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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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A representation of a biological checkpoint. The positive state of node C is a condition for A to activate the checkpoint node. The activation of B depends on the absence of C.
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Figure 2: A representation of a biological checkpoint. The positive state of node C is a condition for A to activate the checkpoint node. The activation of B depends on the absence of C.

Mentions: We used the budding yeast cell-cycle as a paradigm for a complex biological regulatory system. Figure 1 shows a schematic representation of this network. For details see Methods. The network includes 67 nodes which represent important components (i.e. RNA, proteins, cellular events) required for proper transitions between all cell-cycle phases. Redundant gene functions were represented in our network each by a single element. A checkpoint was modeled by a node whose level is induced by a specific regulator, but its ability to activate some other element depends on the absence of that regulator (Figure 2). For instance, the DNA replication checkpoint (cPS) is activated by S-phase, and it promotes metaphase only in the absence (completion) of S-phase [10] (Figure 1B).


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)

A representation of a biological checkpoint. The positive state of node C is a condition for A to activate the checkpoint node. The activation of B depends on the absence of C.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: A representation of a biological checkpoint. The positive state of node C is a condition for A to activate the checkpoint node. The activation of B depends on the absence of C.
Mentions: We used the budding yeast cell-cycle as a paradigm for a complex biological regulatory system. Figure 1 shows a schematic representation of this network. For details see Methods. The network includes 67 nodes which represent important components (i.e. RNA, proteins, cellular events) required for proper transitions between all cell-cycle phases. Redundant gene functions were represented in our network each by a single element. A checkpoint was modeled by a node whose level is induced by a specific regulator, but its ability to activate some other element depends on the absence of that regulator (Figure 2). For instance, the DNA replication checkpoint (cPS) is activated by S-phase, and it promotes metaphase only in the absence (completion) of S-phase [10] (Figure 1B).

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