Abstract
The growth and division of eukaryotic cells are regulated by complex, multi-scale networks. In this process, the mechanism of controlling cell-cycle progression has to be robust against inherent noise in the system. In this paper, a hybrid stochastic model is developed to study the effects of noise on the control mechanism of the budding yeast cell cycle. The modeling approach leverages, in a single multi-scale model, the advantages of two regimes: (1) the computational efficiency of a deterministic approach, and (2) the accuracy of stochastic simulations. Our results show that this hybrid stochastic model achieves high computational efficiency while generating simulation results that match very well with published experimental measurements.
Highlights
The eukaryotic cell cycle is a complex process by which a growing cell replicates its DNA and divides into two cells, each capable of repeating the process
We develop a hybrid stochastic model of the budding yeast cell cycle, consisting of 45 proteins and 19 mRNAs
In Section Methods, we will elaborate the steps for building our hybrid stochastic model
Summary
The eukaryotic cell cycle is a complex process by which a growing cell replicates its DNA and divides into two cells, each capable of repeating the process. Progression through the cycle is controlled by networks of genes, mRNAs, and proteins, with interactions that can be modeled as chemical reaction channels. Experimental studies and mathematical models of frog eggs[1,2], fission yeast[3,4], and budding yeast[5,6] have shed light on mechanisms of cell-cycle regulation in the cells of higher organisms. Various modeling approaches, such as deterministic models[10,11,12], Boolean networks[13,14,15,16,17,18,19], and stochastic models[20,21,22,23,24,25,26], have been adopted to explore the roles of different gene and protein interactions in robust progression through the cell cycle
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