ArticleFrontiers in microbiology2016
Strong Inference in Mathematical Modeling: A Method for Robust Science in the Twenty-First Century.
Article in Frontiers in microbiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Mapping the Landscape of Magnetic Field Effects on Neural Regeneration and Repair: A Combined Systematic Review, Mathematical Model, and Meta-Analysis.Journal of tissue engineering and regenerative medicine · 2023Pooled it
- BCG vaccination reduces the rate of Mycobacterium tuberculosis dissemination between murine lungs.Research square · 2026Article
- BCG vaccination reduces the rate of Mycobacterium tuberculosis dissemination between murine lungs.bioRxiv : the preprint server for biology · 2026Article
- Informing policy via dynamic models: Cholera in Haiti.PLoS computational biology · 2024Article
- Opening Pandora's box: caveats with using toolbox-based approaches in mathematical modeling in biology.Frontiers in applied mathematics and statistics · 2024Article
- A brief overview of mathematical modeling of the within-host dynamics of Mycobacterium tuberculosis.Frontiers in applied mathematics and statistics · 2024Article
- Article
- Mathematical Modeling to Guide Experimental Design: T Cell Clustering as a Case Study.Bulletin of mathematical biology · 2022Article
- Application of Mathematical Modeling and Computational Tools in the Modern Drug Design and Development Process.Molecules (Basel, Switzerland) · 2022Review
- Article
- Photosynthesis dynamics and regulation sensed in the frequency domain.Plant physiology · 2021Article
- Fusion and fission events regulate endosome maturation and viral escape.Scientific reports · 2021Article
- Impact of Oseltamivir Treatment on Influenza A and B Virus Dynamics in Human Volunteers.Frontiers in microbiology · 2021Article
- Modelling the dynamics of tuberculosis lesions in a virtual lung: Role of the bronchial tree in endogenous reinfection.PLoS computational biology · 2020Article
- Model Averaging in Viral Dynamic Models.The AAPS journal · 2020Article
- The Rate of CD4 T Cell Entry into the Lungs during Mycobacterium tuberculosis Infection Is Determined by Partial and Opposing Effects of Multiple Chemokine Receptors.Infection and immunity · 2019Article
- Clustering of Activated CD8 T Cells Around Malaria-Infected Hepatocytes Is Rapid and Is Driven by Antigen-Specific Cells.Frontiers in immunology · 2019Article
- Combining Mathematical Models With Experimentation to Drive Novel Mechanistic Insights Into Macrophage Function.Frontiers in immunology · 2019Article
- Validated Models of Immune Response to Virus Infection.Current opinion in systems biology · 2018Article
- Noise Is Not Error: Detecting Parametric Heterogeneity Between Epidemiologic Time Series.Frontiers in microbiology · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While there are many opinions on what mathematical modeling in biology is, in essence, modeling is a mathematical tool, like a microscope, which allows consequences to logically follow from a set of assumptions. Only when this tool is applied appropriately, as microscope is used to look at small items, it may allow to understand importance of specific mechanisms/assumptions in biological processes. Mathematical modeling can be less useful or even misleading if used inappropriately, for example, when a microscope is used to study stars. According to some philosophers (Oreskes et al., 1994), the best use of mathematical models is not when a model is used to confirm a hypothesis but rather when a model shows inconsistency of the model (defined by a specific set of assumptions) and data. Following the principle of strong inference for experimental sciences proposed by Platt (1964), I suggest "strong inference in mathematical modeling" as an effective and robust way of using mathematical modeling to understand mechanisms driving dynamics of biological systems. The major steps of strong inference in mathematical modeling are (1) to develop multiple alternative models for the phenomenon in question; (2) to compare the models with available experimental data and to determine which of the models are not consistent with the data; (3) to determine reasons why rejected models failed to explain the data, and (4) to suggest experiments which would allow to discriminate between remaining alternative models. The use of strong inference is likely to provide better robustness of predictions of mathematical models and it should be strongly encouraged in mathematical modeling-based publications in the Twenty-First century.
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.