Dynamical Analysis of Yeast Cell Cycle Using a Stochastic Markov Model
Introduction: The cell cycle network is responsible of control, growth and proliferation of cells. The relationship between the cell cycle network and cancer emergence, and the complex reciprocal interactions between genes/proteins calls for computational models to analyze this regulatory network. A...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | fas |
| Published: |
Kerman University of Medical Sciences
2021-03-01
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| Series: | مجله انفورماتیک سلامت و زیست پزشکی |
| Subjects: | |
| Online Access: | http://jhbmi.ir/article-1-484-en.html |
| Summary: | Introduction: The cell cycle network is responsible of control, growth and proliferation of cells. The relationship between the cell cycle network and cancer emergence, and the complex reciprocal interactions between genes/proteins calls for computational models to analyze this regulatory network. Ample experimental data confirm the existence of random behaviors in the interactions between genes and proteins in gene regulatory networks. Genetic factors, regulatory dynamics at the microscopic level, transcription rates of genes, and many other factors that depend on variable environmental conditions cause random behaviors in the cell cycle network.
Method: The aim of this study was to present a stochastic Markov model to simulate interactions between proteins in a complex network of fission yeast cell cycle and to predict the dynamics of proteins. We used local sensitivity analysis to investigate the relationship between the weight of protein / gene interactions with the probabilities of phase transition in the cell cycle.
Results: Using this model, the probability of transition between different phases of the cell cycle in the presence of different levels of noise was investigated and it was proved that the cell cycle path has the highest probability among all possible pathways for the cell. By performing sensitivity analysis, the correlation between the weight of interactions between proteins and the probability of transition between different phases of the cell cycle was calculated.
Conclusion: Our local sensitivity analysis revealed that how perturbation on parameters affect the transition probabilities between subsequent cell cycle phases, so it suggests testable hypotheses in the experimental environments. Also, the model of this study proves the stability of the cell cycle in the presence of moderate levels of noise. |
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| ISSN: | 2423-3870 2423-3498 |