Lee Sang-Hee, Kang Seung-Ho
Division of Mathematical Modeling, National Institute for Mathematical Sciences, Daejeon, 305-811, Republic of Korea.
Department of Information Security, Dongshin University, Naju, 520-714, Republic of Korea.
Theory Biosci. 2015 Dec;134(3-4):117-25. doi: 10.1007/s12064-015-0213-7. Epub 2015 Aug 29.
The locomotion behavior of Caenorhabditis elegans has been studied extensively to understand the respective roles of neural control and biomechanics as well as the interaction between them. Constructing a mathematical model is helpful to understand the locomotion behavior in various surrounding conditions that are difficult to realize in experiments. In this study, we built three hidden Markov models (HMMs) for the crawling behavior of C. elegans in a controlled environment with no chemical treatment and in a formaldehyde-treated environment (0.1 and 0.5 ppm). The organism's crawling activity was recorded using a digital camcorder for 20 min at a rate of 24 frames per second. All shape patterns were quantified by branch length similarity (BLS) entropy and classified into four groups using the self-organizing map (SOM). Comparison of the simulated behavior generated by HMMs and the actual crawling behavior demonstrated that the HMM coupled with the SOM was successful in characterizing the crawling behavior. In addition, we briefly discussed the possibility of using the HMM together with BLS entropy to develop bio-monitoring systems to determine water quality.
秀丽隐杆线虫的运动行为已被广泛研究，以了解神经控制和生物力学各自的作用以及它们之间的相互作用。构建数学模型有助于理解在实验中难以实现的各种周围条件下的运动行为。在本研究中，我们针对秀丽隐杆线虫在无化学处理的受控环境以及甲醛处理环境（0.1 ppm和0.5 ppm）中的爬行行为构建了三个隐马尔可夫模型（HMM）。使用数码摄像机以每秒24帧的速率记录该生物体20分钟的爬行活动。所有形状模式通过分支长度相似性（BLS）熵进行量化，并使用自组织映射（SOM）分为四组。由HMM生成的模拟行为与实际爬行行为的比较表明，结合SOM的HMM成功地刻画了爬行行为。此外，我们简要讨论了将HMM与BLS熵一起用于开发生物监测系统以确定水质的可能性。
