ArticleCurrent opinion in biomedical engineering2019
Global sensitivity analysis of biological multi-scale models.
Article in Current opinion in biomedical engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
26 citing papers in PubMed.
- Article
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- A Mechanistic Digital Twin of uPAR-Driven Prostate Cancer Invasion Integrating ODE Signalling and Agent-Based Modelling.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Opportunities for machine learning and artificial intelligence in physiologically-based pharmacokinetic (PBPK) modeling.Advanced drug delivery reviews · 2025Review
- Rankings of tuberculosis antibiotic treatment regimens are sensitive to spatial scale, detection limit, and initial host bacterial burden.Journal of theoretical biology · 2025Article
- Precision design of dextran-permeated agarose hydrogels matching adipose stem cell adhesion timescales.Materials today. Bio · 2025Article
- Challenges and opportunities in uncertainty quantification for healthcare and biological systems.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Review
- Article
- Optimizing Solid Tumor Treatment with Antibody-drug Conjugates Using Agent-Based Modeling: Considering the Role of a Carrier Dose and Payload Class.Pharmaceutical research · 2024Article
- A review of mechanistic learning in mathematical oncology.Frontiers in immunology · 2024Review
- Endothelial cells signaling and patterning under hypoxia: a mechanistic integrative computational model including the Notch-Dll4 pathway.Frontiers in physiology · 2024Article
- In silico agent-based modeling approach to characterize multiple in vitro tuberculosis infection models.PloS one · 2024Article
- Use of Individual-Based Mathematical Modelling to Understand More About Antibiotic Resistance Within-Host.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Computational modeling of AMPK and mTOR crosstalk in glutamatergic synapse calcium signaling.NPJ systems biology and applications · 2023Article
- Conservation of forest biomass and forest-dependent wildlife population: Uncertainty quantification of the model parameters.Heliyon · 2023Article
- Endothelial cells signaling and patterning under hypoxia: a mechanistic integrative computational model including the Notch-Dll4 pathway.bioRxiv : the preprint server for biology · 2023Article
- Climate drives global functional trait variation in lizards.Nature ecology & evolution · 2023Article
- Integration of transcriptomics data into agent-based models of solid tumor metastasis.Computational and structural biotechnology journal · 2023Article
- A virtual host model of Mycobacterium tuberculosis infection identifies early immune events as predictive of infection outcomes.Journal of theoretical biology · 2022Article
- Multiscale modeling in disease.Current opinion in systems biology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Mathematical models of biological systems need to both reflect and manage the inherent complexities of biological phenomena. Through their versatility and ability to capture behavior at multiple scales, multi-scale models offer a valuable approach. Due to the typically nonlinear and stochastic nature of multi-scale models as well as unknown parameter values, various types of uncertainty are present; thus, effective assessment and quantification of such uncertainty through sensitivity analysis is important. In this review, we discuss global sensitivity analysis in the context of multi-scale and multi-compartment models and highlight its value in model development and analysis. We present an overview of sensitivity analysis methods, approaches for extending such methods to a multi-scale setting, and examples of how sensitivity analysis can inform model reduction. Through schematics and references to past work, we aim to emphasize the advantages and usefulness of such techniques.
Identifiers
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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.