scholarly journals A Model of Yeast Cell-Cycle Regulation Based on a Standard Component Modeling Strategy for Protein Regulatory Networks

PLoS ONE ◽  
2016 ◽  
Vol 11 (5) ◽  
pp. e0153738 ◽  
Author(s):  
Teeraphan Laomettachit ◽  
Katherine C. Chen ◽  
William T. Baumann ◽  
John J. Tyson
1991 ◽  
Vol 5 (12b) ◽  
pp. 2405-2419 ◽  
Author(s):  
D Lydall ◽  
G Ammerer ◽  
K Nasmyth

2010 ◽  
Vol 6 (1) ◽  
pp. 405 ◽  
Author(s):  
Debashis Barik ◽  
William T Baumann ◽  
Mark R Paul ◽  
Bela Novak ◽  
John J Tyson

1993 ◽  
Vol 120 (6) ◽  
pp. 1305-1320 ◽  
Author(s):  
D J Lew ◽  
S I Reed

Analysis of cell cycle regulation in the budding yeast Saccharomyces cerevisiae has shown that a central regulatory protein kinase, Cdc28, undergoes changes in activity through the cell cycle by associating with distinct groups of cyclins that accumulate at different times. The various cyclin/Cdc28 complexes control different aspects of cell cycle progression, including the commitment step known as START and mitosis. We found that altering the activity of Cdc28 had profound effects on morphogenesis during the yeast cell cycle. Our results suggest that activation of Cdc28 by G1 cyclins (Cln1, Cln2, or Cln3) in unbudded G1 cells triggers polarization of the cortical actin cytoskeleton to a specialized pre-bud site at one end of the cell, while activation of Cdc28 by mitotic cyclins (Clb1 or Clb2) in budded G2 cells causes depolarization of the cortical actin cytoskeleton and secretory apparatus. Inactivation of Cdc28 following cyclin destruction in mitosis triggers redistribution of cortical actin structures to the neck region for cytokinesis. In the case of pre-bud site assembly following START, we found that the actin rearrangement could be triggered by Cln/Cdc28 activation in the absence of de novo protein synthesis, suggesting that the kinase may directly phosphorylate substrates (such as actin-binding proteins) that regulate actin distribution in cells.


2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Stephen Kotiang ◽  
Ali Eslami

Abstract Background The desire to understand genomic functions and the behavior of complex gene regulatory networks has recently been a major research focus in systems biology. As a result, a plethora of computational and modeling tools have been proposed to identify and infer interactions among biological entities. Here, we consider the general question of the effect of perturbation on the global dynamical network behavior as well as error propagation in biological networks to incite research pertaining to intervention strategies. Results This paper introduces a computational framework that combines the formulation of Boolean networks and factor graphs to explore the global dynamical features of biological systems. A message-passing algorithm is proposed for this formalism to evolve network states as messages in the graph. In addition, the mathematical formulation allows us to describe the dynamics and behavior of error propagation in gene regulatory networks by conducting a density evolution (DE) analysis. The model is applied to assess the network state progression and the impact of gene deletion in the budding yeast cell cycle. Simulation results show that our model predictions match published experimental data. Also, our findings reveal that the sample yeast cell-cycle network is not only robust but also consistent with real high-throughput expression data. Finally, our DE analysis serves as a tool to find the optimal values of network parameters for resilience against perturbations, especially in the inference of genetic graphs. Conclusion Our computational framework provides a useful graphical model and analytical tools to study biological networks. It can be a powerful tool to predict the consequences of gene deletions before conducting wet bench experiments because it proves to be a quick route to predicting biologically relevant dynamic properties without tunable kinetic parameters.


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