Department of Bioengineering, BioCircuits Institute, Molecular Biology Section, Division of Biology, University of California, San Diego, La Jolla, CA 92093, USA.
Nucleic Acids Res. 2010 May;38(8):2676-81. doi: 10.1093/nar/gkp1069. Epub 2009 Dec 17.
Computational modeling of biological systems has become an effective tool for analyzing cellular behavior and for elucidating key properties of the intricate networks that underlie experimental observations. While most modeling techniques rely heavily on the concentrations of intracellular molecules, little attention has been paid to tracking and simulating the significant volume fluctuations that occur over each cell division cycle. Here, we use fluorescence microscopy to acquire single cell volume trajectories for a large population of Saccharomyces cerevisiae cells. Using this data, we generate a comprehensive set of statistics that govern the growth and division of these cells over many generations, and we discover several interesting trends in their size, growth and protein production characteristics. We use these statistics to develop an accurate model of cell cycle volume dynamics, starting at cell birth. Finally, we demonstrate the importance of tracking volume fluctuations by combining cell division dynamics with a minimal gene expression model for a constitutively expressed fluorescent protein. The significant oscillations in the cellular concentration of a stable, highly expressed protein mimic the observed experimental trajectories and demonstrate the fundamental impact that the cell cycle has on cellular functions.
生物系统的计算建模已成为分析细胞行为和阐明复杂网络关键特性的有效工具，这些网络是实验观察的基础。虽然大多数建模技术主要依赖于细胞内分子的浓度，但很少有人关注跟踪和模拟每个细胞分裂周期中发生的显著体积波动。在这里，我们使用荧光显微镜获取大量酿酒酵母细胞的单细胞体积轨迹。使用这些数据，我们生成了一套全面的统计数据，这些数据可以控制这些细胞在许多代中的生长和分裂，并且我们发现了它们在大小、生长和蛋白质产生特性方面的一些有趣趋势。我们使用这些统计数据，从细胞出生开始，开发了一个准确的细胞周期体积动力学模型。最后，我们通过将细胞分裂动力学与一个组成型表达荧光蛋白的最小基因表达模型相结合，证明了跟踪体积波动的重要性。稳定、高表达的蛋白质的细胞浓度的显著振荡模拟了观察到的实验轨迹，并证明了细胞周期对细胞功能的根本影响。
