Standaert A R, Geeraerd A H, Bernaerts K, Francois K, Devlieghere F, Debevere J, Van Impe J F
BioTeC-Bioprocess Technology and Control, Department of Chemical Engineering, Katholieke Universiteit Leuven, W. de Croylaan 46, B-3001 Leuven, Belgium.
Int J Food Microbiol. 2005 Apr 15;100(1-3):55-66. doi: 10.1016/j.ijfoodmicro.2004.10.033. Epub 2004 Dec 16.
The research presented in this paper analyses a newly developed experimental protocol for isolating single cells by constructing a simulation model of the process. The protocol involves sequential 50% dilutions of a cell suspension in a microtiter plate, so that eventually, wells are obtained containing exactly one cell. The aim of this modelling study is (i) to gain insight in the governing mechanisms of the dilution process, (ii) to confirm experimental findings and (iii) to enable the prediction of an average outcome for future experiments. The model construction process is presented chronologically. The initial basic model simulates the experiment as a sequence of binomial processes, using Monte Carlo techniques. Statistical analysis of the results shows that aggregational factors need to be taken into account in the form of a lognormal distribution. Several issues involved in this adaptation are discussed. To fully account for cell aggregation in the dilution process, a cell clumping algorithm is built into the simulation model. Simulation data from the resulting model show similar statistical characteristics as the experimental data and yield reliable prediction intervals for the available experimental data. The simulation model is a useful tool to support experimental findings and predict the outcome of future experiments. Even more importantly, this study emphasises the importance of careful statistical analysis in single cell research. The impact of stochastic effects is considerably amplified at the low cell concentrations involved and needs to be taken into account in any modelling effort.
本文所呈现的研究通过构建该过程的模拟模型,分析了一种新开发的用于分离单细胞的实验方案。该方案包括在微量滴定板中对细胞悬液进行连续50%的稀释,以便最终获得恰好含有一个细胞的孔。这项建模研究的目的是:(i)深入了解稀释过程的控制机制;(ii)证实实验结果;(iii)能够预测未来实验的平均结果。模型构建过程按时间顺序呈现。初始基本模型使用蒙特卡罗技术将实验模拟为一系列二项式过程。结果的统计分析表明,需要以对数正态分布的形式考虑聚集因素。讨论了这种调整中涉及的几个问题。为了充分考虑稀释过程中的细胞聚集,在模拟模型中内置了细胞团块算法。所得模型的模拟数据显示出与实验数据相似的统计特征,并为现有实验数据产生可靠的预测区间。该模拟模型是支持实验结果和预测未来实验结果的有用工具。更重要的是,本研究强调了在单细胞研究中进行仔细统计分析的重要性。在涉及的低细胞浓度下,随机效应的影响会显著放大,并且在任何建模工作中都需要考虑。