Testing a mathematical model of the yeast cell cycle.
Journal: 2002/May - Molecular Biology of the Cell
ISSN: 1059-1524
Abstract:
We derived novel, testable predictions from a mathematical model of the budding yeast cell cycle. A key qualitative prediction of bistability was confirmed in a strain simultaneously lacking cdc14 and G1 cyclins. The model correctly predicted quantitative dependence of cell size on gene dosage of the G1 cyclin CLN3, but it incorrectly predicted strong genetic interactions between G1 cyclins and the anaphase-promoting complex specificity factor Cdh1. To provide constraints on model generation, we determined accurate concentrations for the abundance of all nine cyclins as well as the inhibitor Sic1 and the catalytic subunit Cdc28. For many of these we determined abundance throughout the cell cycle by centrifugal elutriation, in the presence or absence of Cdh1. In addition, perturbations to the Clb-kinase oscillator were introduced, and the effects on cyclin and Sic1 levels were compared between model and experiment. Reasonable agreement was obtained in many of these experiments, but significant experimental discrepancies from the model predictions were also observed. Thus, the model is a strong but incomplete attempt at a realistic representation of cell cycle control. Constraints of the sort developed here will be important in development of a truly predictive model.
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Mol Biol Cell 13(1): 52-70

Testing a Mathematical Model of the Yeast Cell Cycle

The Rockefeller University, New York, New York 10021
Corresponding author. E-mail address: ude.rellefekcor.xavkcor@ssorcf.
Received 2001 May 25; Revised 2001 Sep 25; Accepted 2001 Oct 10.

Abstract

We derived novel, testable predictions from a mathematical model of the budding yeast cell cycle. A key qualitative prediction of bistability was confirmed in a strain simultaneously lacking cdc14 and G1 cyclins. The model correctly predicted quantitative dependence of cell size on gene dosage of the G1 cyclin CLN3, but it incorrectly predicted strong genetic interactions between G1 cyclins and the anaphase- promoting complex specificity factor Cdh1. To provide constraints on model generation, we determined accurate concentrations for the abundance of all nine cyclins as well as the inhibitor Sic1 and the catalytic subunit Cdc28. For many of these we determined abundance throughout the cell cycle by centrifugal elutriation, in the presence or absence of Cdh1. In addition, perturbations to the Clb-kinase oscillator were introduced, and the effects on cyclin and Sic1 levels were compared between model and experiment. Reasonable agreement was obtained in many of these experiments, but significant experimental discrepancies from the model predictions were also observed. Thus, the model is a strong but incomplete attempt at a realistic representation of cell cycle control. Constraints of the sort developed here will be important in development of a truly predictive model.

Abstract

Extracts from log-phase diploid cultures (W303 background) using the NaOH/TCA method were analyzed for number of copies per cell of the indicated protein fused to protein A, as described in MATERIALS AND METHODS. Note that these are unsynchronized cultures, so the number of copies per cell measured is less than the peak number of copies per cell. Values are mean ± SEM, with the number of determinations in parentheses. Figure Figure44 presents a sample experiment in this series.

All data are derived from diploid strains. The protein A data are reprinted from Table Table11 to allow comparison. Experiments determining the level of endogenous Clb2 using anti-Clb2 and MBP-Clb2 as standard, and determining myc-tagged Cln2, Clb5, and Cln3 with GST-myc using anti-myc and GST-myc as standard are presented. The myc-tagged genes were expressed in heterozygous diploids in the BF264-15D background. For the determinations of myc-tagged protein and endogenous Clb2 levels, one culture (two for Clb2) was extracted with the glass bead/SDS method and one with the NaOH/TCA method. These two methods gave similarly efficient extraction and similar results. Values are mean ± SEM, with number of determinations in parentheses.

ACKNOWLEDGMENTS

The authors thank Caihong Li for expert technical assistance, especially in the challenging experiment in Figure Figure1;1; John Tyson and Kathy Chen for help in understanding and using the model; Mike Rout and Brian Chait for useful discussions; Peter Schwartz and Jung-Im Lee for preliminary work in the system presented in Figure Figure2;2; Phillip Kaldis and Mike Rout for plasmids; Angelika Amon, Bruce Futcher, and Masaki Shirayama for strains; and Mike Rout for the anti-Nop1 antibody. This work was supported by Public Health Service grant GM47238.

ACKNOWLEDGMENTS

Footnotes

DOI:10.1091/mbc.01–05-0265.

Footnotes

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