Abstract
Over the last 30 years, computational biologists have developed increasingly realistic mathematical models of the regulatory networks controlling the division of eukaryotic cells. These models capture data resulting from two complementary experimental approaches: low-throughput experiments aimed at extensively characterizing the functions of small numbers of genes, and large-scale genetic interaction screens that provide a systems-level perspective on the cell division process. The former is insufficient to capture the interconnectivity of the genetic control network, while the latter is fraught with irreproducibility issues. Here, we describe a hybrid approach in which the 630 genetic interactions between 36 cell-cycle genes are quantitatively estimated by high-throughput phenotyping with an unprecedented number of biological replicates. Using this approach, we identify a subset of high-confidence genetic interactions, which we use to refine a previously published mathematical model of the cell cycle. We also present a quantitative dataset of the growth rate of these mutants under six different media conditions in order to inform future cell cycle models.
Highlights
Eukaryotic cells grow and divide using a highly conserved and integrated network of positive and negative controls that ensure genomic integrity and maintain cell size within reasonable bounds
We have addressed this problem by reconsidering the identification of synthetic lethal’ (SL) gene combinations of ‘cell-cycle control’ genes in budding yeast through a disciplined construction of replicate double-mutant strains based on a synthetic gene array (SGA) technology[28] pioneered by Tong and Boone[29] and the E-MAP28 workflow described by Schuldiner[30]
To ‘rescue’ swi4Δ cln3Δ cells, we significantly increased the activation of MBF (Swi[6]: Mbp1) by Bck[2], while simultaneously increasing the inactivation of MBF by Clb[2] and decreasing slightly the activation of MBF by Cln[3], in order to keep the level of MBF activity similar to that of the previous model, minimizing the perturbations to all other mutants that were previously explained by the model
Summary
Eukaryotic cells grow and divide using a highly conserved and integrated network of positive and negative controls that ensure genomic integrity and maintain cell size within reasonable bounds. For the purpose of modeling cell cycle control in budding yeast, it is crucial to have a reliable, well documented, independently confirmed set of SL gene combinations observed in a uniform genetic background. The limited accuracy of the model’s predictions is likely due to the fact that the parameter values in the model were estimated by fitting the model to ‘documented’ SL gene combinations that are themselves unreliable To correct this problem, we have reparametrized the model in light of the ‘high confidence’ SL and viable (V) interactions (shaded orange and blue, respectively in Fig. 4), allowing for some flexibility for the uncertain interactions. To further identify GIs among our set of cell cycle regulator genes that may not be apparent under standard growth conditions, we calculated fitness and GI scores for all double mutant progeny and single mutant parents in the presence of two different carbon sources and in the presence of three checkpoint activating drugs These gene combinations are more prone to result in outliers with higher than expected GI scores and should be interpreted with caution
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