A computational modeling and analysis in cell biological dynamics using electric cell-substrate impedance sensing (ECIS)
A computational modeling and analysis in cell biological dynamics using electric cell-substrate impedance sensing (ECIS)
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DOI：
10.1016/j.bios.2011.12.052
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发表时间：
2012-03-15
影响因子：
12.6
通讯作者：
Wang, Jong-Shyan
中科院分区：
文献类型：
--
作者：
Chen, Szi-Wen;Yang, Jen Ming;Wang, Jong-Shyan
本文对细胞动力学的计算建模和多尺度分析进行了研究。我们的研究旨在：（1）导出并验证细胞生长的数学模型，以及（2）在具有各种时间和频率分辨率的多个观测尺度上定量检测和分析生物学相互依赖性。这项研究是使用一种新的在线细胞监测技术，称为电细胞基质阻抗传感（ECIS），它允许连续跟踪细胞的行为，如粘附，增殖，扩散和微动实际测量的时间序列数据进行的。首先，将我们基于ECIS的细胞生长建模分析结果与使用不同时间间隔通过血细胞计数器测量确定的结果进行比较，我们发现从两种实验方法获得的结果一致。然而，我们的研究表明，它是更容易和更方便的操作与ECIS系统的在线细胞生长监测。其次，对于多尺度分析，我们的研究结果表明，所提出的基于小波的方法可以有效地量化与细胞微动相关的波动，并定量地捕获跨多个观测尺度的生物相互依赖性。请注意，虽然小波方法是众所周知的，它的应用到ECIS时间序列分析是新颖的，前所未有的计算细胞生物学。我们的分析表明，ECIS时间序列的研究可以提供一个有希望的开始和巨大的潜力，在建模和阐明复杂的细胞生物系统的机制。(C)2012 Elsevier B.V.保留所有权利。
In this paper, a study of computational modeling and multi-scale analysis in cell dynamics is presented. Our study aims at: (1) deriving and validating a mathematical model for cell growth, and (2) quantitatively detecting and analyzing the biological interdependencies across multiple observational scales with a variety of time and frequency resolutions. This research was conducted using the time series data practically measured from a novel on-line cell monitoring technique, referred to as electric cell-substrate impedance sensing (ECIS), which allows continuously tracking the cellular behavior such as adhesion, proliferation, spreading and micromotion. First, comparing our ECIS-based cellular growth modeling analysis results with those determined by hematocytometer measurement using different time intervals, we found that the results obtained from both experimental methods consistently agreed. However, our study demonstrated that it is much easier and more convenient to operate with the ECIS system for on-line cellular growth monitoring. Secondly, for multi-scale analysis our results showed that the proposed wavelet-based methodology can effectively quantify the fluctuations associated with cell micromotions and quantitatively capture the biological interdependencies across multiple observational scales. Note that although the wavelet method is well known, its application into the ECIS time series analysis is novel and unprecedented in computational cell biology. Our analyses indicated that the proposed study on ECIS time series could provide a hopeful start and great potentials in both modeling and elucidating the complex mechanisms of cell biological systems. (C) 2012 Elsevier B.V. All rights reserved.