Tan Cheemeng, Smith Robert Phillip, Tsai Ming-Chi, Schwartz Russell, You Lingchong
Lane Center of Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America; Department of Biomedical Engineering, University of California Davis, Davis, California, United States of America.
Division of Mathematics, Science and Technology, Nova Southeastern University, Fort Lauderdale, Florida, United States of America; Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America.
PLoS Comput Biol. 2014 Aug 7;10(8):e1003751. doi: 10.1371/journal.pcbi.1003751. eCollection 2014 Aug.
Fluctuations in the growth rate of a bacterial culture during unbalanced growth are generally considered undesirable in quantitative studies of bacterial physiology. Under well-controlled experimental conditions, however, these fluctuations are not random but instead reflect the interplay between intra-cellular networks underlying bacterial growth and the growth environment. Therefore, these fluctuations could be considered quantitative phenotypes of the bacteria under a specific growth condition. Here, we present a method to identify "phenotypic signatures" by time-frequency analysis of unbalanced growth curves measured with high temporal resolution. The signatures are then applied to differentiate amongst different bacterial strains or the same strain under different growth conditions, and to identify the essential architecture of the gene network underlying the observed growth dynamics. Our method has implications for both basic understanding of bacterial physiology and for the classification of bacterial strains.
在细菌生理学的定量研究中,不平衡生长期间细菌培养物生长速率的波动通常被认为是不理想的。然而,在严格控制的实验条件下,这些波动并非随机出现,而是反映了细菌生长背后的细胞内网络与生长环境之间的相互作用。因此,这些波动可被视为特定生长条件下细菌的定量表型。在此,我们提出一种方法,通过对以高时间分辨率测量的不平衡生长曲线进行时频分析来识别“表型特征”。然后,这些特征被用于区分不同的细菌菌株或同一菌株在不同生长条件下的差异,并识别观察到的生长动态背后基因网络的基本架构。我们的方法对深入理解细菌生理学以及细菌菌株的分类都具有重要意义。