Stochastic models for cell motion and taxis
Stochastic models for cell motion and taxis
复制标题
细胞运动和出租车的随机模型
DOI：
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发表时间：
2004
影响因子：
1.9
通讯作者：
George Oster
中科院分区：
文献类型：
--
作者：
E. Ionides;Kathy S. Fang;R. Rivkah Isseroff;George Oster
摘要：研究细胞运动的某些生物学实验产生的延时视频显微镜数据可以用随机微分方程建模。这些模型提出了用于量化实验结果和测试相关假设的统计数据，并对细胞的定性行为和潜在的生物物理机制产生了影响。定向细胞运动响应于刺激，称为的士，先前已建模在现象学水平上使用凯勒-西格尔扩散方程。凯勒-西格尔模型无法区分某些出租车模式，这促使引入了更丰富的模型，但仍然适用于统计分析。一个状态空间模型配方是用来链接模型提出的细胞速度观测数据。序贯蒙特卡罗方法可以通过最大似然法对一系列适用模型进行参数估计。一个特定的实验情况下，涉及的电场对细胞行为的影响，被认为是详细的。在这种情况下，奥恩斯坦-乌伦贝克模型的细胞速度被认为是比较有利的非线性扩散模型。
Abstract.Certain biological experiments investigating cell motion result in time lapse video microscopy data which may be modeled using stochastic differential equations. These models suggest statistics for quantifying experimental results and testing relevant hypotheses, and carry implications for the qualitative behavior of cells and for underlying biophysical mechanisms. Directional cell motion in response to a stimulus, termed taxis, has previously been modeled at a phenomenological level using the Keller-Segel diffusion equation. The Keller-Segel model cannot distinguish certain modes of taxis, and this motivates the introduction of a richer class of models which is nevertheless still amenable to statistical analysis. A state space model formulation is used to link models proposed for cell velocity to observed data. Sequential Monte Carlo methods enable parameter estimation via maximum likelihood for a range of applicable models. One particular experimental situation, involving the effect of an electric field on cell behavior, is considered in detail. In this case, an Ornstein- Uhlenbeck model for cell velocity is found to compare favorably with a nonlinear diffusion model.
影响因子：
3.4
作者：
Saxton, MJ
通讯作者：
Saxton, MJ
影响因子：
3.4
作者：
Shenderov, AD;Sheetz, MP
通讯作者：
Sheetz, MP