Inferring density-dependent population dynamics mechanisms through rate disambiguation for logistic birth-death processes.
Inferring density-dependent population dynamics mechanisms through rate disambiguation for logistic birth-death processes.
复制标题
通过逻辑出生-死亡过程的速率消歧来推断密度依赖的种群动态机制。
DOI：
10.1007/s00285-023-01877-w
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发表时间：
2023
影响因子：
1.9
通讯作者：
Thomas,PeterJ
中科院分区：
文献类型：
--
作者：
Huynh,Linh;Scott,JacobG;Thomas,PeterJ
密度依赖性对于微生物和癌细胞的生态和进化非常重要。通常，我们只能测量净增长率，但产生观察到的动态的潜在密度依赖机制可以体现在出生过程、死亡过程或两者中。因此，我们利用细胞数量波动的均值和方差，从遵循逻辑增长的随机出生-死亡过程的时间序列中分别确定出生率和死亡率。我们的非参数方法为随机参数可识别性提供了一种新颖的视角，我们通过分析离散化箱大小的准确性来验证该方法。我们将我们的方法应用于同质细胞群经历三个阶段的场景：（1）自然生长至其承载能力，（2）用药物处理降低其承载能力，以及（3）克服药物作用以恢复其原始承载能力。在每个阶段，我们都明确了动态是否通过出生过程、死亡过程或两者的某种组合发生，这有助于理解耐药机制。在样本量有限的情况下，我们提供了一种基于最大似然的替代方法，并解决约束非线性优化问题，以确定给定单元数时间序列的最可能的密度依赖参数。我们的方法可以应用于不同规模的其他生物系统，以消除相同净增长率背后的密度依赖机制的歧义。
Density dependence is important in the ecology and evolution of microbial and cancer cells. Typically, we can only measure net growth rates, but the underlying density-dependent mechanisms that give rise to the observed dynamics can manifest in birth processes, death processes, or both. Therefore, we utilize the mean and variance of cell number fluctuations to separately identify birth and death rates from time series that follow stochastic birth-death processes with logistic growth. Our nonparametric method provides a novel perspective on stochastic parameter identifiability, which we validate by analyzing the accuracy in terms of the discretization bin size. We apply our method to the scenario where a homogeneous cell population goes through three stages: (1) grows naturally to its carrying capacity, (2) is treated with a drug that reduces its carrying capacity, and (3) overcomes the drug effect to restore its original carrying capacity. In each stage, we disambiguate whether the dynamics occur through the birth process, death process, or some combination of the two, which contributes to understanding drug resistance mechanisms. In the case of limited sample sizes, we provide an alternative method based on maximum likelihood and solve a constrained nonlinear optimization problem to identify the most likely density dependence parameter for a given cell number time series. Our methods can be applied to other biological systems at different scales to disambiguate density-dependent mechanisms underlying the same net growth rate.