A computational modeling and analysis in cell biological dynamics using electric cell-substrate impedance sensing (ECIS)

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• 作者：Szi-Wen Chen^a ; ^b ; Jen Ming Yang^c ; ^{jmyang@mail.cgu.edu.tw} ; Jhe-Hao Yang^a ; Shu Jyuan Yang^c ; Jong-Shyan Wang^d
• 关键词：Cell growth ; Cell micromotion ; Electric cell-substrate impedance sensing (ECIS) ; Multi-scale analysis ; Real-time cell monitoring
• 刊名：Biosensors and Bioelectronics
• 出版年：2012
• 期刊代码：12_09565663
• 类别：ch
• 出版时间：15 March, 2012
• 卷：33
• 期：1
• 页码：196-203
• 文件大小：1254 K

摘要

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.