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A Hybrid Stochastic Model of the Budding Yeast Cell Cycle Control Mechanism

Published: 02 October 2016 Publication History

Abstract

The budding yeast cell cycle is regulated by a complex chemical reaction network. Several deterministic models have been proposed to model this control mechanism. However, experimental data exhibit considerable variability from cell to cell during cell growth and division. It is also observed that certain mutant cells are more vulnerable to noise than wild type cells. The observed variability comes from two sources: intrinsic noise coming from fluctuations of molecules and extrinsic noise introduced by variations in the division process. As a result, molecular fluctuations cannot be neglected, and they may significantly affect the behavior of a cell. To accurately model the cell cycle control mechanism, stochastic models are needed.
A rigorous solution to this problem is to convert a deterministic model to its stochastic equivalent and apply Gille-spie's stochastic simulation algorithm (SSA). But the conversion process is not straightforward and often results in a much larger system. Moreover, the high computational cost of Gillespie's algorithm make it difficult to simulate a practical budding yeast cell cycle model. In this manuscript, we present a hybrid (ODE/SSA) stochastic model for the budding yeast cell cycle. Based on error analysis for multiscale systems and the observation that fluctuations of mRNAs are the primary sources of noise, in this hybrid model the dynamics of mRNAs are simulated by Gillespie's algorithm, while the cell cycle mechanism at the protein level is modeled by ordinary differential equations (ODEs). Numerical experiments, implemented by Haseltine and Rawlings' hybrid simulation algorithm, demonstrate that the hybrid model matches very well with biological experimental data for both wild-type cells and mutant cells in different nutrient media.

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Cited By

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  • (2018)Accuracy Analysis of Hybrid Stochastic Simulation Algorithm on Linear Chain Reaction SystemsBulletin of Mathematical Biology10.1007/s11538-018-0461-z81:8(3024-3052)Online publication date: 10-Jul-2018

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cover image ACM Conferences
BCB '16: Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics
October 2016
675 pages
ISBN:9781450342254
DOI:10.1145/2975167
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 02 October 2016

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  1. budding yeast cell cycle model
  2. hybrid ODE/SSA method
  3. stochastic simulation algorithm (SSA)

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View all
  • (2018)Accuracy Analysis of Hybrid Stochastic Simulation Algorithm on Linear Chain Reaction SystemsBulletin of Mathematical Biology10.1007/s11538-018-0461-z81:8(3024-3052)Online publication date: 10-Jul-2018

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