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.

中文翻译：

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遗传相互作用源自6589个酵母细胞周期突变体的高通量表型。

在过去的30年中，计算生物学家已经开发出越来越现实的数学模型，用于控制真核细胞分裂的调控网络。这些模型捕获的数据来自两种互补的实验方法：旨在大量表征少量基因功能的低通量实验，以及为细胞分裂过程提供系统级视角的大规模遗传相互作用筛选。前者不足以捕捉遗传控制网络的互连性，而后者则充满了不可复制性的问题。在这里，我们描述了一种混合方法，其中36个细胞周期基因之间的630个遗传相互作用是通过高通量表型与空前的生物复制数量进行定量估计的。使用这种方法，我们确定了高可信度遗传相互作用的一个子集，我们用它来完善以前发布的细胞周期数学模型。我们还提出了在六个不同的媒体条件下这些突变体的增长率的定量数据集，以告知未来的细胞周期模型。