The time-profile of cell growth in fission yeast: model selection criteria favoring bilinear models over exponential ones.
BACKGROUND
There is considerable controversy concerning the exact growth profile of size parameters during the cell cycle. Linear, exponential and bilinear models are commonly considered, and the same model may not apply for all species. Selection of the most adequate model to describe a given data-set requires the use of quantitative model selection criteria, such as the partial (sequential) F-test, the Akaike information criterion and the Schwarz Bayesian information criterion, which are suitable for comparing differently parameterized models in terms of the quality and robustness of the fit but have not yet been used in cell growth-profile studies.


RESULTS
Length increase data from representative individual fission yeast (Schizosaccharomyces pombe) cells measured on time-lapse films have been reanalyzed using these model selection criteria. To fit the data, an extended version of a recently introduced linearized biexponential (LinBiExp) model was developed, which makes possible a smooth, continuously differentiable transition between two linear segments and, hence, allows fully parametrized bilinear fittings. Despite relatively small differences, essentially all the quantitative selection criteria considered here indicated that the bilinear model was somewhat more adequate than the exponential model for fitting these fission yeast data.


CONCLUSIONS
A general quantitative framework was introduced to judge the adequacy of bilinear versus exponential models in the description of growth time-profiles. For single cell growth, because of the relatively limited data-range, the statistical evidence is not strong enough to favor one model clearly over the other and to settle the bilinear versus exponential dispute. Nevertheless, for the present individual cell growth data for fission yeast, the bilinear model seems more adequate according to all metrics, especially in the case of wee1Delta cells.
IVAX Research, Inc., Miami, FL 33137, USA. Peter_Buchwald@ivax.com
Peter
P
Buchwald
Akos
A
Sveiczer
D001703:Biophysics / Q000379:methods; D002453:Cell Cycle; D007700:Kinetics; D008954:Models, Biological; D015233:Models, Statistical; D008962:Models, Theoretical; D009154:Mutation; D012568:Schizosaccharomyces / Q000502:physiology*; D013997:Time Factors