4.7 Article

From a discrete to a continuum model of cell dynamics in one dimension

期刊

PHYSICAL REVIEW E
卷 80, 期 3, 页码 -

出版社

AMER PHYSICAL SOC
DOI: 10.1103/PhysRevE.80.031912

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资金

  1. Engineering and Physical Sciences Research Council [GR/S72023/01]
  2. Oxford University Life Sciences Interface Doctoral Training Centre (LSI DTC)
  3. NIH [U56CA113004]
  4. National Cancer Institute and the British Council [PMI2]
  5. Royal Society-Wolfson Foundation
  6. BBSRC [BB/D020190/1] Funding Source: UKRI
  7. Biotechnology and Biological Sciences Research Council [BB/D020190/1] Funding Source: researchfish
  8. Engineering and Physical Sciences Research Council [EP/E500536/1, GR/S72023/01] Funding Source: researchfish

向作者/读者索取更多资源

Multiscale modeling is emerging as one of the key challenges in mathematical biology. However, the recent rapid increase in the number of modeling methodologies being used to describe cell populations has raised a number of interesting questions. For example, at the cellular scale, how can the appropriate discrete cell-level model be identified in a given context? Additionally, how can the many phenomenological assumptions used in the derivation of models at the continuum scale be related to individual cell behavior? In order to begin to address such questions, we consider a discrete one-dimensional cell-based model in which cells are assumed to interact via linear springs. From the discrete equations of motion, the continuous Rouse [P. E. Rouse, J. Chem. Phys. 21, 1272 (1953)] model is obtained. This formalism readily allows the definition of a cell number density for which a nonlinear fast diffusion equation is derived. Excellent agreement is demonstrated between the continuum and discrete models. Subsequently, via the incorporation of cell division, we demonstrate that the derived nonlinear diffusion model is robust to the inclusion of more realistic biological detail. In the limit of stiff springs, where cells can be considered to be incompressible, we show that cell velocity can be directly related to cell production. This assumption is frequently made in the literature but our derivation places limits on its validity. Finally, the model is compared with a model of a similar form recently derived for a different discrete cell-based model and it is shown how the different diffusion coefficients can be understood in terms of the underlying assumptions about cell behavior in the respective discrete models.

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