ArticlePLoS computational biology2013
Spartan: a comprehensive tool for understanding uncertainty in simulations of biological systems.
Article in PLoS computational biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 37 papers, 1 of them a synthesis that pooled it.
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
37 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Impact of interventions to reduce nosocomial transmission of SARS-CoV-2 in English NHS Trusts: a computational modelling study.BMC infectious diseases · 2024Article
- Histology-guided mathematical model of tumor oxygenation: sensitivity analysis of physical and computational parameters.bioRxiv : the preprint server for biology · 2024Article
- The effectiveness and efficiency of asymptomatic SARS-CoV-2 testing strategies for patient and healthcare workers within acute NHS hospitals during an omicron-like period.BMC infectious diseases · 2024Article
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- Mathematical modelling of activation-induced heterogeneity in TNF, IL6, NOS2, and IL1β expression reveals cell state transitions underpinning macrophage responses to LPS.Wellcome open research · 2022Article
- Spatial structure impacts adaptive therapy by shaping intra-tumoral competition.Communications medicine · 2022Article
- A study of error diversity in robotic swarms for task partitioning in foraging tasks.Frontiers in robotics and AI · 2022Article
- Targeting Cellular DNA Damage Responses in Cancer: An In Vitro-Calibrated Agent-Based Model Simulating Monolayer and Spheroid Treatment Responses to ATR-Inhibiting Drugs.Bulletin of mathematical biology · 2021Article
- The impact of testing and infection prevention and control strategies on within-hospital transmission dynamics of COVID-19 in English hospitals.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2021Article
- Simulating CXCR5 Dynamics in Complex Tissue Microenvironments.Frontiers in immunology · 2021Article
- Neutrophil Dynamics AffectFrontiers in immunology · 2021Article
- Systems biology predicts that fibrosis in tuberculous granulomas may arise through macrophage-to-myofibroblast transformation.PLoS computational biology · 2020Article
- B cell zone reticular cell microenvironments shape CXCL13 gradient formation.Nature communications · 2020Article
- LYMESIM 2.0: An Updated Simulation of Blacklegged Tick (Acari: Ixodidae) Population Dynamics and Enzootic Transmission of Borrelia burgdorferi (Spirochaetales: Spirochaetaceae).Journal of medical entomology · 2020Article
- IL-10 Impairs Local Immune Response in Lung Granulomas and Lymph Nodes during EarlyJournal of immunology (Baltimore, Md. : 1950) · 2020Article
- Global sensitivity analysis of biological multi-scale models.Current opinion in biomedical engineering · 2019Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
Abstract
Integrating computer simulation with conventional wet-lab research has proven to have much potential in furthering the understanding of biological systems. Success requires the relationship between simulation and the real-world system to be established: substantial aspects of the biological system are typically unknown, and the abstract nature of simulation can complicate interpretation of in silico results in terms of the biology. Here we present spartan (Simulation Parameter Analysis RToolkit ApplicatioN), a package of statistical techniques specifically designed to help researchers understand this relationship and provide novel biological insight. The tools comprising spartan help identify which simulation results can be attributed to the dynamics of the modelled biological system, rather than artefacts of biological uncertainty or parametrisation, or simulation stochasticity. Statistical analyses reveal the influence that pathways and components have on simulation behaviour, offering valuable biological insight into aspects of the system under study. We demonstrate the power of spartan in providing critical insight into aspects of lymphoid tissue development in the small intestine through simulation. Spartan is released under a GPLv2 license, implemented within the open source R statistical environment, and freely available from both the Comprehensive R Archive Network (CRAN) and http://www.cs.york.ac.uk/spartan. The techniques within the package can be applied to traditional ordinary or partial differential equation simulations as well as agent-based implementations. Manuals, comprehensive tutorials, and example simulation data upon which spartan can be applied are available from the website.
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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.