Hybrid ODE/SSA Model of the Budding Yeast Cell Cycle Control Mechanism with Mutant Case Study

Ahmadian, Mansooreh; Wang, Shuo; Tyson, John J.; Cao, Yu

doi:10.1145/3107411.3107437

Cited by 5 publications

(9 citation statements)

References 28 publications

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“…For gene-mRNA-protein regulatory networks, an effective 'partitioning' strategy is to simulate mRNA synthesis and degradation by SSA and all subsequent protein-interaction processes by nonlinear ODEs. We have shown (Ahmadian et al, 2017;Liu et al, 2012;Wang et al, 2016) that this approach is 50-100 times faster than 'brute force' SSA, without sacrificing accuracy of the stochastic results.…”

Section: Stochastic Modelsmentioning

confidence: 95%

Modeling the dynamic behavior of biochemical regulatory networks

Tyson

Laomettachit

Kraikivski

2019

Journal of Theoretical Biology

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Strategies for modeling the complex dynamical behavior of gene/protein regulatory networks have evolved over the last 50 years as both the knowledge of these molecular control systems and the power of computing resources have increased. Here, we review a number of common modeling approaches, including Boolean (logical) models, systems of piecewise-linear or fully non-linear ordinary differential equations, and stochastic models (including hybrid deterministic/stochastic approaches). We discuss the pro's and con's of each approach, to help novice modelers choose a modeling strategy suitable to their problem, based on the type and bounty of available experimental information. We illustrate different modeling strategies in terms of some abstract network motifs, and in the specific context of cell cycle regulation.

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Section: Stochastic Modelsmentioning

confidence: 95%

Modeling the dynamic behavior of biochemical regulatory networks

Tyson

Laomettachit

Kraikivski

2019

Journal of Theoretical Biology

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“…Thus, to construct a model that accounts for the variabilities in the cell cycle, it is crucial to incorporate the dynamics of mRNAs along with protein regulators in a stochastic model. Therefore, in our earlier work in [37], we substantially extended Chen’s model by adding the dynamics of 19 important mRNAs. Moreover, we constructed a hybrid stochastic model that not only explains the average properties of the budding yeast cell cycle, it can also reproduce the variability in critical characteristics of the cell cycle, in addition to stochastic phenotypes of specific mutants.…”

Section: Methods and Simulationmentioning

confidence: 99%

“…After such partitioning we apply the HR hybrid algorithm to simulate the trajectories of state variables. The step by step algorithm is provided in [35, 37] and more implementation details can be found in [42]. In those works, we have shown that placing the dynamics of mRNAs into SSA regime and solving ODEs for protein regulatory network leads to sufficiently accurate results, and significantly reduces the computational cost.…”

Section: Methods and Simulationmentioning

confidence: 99%

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A stochastic model of size control in the budding yeast cell cycle

2019

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Background Cell size is a key characteristic that significantly affects many aspects of cellular physiology. There are specific control mechanisms during cell cycle that maintain the cell size within a range from generation to generation. Such control mechanisms introduce substantial variabilities to important properties of the cell cycle such as growth and division. To quantitatively study the effect of such variability in progression through cell cycle, detailed stochastic models are required. Results In this paper, a new hybrid stochastic model is proposed to study the effect of molecular noise and size control mechanism on the variabilities in cell cycle of the budding yeast Saccharomyces cerevisiae . The proposed model provides an accurate, yet computationally efficient approach for simulation of an intricate system by integrating the deterministic and stochastic simulation schemes. The developed hybrid stochastic model can successfully capture several key features of the cell cycle observed in experimental data. In particular, the proposed model: 1) confirms that the majority of noise in size control stems from low copy numbers of transcripts in the G1 phase, 2) identifies the size and time regulation modules in the size control mechanism, and 3) conforms with phenotypes of early G1 mutants in exquisite detail. Conclusions Hybrid stochastic modeling approach can be used to provide quantitative descriptions for stochastic properties of the cell cycle within a computationally efficient framework.

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“…After such partitioning we apply the HR hybrid algorithm to simulate the trajectories of state variables. The step by step algorithm is provided in [35,37] and more implementation details can be found in [42]. In those works, we have shown that placing the dynamics of mRNAs into SSA regime and solving ODEs for protein regulatory network leads to sufficiently accurate results, and significantly reduces the computational cost.…”

Section: The Hr Hybrid Modelmentioning

confidence: 99%

A Stochastic Model of Size Control in the Budding Yeast Cell Cycle

Ahmadian

Tyson

Cao

2018

Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

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Background: Cell size is a key characteristic that significantly affects many aspects of cellular physiology. There are specific control mechanisms during cell cycle that maintain the cell size within a range from generation to generation. Such control mechanisms introduce substantial variabilities to important properties of the cell cycle such as growth and division. To quantitatively study the effect of such variability in progression through cell cycle, detailed stochastic models are required. Results: In this paper, a new hybrid stochastic model is proposed to study the effect of molecular noise and size control mechanism on the variabilities in cell cycle of the budding yeast Saccharomyces cerevisiae. The proposed model provides an accurate, yet computationally efficient approach for simulation of an intricate system by integrating the deterministic and stochastic simulation schemes. The developed hybrid stochastic model can successfully capture several key features of the cell cycle observed in experimental data. In particular, the proposed model: 1) confirms that the majority of noise in size control stems from low copy numbers of transcripts in the G1 phase, 2) identifies the size and time regulation modules in the size control mechanism, and 3) conforms with phenotypes of early G1 mutants in exquisite detail. Conclusions: Hybrid stochastic modeling approach can be used to provide quantitative descriptions for stochastic properties of the cell cycle within a computationally efficient framework.

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Hybrid ODE/SSA Model of the Budding Yeast Cell Cycle Control Mechanism with Mutant Case Study

Cited by 5 publications

References 28 publications

Modeling the dynamic behavior of biochemical regulatory networks

Modeling the dynamic behavior of biochemical regulatory networks

A stochastic model of size control in the budding yeast cell cycle

A Stochastic Model of Size Control in the Budding Yeast Cell Cycle

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