021
To Sobol or not to Sobol? The effects of sampling schemes in systems biology applications.
Article in Mathematical biosciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
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- Sensitivity analysis of factors influencing the ecology of mosquitoes involved in the transmission of Rift Valley fever virus.PLoS neglected tropical diseases · 2026Article
- Calibration, Sensitivity and Uncertainty Analysis of Complex Ecological Models-A Review.Ecology letters · 2026Review
- Signed, sealed, delivered: a generalizable model for living biotherapeutic dosing and metabolism.NPJ systems biology and applications · 2026Article
- Infectious disease modeling for public health practice: projections, scenarios, and uncertainty in three phases of outbreak response.American journal of epidemiology · 2026Article
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- Aggregate data modelling: A fast implementation for fitting pharmacometrics models to summary-level data in R.Journal of pharmacokinetics and pharmacodynamics · 2025Article
- Astrocyte Reprogramming Drives Tumor Progression and Chemotherapy Resistance in Agent-Based Models of Breast Cancer Brain Metastases.Bulletin of mathematical biology · 2025Article
- Opportunities for machine learning and artificial intelligence in physiologically-based pharmacokinetic (PBPK) modeling.Advanced drug delivery reviews · 2025Review
- A hybrid analytical and data driven framework for optimizing radially grooved wet clutch geometry.Scientific reports · 2025Article
- Assessing parameter identifiability of a hemodynamics PDE model using spectral surrogates and dimension reduction.PLoS computational biology · 2025Article
- A mathematical model of metacarpal subchondral bone adaptation, microdamage and repair in racehorses.Journal of the Royal Society, Interface · 2025Article
- An efficient and flexible framework for inferring global sensitivity of agent-based model parameters.PLoS computational biology · 2025Article
- Rankings of tuberculosis antibiotic treatment regimens are sensitive to spatial scale, detection limit, and initial host bacterial burden.Journal of theoretical biology · 2025Article
- A mechanistic computational model of HGF-VEGF-mediated endothelial cell proliferation and vascular permeability.iScience · 2025Article
- Physics-informed neural networks with hybrid Kolmogorov-Arnold network and augmented Lagrangian function for solving partial differential equations.Scientific reports · 2025Article
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4 authors.
Funding
Abstract
Computational and mathematical models in biology rely heavily on the parameters that characterize them. However, robust estimates for their values are typically elusive and thus a large parameter space becomes necessary for model study, particularly to make translationally impactful predictions. Sampling schemes exploring parameter spaces for models are used for a variety of purposes in systems biology, including model calibration and sensitivity analysis. Typically, random sampling is used; however, when models have a high number of unknown parameters or the models are highly complex, computational cost becomes an important factor. This issue can be reduced through the use of efficient sampling schemes such as Latin hypercube sampling (LHS) and Sobol sequences. In this work, we compare and contrast three sampling schemes - random sampling, LHS, and Sobol sequences - for the purposes of performing both parameter sensitivity analysis and model calibration. In addition, we apply these analyses to different types of computational and mathematical models of varying complexity: a simple ODE model, a complex ODE model, and an agent-based model. In general, the sampling scheme had little effect when used for calibration efforts, but when applied to sensitivity analyses, Sobol sequences exhibited faster convergence. While the observed benefit to convergence is relatively small, Sobol sequences are computationally less expensive to compute than LHS samples and also have the benefit of being deterministic, which allows for better reproducibility of results.
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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.