Analysis of single-cell measurements of bacterial growth and division often relied on testing preconceived models of cell size control mechanisms. Such an approach could limit the scope of data analysis and prevent us from uncovering new information. Here, we take an “agnostic” approach by applying regression methods to multiple simultaneously measured cellular variables, which allow us to infer dependencies among those variables from their apparent correlations. Besides previously observed correlations attributed to particular cell size control mechanisms, we identify dependencies that point to potentially new mechanisms. In particular, cells born smaller than their sisters tend to grow faster and make up for the size difference acquired during division. We also find that sister cells are correlated beyond what single-cell, size-control models predict. These trends are consistently found in repeat experiments, although the dependencies vary quantitatively. Such variation highlights the sensitivity of cell growth to environmental variations and the limitation of currently used experimental setups.

细菌生长和分裂的单细胞测量分析通常依赖于测试细胞大小控制机制的先入为主的模型。这种方法可能会限制数据分析的范围并阻止我们发现新信息。在这里，我们采用“不可知论”的方法，将回归方法应用于多个同时测量的细胞变量，这使我们能够从这些变量的明显相关性中推断出这些变量之间的依赖关系。除了先前观察到的归因于特定单元大小控制机制的相关性外，我们还确定了指向潜在新机制的依赖关系。特别是，出生时比其姐妹更小的细胞往往会生长得更快，并弥补在分裂过程中获得的大小差异。我们还发现姐妹细胞的相关性超出了单细胞，尺寸控制模型预测。尽管依赖性在数量上有所不同，但在重复实验中始终可以发现这些趋势。这种变化突出了细胞生长对环境变化的敏感性以及当前使用的实验装置的局限性。