Sherer E, Tocce E, Hannemann R E, Rundell A E, Ramkrishna D
School of Chemical Engineering, Forney Hall of Chemical Engineering, 480 Stadium Mall Way, Purdue University, West Lafayette, Indiana 47907, USA.
Biotechnol Bioeng. 2008 Mar 1;99(4):960-74. doi: 10.1002/bit.21633.
A methodology is developed that determines age-specific transition rates between cell cycle phases during balanced growth by utilizing age-structured population balance equations. Age-distributed models are the simplest way to account for varied behavior of individual cells. However, this simplicity is offset by difficulties in making observations of age distributions, so age-distributed models are difficult to fit to experimental data. Herein, the proposed methodology is implemented to identify an age-structured model for human leukemia cells (Jurkat) based only on measurements of the total number density after the addition of bromodeoxyuridine partitions the total cell population into two subpopulations. Each of the subpopulations will temporarily undergo a period of unbalanced growth, which provides sufficient information to extract age-dependent transition rates, while the total cell population remains in balanced growth. The stipulation of initial balanced growth permits the derivation of age densities based on only age-dependent transition rates. In fitting the experimental data, a flexible transition rate representation, utilizing a series of cubic spline nodes, finds a bimodal G(0)/G(1) transition age probability distribution best fits the experimental data. This resolution may be unnecessary as convex combinations of more restricted transition rates derived from normalized Gaussian, lognormal, or skewed lognormal transition-age probability distributions corroborate the spline predictions, but require fewer parameters. The fit of data with a single log normal distribution is somewhat inferior suggesting the bimodal result as more likely. Regardless of the choice of basis functions, this methodology can identify age distributions, age-specific transition rates, and transition-age distributions during balanced growth conditions.
通过利用年龄结构的种群平衡方程，开发了一种方法来确定平衡生长期间细胞周期各阶段之间的年龄特异性转变率。年龄分布模型是解释单个细胞不同行为的最简单方法。然而，这种简单性被年龄分布观测的困难所抵消，因此年龄分布模型难以拟合实验数据。在此，所提出的方法仅基于在添加溴脱氧尿苷将总细胞群分为两个亚群后对总数密度的测量来识别人类白血病细胞（Jurkat）的年龄结构模型。每个亚群将暂时经历一段不平衡生长时期，这提供了足够的信息来提取年龄依赖性转变率，而总细胞群保持平衡生长。初始平衡生长的规定允许仅基于年龄依赖性转变率推导年龄密度。在拟合实验数据时，利用一系列三次样条节点的灵活转变率表示发现双峰G(0)/G(1)转变年龄概率分布最适合实验数据。由于从归一化高斯、对数正态或偏态对数正态转变年龄概率分布导出的更受限转变率的凸组合证实了样条预测，但需要的参数更少，所以这种分辨率可能不必要。用单一对数正态分布拟合数据有些逊色，表明双峰结果更有可能。无论基函数的选择如何，该方法都可以识别平衡生长条件下的年龄分布、年龄特异性转变率和转变年龄分布。
