Timing robustness in the budding and fission yeast cell cycles.

Citation data:

PloS one, ISSN: 1932-6203, Vol: 5, Issue: 2, Page: e8906

Publication Year:
2010
Usage 3848
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Citations 13
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Repository URL:
http://hdl.handle.net/10754/596850
PMID:
20126540
DOI:
10.1371/journal.pone.0008906; 10.1371/journal.pone.0008906.g002; 10.1371/journal.pone.0008906.t001; 10.1371/journal.pone.0008906.g001
PMCID:
PMC2813865; 2813865
Author(s):
Karan Mangla; David L. Dill; Mark A. Horowitz; Vladimir Brezina
Publisher(s):
Public Library of Science (PLoS); Figshare
Tags:
Medicine; Biochemistry, Genetics and Molecular Biology; Agricultural and Biological Sciences; Models, Biological; Biological Sciences; Cell Biology; Information And Computing Sciences; yeast; silico; mutation
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article description
Robustness of biological models has emerged as an important principle in systems biology. Many past analyses of Boolean models update all pending changes in signals simultaneously (i.e., synchronously), making it impossible to consider robustness to variations in timing that result from noise and different environmental conditions. We checked previously published mathematical models of the cell cycles of budding and fission yeast for robustness to timing variations by constructing Boolean models and analyzing them using model-checking software for the property of speed independence. Surprisingly, the models are nearly, but not totally, speed-independent. In some cases, examination of timing problems discovered in the analysis exposes apparent inaccuracies in the model. Biologically justified revisions to the model eliminate the timing problems. Furthermore, in silico random mutations in the regulatory interactions of a speed-independent Boolean model are shown to be unlikely to preserve speed independence, even in models that are otherwise functional, providing evidence for selection pressure to maintain timing robustness. Multiple cell cycle models exhibit strong robustness to timing variation, apparently due to evolutionary pressure. Thus, timing robustness can be a basis for generating testable hypotheses and can focus attention on aspects of a model that may need refinement.