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Ergodic Sets as Cell Phenotype of Budding Yeast Cell Cycle

Overview of attention for article published in PLOS ONE, October 2012
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Title
Ergodic Sets as Cell Phenotype of Budding Yeast Cell Cycle
Published in
PLOS ONE, October 2012
DOI 10.1371/journal.pone.0045780
Pubmed ID
Authors

Robert G. Todd, Tomáš Helikar

Abstract

It has been suggested that irreducible sets of states in Probabilistic Boolean Networks correspond to cellular phenotype. In this study, we identify such sets of states for each phase of the budding yeast cell cycle. We find that these "ergodic sets" underly the cyclin activity levels during each phase of the cell cycle. Our results compare to the observations made in several laboratory experiments as well as the results of differential equation models. Dynamical studies of this model: (i) indicate that under stochastic external signals the continuous oscillating waves of cyclin activity and the opposing waves of CKIs emerge from the logic of a Boolean-based regulatory network without the need for specific biochemical/kinetic parameters; (ii) suggest that the yeast cell cycle network is robust to the varying behavior of cell size (e.g., cell division under nitrogen deprived conditions); (iii) suggest the irreversibility of the Start signal is a function of logic of the G1 regulon, and changing the structure of the regulatory network can render start reversible.

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The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Mexico 1 3%
Canada 1 3%
Unknown 29 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 32%
Student > Ph. D. Student 9 29%
Student > Master 4 13%
Student > Bachelor 3 10%
Student > Doctoral Student 1 3%
Other 2 6%
Unknown 2 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 42%
Biochemistry, Genetics and Molecular Biology 9 29%
Computer Science 3 10%
Physics and Astronomy 1 3%
Medicine and Dentistry 1 3%
Other 1 3%
Unknown 3 10%