bioRxivCell Biology
The Correlation Between Cell and Nucleus Size is Explained by an Eukaryotic Cell Growth Model
Abstract
In eukaryotes, the cell volume is observed to be strongly correlated with the nuclear volume. The slope of this correlation depends on the cell type, growth condition, and the physical environment of the cell. We develop a computational model of cell growth and proteome increase, incorporating the kinetics of amino acid import, protein/ribosome synthesis and degradation, and active transport of proteins between the cytoplasm and the nucleoplasm. We also include a simple model of ribosome biogenesis and assembly. Results show that the cell volume is tightly correlated with the nuclear volume, and the cytoplasm-nucleoplasm transport rates strongly influences the cell growth rate as well as the cytoplasm/nucleoplasm ratio. Ribosome assembly and the ratio of ribosomal proteins to mature ribosomes also influence the cell volume and the cell growth rate. We find that in order to regulate the cell growth rate and the cytoplasm/nucleoplasm ratio, the cell must optimally control groups of kinetic parameters together, which could explain the quantitative roles of canonical growth pathways. Finally, using an extension of our model and single cell RNAseq data, it is possible to construct a detailed proteome distribution, provided that a quantitative cell division mechanism is known. Author summaryWe develop computational model of cell proteome increase and cell growth to compute the cell volume to nuclear volume ratio. The model incorporates essential kinetics of protein and ribosome synthesis/degradation, and their transport across the nuclear envelope. The model also incorporates ribosome biogenesis and assembly. The model identifies the most important parameters in determining the cell growth rate and the nucleoplasm/cytoplasm ratio, and provides a computational starting point to construct the cell proteome based on the RNAseq data.
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: clustering
- approach: statistical model
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