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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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Simulation of the network under normal conditions of wild type and mutant strains. A. wild-type. B. and C. cdc6Δ. The initial states of CDC6 and Cdc6 are either 9 (B) or 0 (C). D. clb2Δ, E. cdc20Δ. In A oscillations continued infinitely, whereas in B-E simulation reached steady state and all steps are shown.
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Figure 3: Simulation of the network under normal conditions of wild type and mutant strains. A. wild-type. B. and C. cdc6Δ. The initial states of CDC6 and Cdc6 are either 9 (B) or 0 (C). D. clb2Δ, E. cdc20Δ. In A oscillations continued infinitely, whereas in B-E simulation reached steady state and all steps are shown.

Mentions: The initial states assigned to the nodes reflect a single cell at early G1 (see details in Methods). In general, a simulation goes on until either a steady state or an infinite loop is reached. Our simulation demonstrated oscillation, accurate and sequential progression through S-phase, entry into metaphase (M), and exit from metaphase (anaphase, A) (Figure 3A). Figure 3A shows two cell-cycles, but identical oscillations occurred infinitely (data not shown). Our simulation revealed the sequential and periodic expression of the G1, G1/S, S and M-phase cyclins, namely, Cln3, Cln1, Clb5, and Clb2 (RNA and proteins) and their activities (when in complex with Cdk1) (Figure 3A), as expected from experimental results (reviewed in [11,12]). Periodic and timely expression was also evident for all transcription factors that regulate the cell-cycle (Additional file 1), in agreement with experimental data. [13]. This validates the network structure and parameters.


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)

Simulation of the network under normal conditions of wild type and mutant strains. A. wild-type. B. and C. cdc6Δ. The initial states of CDC6 and Cdc6 are either 9 (B) or 0 (C). D. clb2Δ, E. cdc20Δ. In A oscillations continued infinitely, whereas in B-E simulation reached steady state and all steps are shown.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Simulation of the network under normal conditions of wild type and mutant strains. A. wild-type. B. and C. cdc6Δ. The initial states of CDC6 and Cdc6 are either 9 (B) or 0 (C). D. clb2Δ, E. cdc20Δ. In A oscillations continued infinitely, whereas in B-E simulation reached steady state and all steps are shown.
Mentions: The initial states assigned to the nodes reflect a single cell at early G1 (see details in Methods). In general, a simulation goes on until either a steady state or an infinite loop is reached. Our simulation demonstrated oscillation, accurate and sequential progression through S-phase, entry into metaphase (M), and exit from metaphase (anaphase, A) (Figure 3A). Figure 3A shows two cell-cycles, but identical oscillations occurred infinitely (data not shown). Our simulation revealed the sequential and periodic expression of the G1, G1/S, S and M-phase cyclins, namely, Cln3, Cln1, Clb5, and Clb2 (RNA and proteins) and their activities (when in complex with Cdk1) (Figure 3A), as expected from experimental results (reviewed in [11,12]). Periodic and timely expression was also evident for all transcription factors that regulate the cell-cycle (Additional file 1), in agreement with experimental data. [13]. This validates the network structure and parameters.

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
Related in: MedlinePlus