Pham Tuan D, Ichikawa Kazuhisa
Aizu Research Cluster for Medical Engineering and Informatics, Center for Advanced Information Science and Technology, The University of Aizu, 965-8580, Aizuwakamatsu, Fukushima, Japan.
Theor Biol Med Model. 2013 Oct 24;10:62. doi: 10.1186/1742-4682-10-62.
One of the most challenging problems in biological image analysis is the quantification of the dynamical mechanism and complexity of the intracellular space. This paper investigates potential spatial chaos and complex behavior of the intracellular space of typical cancer and normal cell images whose structural details are revealed by the combination of scanning electron microscopy and focused ion beam systems. Such numerical quantifications have important implications for computer modeling and simulation of diseases.
Cancer cell lines derived from a human head and neck squamous cell carcinoma (SCC-61) and normal mouse embryonic fibroblast (MEF) cells produced by focused ion beam scanning electron microscopes were used in this study. Spatial distributions of the organelles of cancer and normal cells can be analyzed at both short range and long range of the bounded dynamical system of the image space, depending on the orientations of the spatial cell. A procedure was designed for calculating the largest Lyapunov exponent, which is an indicator of the potential chaotic behavior in intracellular images. Furthermore, the sample entropy and regularity dimension were applied to measure the complexity of the intracellular images.
Positive values of the largest Lyapunov exponents (LLEs) of the intracellular space of the SCC-61 were obtained in different spatial orientations for both long-range and short-range models, suggesting the chaotic behavior of the cell. The MEF has smaller positive values of LLEs in the long range than those of the SCC-61, and zero vales of the LLEs in the short range analysis, suggesting a non-chaotic behavior. The intracellular space of the SCC-61 is found to be more complex than that of the MEF. The degree of complexity measured in the spatial distribution of the intracellular space in the diagonal direction was found to be approximately twice larger than the complexity measured in the horizontal and vertical directions.
Initial findings are promising for characterizing different types of cells and therefore useful for studying cancer cells in the spatial domain using state-of-the-art imaging technology. The measures of the chaotic behavior and complexity of the spatial cell will help computational biologists gain insights into identifying associations between the oscillation patterns and spatial parameters of cells, and appropriate model for simulating cancer cell signaling networks for cancer treatment and new drug discovery.
生物图像分析中最具挑战性的问题之一是对细胞内空间的动力学机制和复杂性进行量化。本文研究了典型癌细胞和正常细胞图像细胞内空间潜在的空间混沌和复杂行为，这些细胞图像的结构细节通过扫描电子显微镜和聚焦离子束系统的结合得以揭示。此类数值量化对于疾病的计算机建模和模拟具有重要意义。
本研究使用了由聚焦离子束扫描电子显微镜产生的源自人头颈鳞状细胞癌（SCC - 61）的癌细胞系和正常小鼠胚胎成纤维细胞（MEF）。根据图像空间有界动力学系统中空间细胞的方向，可在短程和长程范围内分析癌细胞和正常细胞细胞器的空间分布。设计了一个计算最大Lyapunov指数的程序，该指数是细胞内图像潜在混沌行为的指标。此外，应用样本熵和正则维数来测量细胞内图像的复杂性。
对于SCC - 61细胞内空间，在长程和短程模型的不同空间方向上均获得了最大Lyapunov指数（LLE）的正值，表明细胞具有混沌行为。MEF在长程范围内LLE的正值小于SCC - 61，在短程分析中LLE值为零，表明其具有非混沌行为。发现SCC - 61的细胞内空间比MEF的更复杂。在对角方向上细胞内空间空间分布测量的复杂程度约为水平和垂直方向测量复杂程度的两倍。
初步研究结果对于表征不同类型的细胞很有前景，因此对于使用先进成像技术在空间领域研究癌细胞很有用。空间细胞混沌行为和复杂性的测量将有助于计算生物学家深入了解识别细胞振荡模式与空间参数之间的关联，以及为癌症治疗和新药发现模拟癌细胞信号网络的合适模型。
