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Dynamical Analysis of the Fission Yeast Cell Cycle Via Markov Chain Publisher Pubmed



Shafiekhani S1, 2, 3 ; Kraikivski P4 ; Gheibi N5 ; Ahmadian M6 ; Jafari AH1, 2
Authors
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Authors Affiliations
  1. 1. Department of Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
  2. 2. Research Center for Biomedical Technologies and Robotics, Tehran, Iran
  3. 3. Students’ Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran
  4. 4. Division of Systems Biology, Academy of Integrated Science, Virginia Tech, Blacksburg, VA, United States
  5. 5. Cellular and Molecular Research Center, Research Institute for Prevention of Non-Communicable Diseases, Qazvin University of Medical Sciences, Qazvin, Iran
  6. 6. Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado-Denver Anschutz Medical Campus, Aurora, CO, United States

Source: Current Genetics Published:2021


Abstract

The cell cycle is a complex network involved in the regulation of cell growth and proliferation. Intrinsic molecular noise in gene expression in the cell cycle network can generate fluctuations in protein concentration. How the cell cycle network maintains its robust transitions between cell cycle phases in the presence of these fluctuations remains unclear. To understand the complex and robust behavior of the cell cycle system in the presence of intrinsic noise, we developed a Markov model for the fission yeast cell cycle system. We quantified the effect of noise on gene and protein activity and on the probability of transition between different phases of the cell cycle. Our analysis shows how network perturbations decide the fate of the cell. Our model predicts that the cell cycle pathway (subsequent transitions from G 1 → S → G 2 → M) is the most robust and probable pathway among all possible trajectories in the cell cycle network. We performed a sensitivity analysis to find correlations between protein interaction weights and transition probabilities between cell cycle phases. The sensitivity analysis predicts how network perturbations affect the transition probability between different cell cycle phases and, consequently, affect different cell fates, thus, forming testable in vitro/in vivo hypotheses. Our simulation results agree with published experimental findings and reveal how noise in the cell cycle regulatory network can affect cell cycle progression. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.