ArticleRoyal Society open science2018
Scalable population-level modelling of biological cells incorporating mechanics and kinetics in continuous time.
Article in Royal Society open science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Virtual twins and the future of human developmental biology.NPJ systems biology and applications · 2026Review
- Morphological Stability for in silico Models of Avascular Tumors.Bulletin of mathematical biology · 2024Article
- Suppression of beta oscillations by delayed feedback in a cortex-basal ganglia-thalamus-pedunculopontine nucleus neural loop model.Journal of biological physics · 2023Article
- Data-driven spatio-temporal modelling of glioblastoma.Royal Society open science · 2023Review
- Derivation of continuum models from discrete models of mechanical forces in cell populations.Journal of mathematical biology · 2021Article
- Modeling binary and graded cone cell fate patterning in the mouse retina.PLoS computational biology · 2020Article
- Bridging the gap between individual-based and continuum models of growing cell populations.Journal of mathematical biology · 2020Article
- From energy to cellular forces in the Cellular Potts Model: An algorithmic approach.PLoS computational biology · 2019Article
- Stochastic Simulation of Pattern Formation in Growing Tissue: A Multilevel Approach.Bulletin of mathematical biology · 2019Article
- Computational Structural Biology: Successes, Future Directions, and Challenges.Molecules (Basel, Switzerland) · 2019Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The processes taking place inside the living cell are now understood to the point where predictive computational models can be used to gain detailed understanding of important biological phenomena. A key challenge is to extrapolate this detailed knowledge of the individual cell to be able to explain at the population level how cells interact and respond with each other and their environment. In particular, the goal is to understand how organisms develop, maintain and repair functional tissues and organs. In this paper, we propose a novel computational framework for modelling populations of interacting cells. Our framework incorporates mechanistic, constitutive descriptions of biomechanical properties of the cell population, and uses a coarse-graining approach to derive individual rate laws that enable propagation of the population through time. Thanks to its multiscale nature, the resulting simulation algorithm is extremely scalable and highly efficient. As highlighted in our computational examples, the framework is also very flexible and may straightforwardly be coupled with continuous-time descriptions of biochemical signalling within, and between, individual cells.
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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.