Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, Tennessee, United States of America.
PLoS One. 2010 Mar 10;5(3):e9636. doi: 10.1371/journal.pone.0009636.
Organisms, at scales ranging from unicellular to mammals, have been known to exhibit foraging behavior described by random walks whose segments confirm to Lévy or exponential distributions. For the first time, we present evidence that single cells (mammary epithelial cells) that exist in multi-cellular organisms (humans) follow a bimodal correlated random walk (BCRW).
METHODOLOGY/PRINCIPAL FINDINGS: Cellular tracks of MCF-10A pBabe, neuN and neuT random migration on 2-D plastic substrates, analyzed using bimodal analysis, were found to reveal the BCRW pattern. We find two types of exponentially distributed correlated flights (corresponding to what we refer to as the directional and re-orientation phases) each having its own correlation between move step-lengths within flights. The exponential distribution of flight lengths was confirmed using different analysis methods (logarithmic binning with normalization, survival frequency plots and maximum likelihood estimation).
CONCLUSIONS/SIGNIFICANCE: Because of the presence of non-uniform turn angle distribution of move step-lengths within a flight and two different types of flights, we propose that the epithelial random walk is a BCRW comprising of two alternating modes with varying degree of correlations, rather than a simple persistent random walk. A BCRW model rather than a simple persistent random walk correctly matches the super-diffusivity in the cell migration paths as indicated by simulations based on the BCRW model.
从单细胞生物到哺乳动物等生物体，其觅食行为一直被描述为随机漫步，其片段符合 Lévy 或指数分布。我们首次提出证据表明，多细胞生物（人类）中的单细胞（乳腺上皮细胞）遵循双峰相关随机漫步（BCRW）。
方法/主要发现：使用双峰分析对 MCF-10A pBabe、neuN 和 neuT 随机迁移的细胞轨迹进行分析，发现它们揭示了 BCRW 模式。我们发现两种类型的指数分布相关飞行（对应于我们所说的定向和重新定向阶段），每个飞行中的移动步长之间都有其自身的相关性。通过使用不同的分析方法（对数分箱归一化、生存频率图和最大似然估计）证实了飞行长度的指数分布。
结论/意义：由于飞行中移动步长的非均匀转角分布和两种不同类型的飞行的存在，我们提出上皮随机漫步是由两种交替模式组成的，其相关性程度不同，而不是简单的持续随机漫步。BCRW 模型而不是简单的持续随机漫步，根据基于 BCRW 模型的模拟，正确匹配了细胞迁移路径中的超扩散性。
