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기계적으로 다른 환경에서 예쁜 꼬마선충의 기는 파형 변화

Effects of Mechanically Different Environments on the Crawling Waveform of Caenorhabditis Elegans

  • 김대연 (한국과학기술원 기계공학과) ;
  • 변수영 (한국과학기술원 기계공학과) ;
  • 김세호 (한국과학기술원 기계공학과) ;
  • 신현정 (한국과학기술원 기계공학과)
  • Kim, Dae-Yeon (Division of Mechanical Engineering, School of Mechanical, Aerospace and Systems Engineering, KAIST) ;
  • Byeon, Soo-Yung (Division of Mechanical Engineering, School of Mechanical, Aerospace and Systems Engineering, KAIST) ;
  • Kim, Se-Ho (Division of Mechanical Engineering, School of Mechanical, Aerospace and Systems Engineering, KAIST) ;
  • Shin, Jennifer Hyun-Jong (Division of Mechanical Engineering, School of Mechanical, Aerospace and Systems Engineering, KAIST)
  • 투고 : 2011.05.31
  • 심사 : 2011.11.03
  • 발행 : 2012.02.01

초록

예쁜 꼬마선충은 모델 생물로서 지금까지 행동과 이를 제어하는 신경세포들 사이의 관계를 밝히기 위한 많은 연구들이 수행되었다. 본 연구에서는 표면의 강성이 다른 고체 환경에서 꼬마선충의 운동관련 적응 행동을 연구하였다. 꼬마선충은 고체 위에서 움직일 때 기는 파형을 조절함으로써 기계적으로 다른 환경에 적응을 한다. 즉, 외부환경이 더 단단해질수록 꼬마선충의 기는 파형의 진폭과 파장이 감소하게 된다. 흥미로운 사실은 기계적인 감각에 결함이 있는 돌연변이의 경우 정상 꼬마선충과는 다른 적응행동을 보인다는 것이다. 이것은 기계적으로 다른 환경에 효과적으로 적응하기 위해서 기계적인 자극을 감지하고 반응하고 적응하는 기작이 있음을 의미한다. 이에 본 연구에서는 꼬마선충이 기계적으로 다른 환경에 적응하는 과정을 설명할 수 있는 신경회로 모델을 제안하였다.

The nematode Caenorhabditis elegans is a widely used model organism in biological research. Thanks to the availability of well-established knowledge about its neural connectivity, a wide range of studies have been attempted to uncover the relationship between behaviors and the responsible neurons. In our research, the adaptive behavior of C. elegans in solid environments with different surface rigidities is investigated, where the worm adapts to different mechanical stiffnesses by modulating its crawling waveform. The amplitude and wavelength of the crawling waveform decrease as the environment becomes more rigid. Interestingly, the mechanosensation-defective mutant shows different responses to the surface rigidity compared to those of the wild-type worm. To explain the adaptation process in mechanically different environments, we suggest a plausible neural circuit model.

키워드

참고문헌

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