ArticleBiophysical journal2017
Optimal Quantification of Contact Inhibition in Cell Populations.
Article in Biophysical journal, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- DYNAMEX: A Time-Resolved Model for Predicting Distribution of Chemicals in In Vitro Cell Assays.Environmental science & technology · 2026Article
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- Quantifying biological heterogeneity in nano-engineered particle-cell interaction experiments.Journal of the Royal Society, Interface · 2025Article
- In silico modeling of directed differentiation of induced pluripotent stem cells to definitive endoderm.PLoS computational biology · 2025Article
- Quantifying cell cycle regulation by tissue crowding.Biophysical journal · 2025Article
- Implementing measurement error models with mechanistic mathematical models in a likelihood-based framework for estimation, identifiability analysis and prediction in the life sciences.Journal of the Royal Society, Interface · 2024Review
- Computationally efficient framework for diagnosing, understanding and predicting biphasic population growth.Journal of the Royal Society, Interface · 2022Article
- Parameter estimation and uncertainty quantification using information geometry.Journal of the Royal Society, Interface · 2022Review
- Autocrine signaling can explain the emergence of Allee effects in cancer cell populations.PLoS computational biology · 2022Article
- 3D bioprinting of a trachea-mimetic cellular construct of a clinically relevant size.Biomaterials · 2021Article
- Quantifying the impact of electric fields on single-cell motility.Biophysical journal · 2021Article
- Identifiability analysis for stochastic differential equation models in systems biology.Journal of the Royal Society, Interface · 2020Article
- Unpacking the Allee effect: determining individual-level mechanisms that drive global population dynamics.Proceedings. Mathematical, physical, and engineering sciences · 2020Article
- Identifying density-dependent interactions in collective cell behaviour.Journal of the Royal Society, Interface · 2020Article
- Simulation and inference algorithms for stochastic biochemical reaction networks: from basic concepts to state-of-the-art.Journal of the Royal Society, Interface · 2019Article
- Three-dimensional experiments and individual based simulations show that cell proliferation drives melanoma nest formation in human skin tissue.BMC systems biology · 2018Article
- Mathematical Models for Cell Migration with Real-Time Cell Cycle Dynamics.Biophysical journal · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Contact inhibition refers to a reduction in the rate of cell migration and/or cell proliferation in regions of high cell density. Under normal conditions, contact inhibition is associated with the proper functioning tissues, whereas abnormal regulation of contact inhibition is associated with pathological conditions, such as tumor spreading. Unfortunately, standard mathematical modeling practices mask the importance of parameters that control contact inhibition through scaling arguments. Furthermore, standard experimental protocols are insufficient to quantify the effects of contact inhibition because they focus on data describing early time, low-density dynamics only. Here we use the logistic growth equation as a caricature model of contact inhibition to make recommendations as to how to best mitigate these issues. Taking a Bayesian approach, we quantify the trade off between different features of experimental design and estimates of parameter uncertainty so that we can reformulate a standard cell proliferation assay to provide estimates of both the low-density intrinsic growth rate, λ, and the carrying capacity density, K, which is a measure of contact inhibition.
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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.