Colorado State University, Chemical and Biological Engineering, Fort Collins, CO, USA.
New Culture, Inc., San Francisco, CA, USA.
NPJ Syst Biol Appl. 2020 May 6;6(1):11. doi: 10.1038/s41540-020-0134-z.
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.
在过去的 30 年里，计算生物学家已经开发出越来越逼真的调控网络数学模型，用于控制真核细胞的分裂。这些模型捕捉到了两种互补的实验方法所产生的数据：旨在广泛描述少数基因功能的低通量实验，以及提供细胞分裂过程系统水平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联性，而后者则存在不可重现性问题。在这里，我们描述了一种混合方法，通过高通量表型分析以空前数量的生物学重复来定量估计 36 个细胞周期基因之间的 630 个遗传相互作用。使用这种方法，我们确定了一组具有高可信度的遗传相互作用，并用它们来改进以前发表的细胞周期数学模型。我们还提供了一个定量数据集，其中包含这些突变体在六种不同培养基条件下的生长速率，以便为未来的细胞周期模型提供信息。
