ArticlePloS one2019
A topological approach to selecting models of biological experiments.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Topologically-based parameter inference for agent-based model selection from spatiotemporal cellular data.PLoS computational biology · 2026Article
- Topological model selection: a case-study in tumour-induced angiogenesis.Bioinformatics (Oxford, England) · 2026Article
- Spatial and Sequential Topological Analysis of Molecular Dynamics Simulations of IgG1 Fc Domains.Journal of chemical theory and computation · 2025Article
- Quantifying collective motion patterns in mesenchymal cell populations using topological data analysis and agent-based modeling.Mathematical biosciences · 2024Article
- Topological data analysis of spatial patterning in heterogeneous cell populations: clustering and sorting with varying cell-cell adhesion.NPJ systems biology and applications · 2023Article
- Temporal Mapper: Transition networks in simulated and real neural dynamics.Network neuroscience (Cambridge, Mass.) · 2023Article
- Risk prediction of clinical adverse outcomes with machine learning in a cohort of critically ill patients with atrial fibrillation.Scientific reports · 2021Article
- Topological data analysis of collective and individual epithelial cells using persistent homology of loops.Soft matter · 2021Article
- Topological Data Analysis Approaches to Uncovering the Timing of Ring Structure Onset in Filamentous Networks.Bulletin of mathematical biology · 2021Article
- Noise robustness of persistent homology on greyscale images, across filtrations and signatures.PloS one · 2021Article
- Topology Applied to Machine Learning: From Global to Local.Frontiers in artificial intelligence · 2021Review
- Minimal Cycle Representatives in Persistent Homology Using Linear Programming: An Empirical Study With User's Guide.Frontiers in artificial intelligence · 2021Article
- Bridging from single to collective cell migration: A review of models and links to experiments.PLoS computational biology · 2020Review
- Learning Equations from Biological Data with Limited Time Samples.Bulletin of mathematical biology · 2020Article
- Analyzing collective motion with machine learning and topology.Chaos (Woodbury, N.Y.) · 2019Article
- Global sensitivity analysis of biological multi-scale models.Current opinion in biomedical engineering · 2019Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We use topological data analysis as a tool to analyze the fit of mathematical models to experimental data. This study is built on data obtained from motion tracking groups of aphids in [Nilsen et al., PLOS One, 2013] and two random walk models that were proposed to describe the data. One model incorporates social interactions between the insects via a functional dependence on an aphid's distance to its nearest neighbor. The second model is a control model that ignores this dependence. We compare data from each model to data from experiment by performing statistical tests based on three different sets of measures. First, we use time series of order parameters commonly used in collective motion studies. These order parameters measure the overall polarization and angular momentum of the group, and do not rely on a priori knowledge of the models that produced the data. Second, we use order parameter time series that do rely on a priori knowledge, namely average distance to nearest neighbor and percentage of aphids moving. Third, we use computational persistent homology to calculate topological signatures of the data. Analysis of the a priori order parameters indicates that the interactive model better describes the experimental data than the control model does. The topological approach performs as well as these a priori order parameters and better than the other order parameters, suggesting the utility of the topological approach in the absence of specific knowledge of mechanisms underlying the data.
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