Stochastic exit from mitosis in budding yeast Model predictions and experimental observations

被引:17
作者
Ball, David A. [2 ]
Ahn, Tae-Hyuk [3 ]
Wang, Pengyuan [3 ]
Chen, Katherine C. [4 ]
Cao, Yang [3 ]
Tyson, John J. [4 ]
Peccoud, Jean [2 ]
Baumann, William T. [1 ]
机构
[1] Virginia Polytech Inst & State Univ, Dept Elect & Comp Engn, Blacksburg, VA 24061 USA
[2] Virginia Polytech Inst & State Univ, Virginia Bioinformat Inst, Blacksburg, VA 24061 USA
[3] Virginia Polytech Inst & State Univ, Dept Comp Sci, Blacksburg, VA 24061 USA
[4] Virginia Polytech Inst & State Univ, Dept Biol Sci, Blacksburg, VA 24061 USA
基金
美国国家卫生研究院; 美国国家科学基金会;
关键词
stochastic phenotype; mitotic exit; non-genetic variability; cell cycle modeling; computational biology; stochastic modeling; deterministic modeling; CELL-CYCLE; PROTEIN EXPRESSION; MOLECULAR-BIOLOGY; GENE-EXPRESSION; CLB2; VARIABILITY; PROTEOLYSIS; ANTAGONISM; MECHANISMS; SIMULATION;
D O I
10.4161/cc.10.6.14966
中图分类号
Q2 [细胞生物学];
学科分类号
071009 ; 090102 ;
摘要
Unlike many mutants that are completely viable or inviable, the CLB2-db Delta clb5 Delta mutant of Saccharomyces cerevisiae is inviable in glucose but partially viable on slower growth media such as raffinose. On raffinose, the mutant cells can bud and divide but in each cycle there is a chance that a cell will fail to divide (telophase arrest), causing it to exit the cell cycle. This effect gives rise to a stochastic phenotype that cannot be explained by a deterministic model. We measure the interbud times of wild-type and mutant cells growing on raffinose and compute statistics and distributions to characterize the mutant's behavior. We convert a detailed deterministic model of the budding yeast cell cycle to a stochastic model and determine the extent to which it captures the stochastic phenotype of the mutant strain. Predictions of the mathematical model are in reasonable agreement with our experimental data and suggest directions for improving the model. Ultimately, the ability to accurately model stochastic phenotypes may prove critical to understanding disease and therapeutic interventions in higher eukaryotes.
引用
收藏
页码:999 / 1009
页数:11
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