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Literature DB >> 366054

The effects of cell density and metabolite flux on cellular dynamics.

Abstract

Density-dependent regulation of cell growth in tissue culture is a well-known phenomenon but the mechanism of regulation remains obscure. Here we explore the effects of cell density and metabolite flux on the collective dynamics of a cell population. The intracellular dynamics are modelled by positive feedback kinetic mechanisms of the kind known to apply to yeast cells. Several experimental observations related to glycolytic oscillations are predicted and it is suggested that the general conclusions may be applicable in a broader context.

Entities: Species

Mesh：

Year: 1978 PMID： 366054 DOI： 10.1007/bf00275897

Source DB: PubMed Journal: J Math Biol ISSN： 0303-6812 Impact factor: 2.259

20 in total

The effects of cell density and metabolite flux on cellular dynamics.

Review 1. Control of growth of mammalian cells in cell culture.

2. The growth in vitro of single isolated tissue cells.

3. Low-resistance junctions between cells in embryos and tissue culture.

4. Diffusion-mediated control of myoblast fusion.

5. Non-linear aspects of dynamic pattern in cellular networks.

6. Instability and dynamic pattern in cellular networks.

Review 7. Influence of geometry on control of cell growth.

8. Electrophysiological control of reversed ciliary beating in Paramecium.

9. Stimulation of glucose transport in cultures of density-inhibited chick embryo cells.

10. Pattern formation in the blue-green alga, Anabaena. I. Basic mechanisms.

1. Dynamical quorum sensing: Population density encoded in cellular dynamics.

2. The Intersection of Theory and Application in Elucidating Pattern Formation in Developmental Biology.

3. Regulation of differentiation in a population of cells interacting through a common pool.

4. A continuum model for coupled cells.

5. Stochastic analysis of reaction-diffusion processes.

6. A multi-time-scale analysis of chemical reaction networks: I. Deterministic systems.

7. A multi-time-scale analysis of chemical reaction networks: II. Stochastic systems.