DMS/NIGMS 1: Statistical modeling and estimation of cellular population dynamics

细胞培养测定是用于确定一组实验条件如何影响细胞生长的关键实验方法。 体外细胞群的生长和动力学。用于扰动条件的方法包括改变 干扰原的量或任何其它培养条件以测量响应。为了量化 在所测试的培养条件和群体生长动力学的变化之间，采用统计模型 来估计和预测这些影响。目前的方法将细胞计数视为统计模型中的响应变量 并将结果总结为IC50等指标。这些方法对于时间、播种计数、 或其它条件，并且可能导致再现性问题。不同的条件导致不同的结果， 即使生长动力学相同，相对细胞计数也是相同的，因此结果不容易推广到其他 场景在这里，我们提出了一个严格的机制为基础的方法估计的反应，使用 人口增长的数学模型，包括细胞分裂，死亡和状态之间的转换。率 事件的响应是分层统计模型中的响应，该模型允许条件甚至细胞系的可变性。的 result是一个分析平台，它将细胞内在特性而不是细胞计数视为结果， 不受实验持续时间、播种密度和其他因素的影响。我们提出这种新的方法作为一个独立的 分析任何细胞培养实验数据的框架。我们将细胞生长建模为分支过程， 描述了单个细胞或细胞类型如何分裂、死亡或经历细胞状态转换， 事件作为由比率参数化的随机变量。我们附上一个统计模型的利率作为一个函数， 关注的协变量。细胞计数形式的数据通过分支过程连接到速率，我们使用 贝叶斯方法来近似似然和估计模型的参数值。这种方法 创建了一个严格的框架，用于执行细胞反应的估计，作为获得的生长速率的函数， 从计数作为输入数据，更一般地用于支化过程参数的估计。我们将建立 统计方法的目的如下：（1）。我们将开发贝叶斯方法和统计框架， 估计细胞的出生率和死亡率。（2.）我们将创建一个分层模型框架来解释细胞系和 实验效果，以帮助创造可重复的结果。（3.）我们将为更复杂的分支开发建模 可以解释经历各种状态转换的种群的动态的过程， 差异化和规模。

Cell culture assays are a critical experimental method used to determine how a set of experimental conditions affect the growth and dynamics of a cell population in vitro. Methods used to perturb the conditions include varying the amount of perturbagen or any other culture condition to measure response. In order to quantify the relationship between the culture conditions tested and the change in population growth dynamics, a statistical model is employed to estimate and predict these effects. Current methods treat cell count as the response variable in statistical models and summarize the result in metrics like the IC50. These methods are not invariant to changes in time, seeding count, or other conditions, and can lead to reproducibility issues. Different conditions lead to different results in terms of relative cell count even if the growth dynamics are the same, so results are not easily generalizable to other scenarios. Here we propose a rigorous mechanism-based method for estimation of response that uses a mathematical model for population growth incorporating cell division, death, and transitions between states. The rate of events are the response in hierarchical statistical models that allows variability in conditions and even cell lines. The result is an analysis platform that treats cell-intrinsic properties rather than cell count as outcomes so that they are invariant to experimental duration, seeding density, and other factors. We propose this novel methodology as a standalone framework for analysis of any cell culture experimental data. We model cell growth as a branching process that describes how an individual cell or type of cells divide, die, or undergo cell state transitions by defining each of these events as random variables parameterized by the rates. We attach a statistical model for the rates as a function of covariates of interest. Data in the form of cell counts connects to rates through the branching process, and we use Bayesian methods to approximate the likelihood and estimate the parameter values of the model. This approach creates a rigorous framework for performing estimation of cellular response as a function of the growth rates obtained from counts as input data, and more generally for estimation of branching process parameters. We will establish the statistical methods in the following aims: (1.) We will develop Bayesian methods and a statistical framework for estimation of cell birth and death rates. (2.) We will create a hierarchical model framework to account for cell line and experimental effects to help create reproducible results. (3.) We will develop modeling for more complicated branching processes that can account for dynamics of a population undergoing a variety of state transitions including cycling, differentiation, and size.