ArticleBMC biophysics2015
The biophysical nature of cells: potential cell behaviours revealed by analytical and computational studies of cell surface mechanics.
Article in BMC biophysics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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31 citing papers in PubMed, 88 citations in OpenAlex.
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- The lymphatic system favours survival of a unique T. brucei population.Biology open · 2023Article
- Multiscale Modeling of Spheroid Tumors: Effect of Nutrient Availability on Tumor Evolution.The journal of physical chemistry. B · 2023Article
- Biophysical models of early mammalian embryogenesis.Stem cell reports · 2023Review
- A multiscale computational model of YAP signaling in epithelial fingering behavior.Biophysical journal · 2022Article
- Seven challenges in the multiscale modeling of multicellular tissues.WIREs mechanisms of disease · 2022Review
- Large-scale simulations of biological cell sorting driven by differential adhesion follow diffusion-limited domain coalescence regime.PLoS computational biology · 2021Article
- Mechanical properties of cell sheets and spheroids: the link between single cells and complex tissues.Biophysical reviews · 2021Review
- Artistoo, a library to build, share, and explore simulations of cells and tissues in the web browser.eLife · 2021Article
- Spots, stripes, and spiral waves in models for static and motile cells : GTPase patterns in cells.Journal of mathematical biology · 2021Article
- Bridging from single to collective cell migration: A review of models and links to experiments.PLoS computational biology · 2020Review
- Article
- Mechanisms of endothelial cell coverage by pericytes: computational modelling of cell wrapping andJournal of the Royal Society, Interface · 2020Article
- Neuronal differentiation influences progenitor arrangement in the vertebrate neuroepithelium.Development (Cambridge, England) · 2019Article
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundThe biophysical characteristics of cells determine their shape in isolation and when packed within tissues. Cells can form regular or irregular epithelial structures, round up and form clusters, or deform and attach to substrates. The acquired shape of cells and tissues is a consequence of (i) internal cytoskeletal processes, such as actin polymerisation and cortical myosin contraction, (ii) adhesion molecules within the cell membrane that interact with substrates and neighbouring cells, and (iii) processes that regulate cell volume. Although these processes seem relatively simple, when combined they unleash a rich variety of cellular behaviour that is not readily understandable outside a theoretical framework.
methodsWe perform a mathematical analysis of a commonly used class of model formalisms that describe cell surface mechanics using an energy-based approach. Predictions are then confirmed through comparison with the computational outcomes of a Vertex model and 2D and 3D simulations of the Cellular Potts model.
resultsThe analytical study reveals the complete possible spectrum of single cell behaviour and tissue packing in both 2D and 3D, by taking the typical core elements of cell surface mechanics into account: adhesion, cortical tension and volume conservation. We show that from an energy-based description, forces and tensions can be derived, as well as the prediction of cell behaviour and tissue packing, providing an intuitive and biologically relevant mapping between modelling parameters and experiments.
conclusionsThe quantitative cellular behaviours and biological insights agree between the analytical study and the diverse computational model formalisms, including the Cellular Potts model. This illustrates the generality of energy-based approaches for cell surface mechanics and highlights how meaningful and quantitative comparisons between models can be established. Moreover, the mathematical analysis reveals direct links between known biophysical properties and specific parameter settings within the Cellular Potts model.
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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.