ArticleBMC bioinformatics2011
Techniques for analysing pattern formation in populations of stem cells and their progeny.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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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.
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Who cites it
6 citing papers in PubMed.
- Exploring the relationship between vascular remodelling and tumour growth using agent-based modelling.PLoS computational biology · 2026Article
- ALOA, a pipeline for preliminary analysis of spatial profiling imaging data.Computational and structural biotechnology journal · 2024Article
- Quantification of spatial and phenotypic heterogeneity in an agent-based model of tumour-macrophage interactions.PLoS computational biology · 2023Article
- Combining multiple spatial statistics enhances the description of immune cell localisation within tumours.Scientific reports · 2020Article
- Identifying the necrotic zone boundary in tumour spheroids with pair-correlation functions.Journal of the Royal Society, Interface · 2016Article
- Reconciling diverse mammalian pigmentation patterns with a fundamental mathematical model.Nature communications · 2016Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundTo investigate how patterns of cell differentiation are related to underlying intra- and inter-cellular signalling pathways, we use a stochastic individual-based model to simulate pattern formation when stem cells and their progeny are cultured as a monolayer. We assume that the fate of an individual cell is regulated by the signals it receives from neighbouring cells via either diffusive or juxtacrine signalling. We analyse simulated patterns using two different spatial statistical measures that are suited to planar multicellular systems: pair correlation functions (PCFs) and quadrat histograms (QHs).
resultsWith a diffusive signalling mechanism, pattern size (revealed by PCFs) is determined by both morphogen decay rate and a sensitivity parameter that determines the degree to which morphogen biases differentiation; high sensitivity and slow decay give rise to large-scale patterns. In contrast, with juxtacrine signalling, high sensitivity produces well-defined patterns over shorter lengthscales. QHs are simpler to compute than PCFs and allow us to distinguish between random differentiation at low sensitivities and patterned states generated at higher sensitivities.
conclusionsPCFs and QHs together provide an effective means of characterising emergent patterns of differentiation in planar multicellular aggregates.
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Registered trials
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