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The effective application of a discrete transition model to explore cell-cycle regulation in yeast.

Rubinstein A, Hazan O, Chor B, Pinter RY, Kassir Y - BMC Res Notes (2013)

Bottom Line: Bench biologists often do not take part in the development of computational models for their systems, and therefore, they frequently employ them as "black-boxes".Our aim was to construct and test a model that does not depend on the availability of quantitative data, and can be directly used without a need for intensive computational background.This methodology can be easily integrated as a useful approach for the study of networks, enriching experimental biology with computational insights.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel.

ABSTRACT

Background: Bench biologists often do not take part in the development of computational models for their systems, and therefore, they frequently employ them as "black-boxes". Our aim was to construct and test a model that does not depend on the availability of quantitative data, and can be directly used without a need for intensive computational background.

Results: We present a discrete transition model. We used cell-cycle in budding yeast as a paradigm for a complex network, demonstrating phenomena such as sequential protein expression and activity, and cell-cycle oscillation. The structure of the network was validated by its response to computational perturbations such as mutations, and its response to mating-pheromone or nitrogen depletion. The model has a strong predicative capability, demonstrating how the activity of a specific transcription factor, Hcm1, is regulated, and what determines commitment of cells to enter and complete the cell-cycle.

Conclusion: The model presented herein is intuitive, yet is expressive enough to elucidate the intrinsic structure and qualitative behavior of large and complex regulatory networks. Moreover our model allowed us to examine multiple hypotheses in a simple and intuitive manner, giving rise to testable predictions. This methodology can be easily integrated as a useful approach for the study of networks, enriching experimental biology with computational insights.

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Hypotheses regarding how Hcm1* activity is regulated. A. hypotheses; B. levels of CLB2 and S-phase; C. levels of S-phase, mitosis and anaphase in α-factor treatment of cells taken from G1 and G1/S stages.
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Figure 6: Hypotheses regarding how Hcm1* activity is regulated. A. hypotheses; B. levels of CLB2 and S-phase; C. levels of S-phase, mitosis and anaphase in α-factor treatment of cells taken from G1 and G1/S stages.

Mentions: The transient expression of Hcm1 is not required for the transient transcription of its target genes [13]. Because Hcm1 is subject to post-translational modification, it was suggested that this modification affects its activity during the cell cycle [13]. Since Hcm1 is a probable Cdk target [40] we examined if this regulation is mediated by either Cln3/Cdk, Cln1/Cdk1 or Clb5/Cdk (Figure 6). Simulations revealed that activation of Hcm1 by Cln3/Cdk resulted in premature decline in the transcription of CLB2 in relation to S-phase (Figure 6B, upper panel). On the other hand, regulation by either Cln1/Cdk or Clb5/Cdk, showed the expected behavior (Figure 6B, middle and lower panels). In order to discriminate between the latter two hypotheses, we examined the response of cells to pheromone treatment. Regulation by Clb5/Cdk showed an abnormal phenotype, namely some G1cells arrested after completion of S-phase (Figure 6C). Our simulations predict that Cln1/Cdk rather than Clb5/Cdk or Clb2/Cdk, is responsible for regulating the activity of Hcm1. All simulations in this report were done according to this prediction.


The effective application of a discrete transition model to explore cell-cycle regulation in yeast.

Rubinstein A, Hazan O, Chor B, Pinter RY, Kassir Y - BMC Res Notes (2013)

Hypotheses regarding how Hcm1* activity is regulated. A. hypotheses; B. levels of CLB2 and S-phase; C. levels of S-phase, mitosis and anaphase in α-factor treatment of cells taken from G1 and G1/S stages.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3750494&req=5

Figure 6: Hypotheses regarding how Hcm1* activity is regulated. A. hypotheses; B. levels of CLB2 and S-phase; C. levels of S-phase, mitosis and anaphase in α-factor treatment of cells taken from G1 and G1/S stages.
Mentions: The transient expression of Hcm1 is not required for the transient transcription of its target genes [13]. Because Hcm1 is subject to post-translational modification, it was suggested that this modification affects its activity during the cell cycle [13]. Since Hcm1 is a probable Cdk target [40] we examined if this regulation is mediated by either Cln3/Cdk, Cln1/Cdk1 or Clb5/Cdk (Figure 6). Simulations revealed that activation of Hcm1 by Cln3/Cdk resulted in premature decline in the transcription of CLB2 in relation to S-phase (Figure 6B, upper panel). On the other hand, regulation by either Cln1/Cdk or Clb5/Cdk, showed the expected behavior (Figure 6B, middle and lower panels). In order to discriminate between the latter two hypotheses, we examined the response of cells to pheromone treatment. Regulation by Clb5/Cdk showed an abnormal phenotype, namely some G1cells arrested after completion of S-phase (Figure 6C). Our simulations predict that Cln1/Cdk rather than Clb5/Cdk or Clb2/Cdk, is responsible for regulating the activity of Hcm1. All simulations in this report were done according to this prediction.

Bottom Line: Bench biologists often do not take part in the development of computational models for their systems, and therefore, they frequently employ them as "black-boxes".Our aim was to construct and test a model that does not depend on the availability of quantitative data, and can be directly used without a need for intensive computational background.This methodology can be easily integrated as a useful approach for the study of networks, enriching experimental biology with computational insights.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel.

ABSTRACT

Background: Bench biologists often do not take part in the development of computational models for their systems, and therefore, they frequently employ them as "black-boxes". Our aim was to construct and test a model that does not depend on the availability of quantitative data, and can be directly used without a need for intensive computational background.

Results: We present a discrete transition model. We used cell-cycle in budding yeast as a paradigm for a complex network, demonstrating phenomena such as sequential protein expression and activity, and cell-cycle oscillation. The structure of the network was validated by its response to computational perturbations such as mutations, and its response to mating-pheromone or nitrogen depletion. The model has a strong predicative capability, demonstrating how the activity of a specific transcription factor, Hcm1, is regulated, and what determines commitment of cells to enter and complete the cell-cycle.

Conclusion: The model presented herein is intuitive, yet is expressive enough to elucidate the intrinsic structure and qualitative behavior of large and complex regulatory networks. Moreover our model allowed us to examine multiple hypotheses in a simple and intuitive manner, giving rise to testable predictions. This methodology can be easily integrated as a useful approach for the study of networks, enriching experimental biology with computational insights.

Show MeSH