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12 - Automated behavioural fingerprinting of Caenorhabditis elegans mutants

Published online by Cambridge University Press:  05 July 2015

By
André E. X. Brown  and
William R. Schafer
Edited by
Florian Markowetz  and
Michael Boutros
André E. X. Brown
Affiliation:
Imperial College
William R. Schafer
Affiliation:
Medical Research Council Laboratory of Molecular Biology
Florian Markowetz
Affiliation:
Cancer Research UK Cambridge Institute
Michael Boutros
Affiliation:
German Cancer Research Center, Heidelberg
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Summary

Rapid advances in genetics, genomics and imaging have given insight into the molecular and cellular basis of behaviour in a variety of model organisms with unprecedented detail and scope. It is increasingly becoming routine to isolate behavioural mutants, clone and characterise mutant genes and discern the molecular and neural basis for a behavioural phenotype. Conversely, reverse genetic approaches have made it possible to straightforwardly identify genes of interest in whole-genome sequences and generate mutants that can be subjected to phenotypic analysis. In this latter approach, it is the phenol typing that presents the major bottleneck; when it comes to connecting phenotype to genotype in freely behaving animals, analysis of behaviour itself remains superficial and time-consuming. However, many proof-of-principle studies of automated behavioural analysis over the last decade have poised the field on the verge of exciting developments that promise to begin closing this gap.

In the broadest sense, our goal in this chapter is to explore what we can learn about the genes involved in neural function by carefully observing behaviour. This approach is rooted in model organism genetics but shares ideas with ethology and neuroscience, as well as computer vision and bioinformatics. After introducing Caenorhabditis elegans as a model, we will survey the research that has led to the current state of the art in worm behavioural phenol typing and present current research that is transforming our approach to behavioural genetics.

The worm as a model organism

Caenorhabditis elegans is a nematode worm that lives in bacteria-rich environments such as rotting fruit and has also been isolated from insects and snails which it is thought to use for longer-range transportation (Barriere & Felix 2005, Lee et al. 2011). In the laboratory, it is commonly cultured on the surface of agar plates seeded with a lawn of the bacterium Escherichia coli as a food source. On plates, worms lie on either their left or right side and crawl by propagating a sinuous dorso-ventral wave from head to tail.

Type
Chapter
Information
Systems Genetics
Linking Genotypes and Phenotypes
 , pp. 234 - 256
Publisher: Cambridge University Press
Print publication year: 2015

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References

Abramoff, M., Magalhaes, P. & Ram, S. (2004), ‘Image processing with ImageJ’, Biophotonics International 11 (7), 36–42.Google Scholar
Ahringer, J. (2006), ‘Reverse genetics’, WormBook, www. wormbook. org.
Albrecht, D. R. & Bargmann, C. I. (2011), ‘High-content behavioral analysis of Caenorhabditis elegans in precise spatiotemporal chemical environments’, Nature Methods 8(7), 599–605.CrossRefGoogle Scholar
Baek, J.-H., Cosman, P., Feng, Z., Silver, J. & Schafer, W. R. (2002), ‘Using machine vision to analyze and classify Caenorhabditis elegans behavioral phenotypes quantitatively’, Journal of Neuroscience Methods 118, 9–21.CrossRefGoogle Scholar
Bargmann, C. (2006), ‘Chemosensation in C. elegans’, WormBook, www. wormbook. org.
Barriere, A. & Felix, M.-A. (2005), ‘High local genetic diversity and low outcrossing rate in Caenorhabditis elegans natural populations’, Current Biology 15(13), 1176–1184.CrossRefGoogle Scholar
Berri, S., Boyle, J. H., Tassieri, M., Hope, I. A. & Cohen, N. (2009), ‘Forward locomotion of the nematode C. elegans is achieved through modulation of a single gait’, HFSP Journal 3(3), 186–193.CrossRefGoogle Scholar
Biron, D., Shibuya, M., Gabel, C., Wasserman, S. M., Clark, D. A. et al. (2006), ‘A diacyl-glycerol kinase modulates long-term thermotactic behavioral plasticity in C. elegans’, Nature Neuroscience 9(12), 1499–1505.CrossRefGoogle Scholar
Boyle, J. H., Berri, S., Tassieri, M., Hope, I. A. & Cohen, N. (2011), ‘Gait modulation in C. elegans: It's not a choice, it's a reflex!’, Frontiers in Behavioral Neuroscience 5, 10.CrossRefGoogle Scholar
Boyle, J. H., Bryden, J. & Cohen, N. (2008), ‘An integrated neuro-mechanical model of C. elegans forward locomotion’, in M., Ishikawa, K., Doya, H., Miyamoto & T., Yamakawa eds., Neural Information Processing, Vol. 4984, Springer, Berlin pp. 37–47.CrossRefGoogle Scholar
Brenner, S. (1974), ‘The genetics of Caenorhabditis elegans’, Genetics 77(1), 71–94.Google Scholar
Brown, A. E. X., Yemini, E. I., Grundy, L. J., Jucikas, T. & Schafer, W. R. (2012), ‘A dictionary of behavioral motifs reveals clusters of genes affecting Caenorhabditis elegans locomotion’, Proceedings of the National Academy of Sciences of the USA 110(2), 791–796.Google Scholar
C., elegans Sequencing Consortium (1998), ‘Genome sequence of the nematode C. elegans: A platform for investigating biology’, Science 282(5396), 2012–2018.Google Scholar
Calixto, A., Chelur, D., Topalidou, I., Chen, X. & Chalfie, M. (2010), ‘Enhanced neuronal RNAi in C. elegans using SID-1’, Nature Methods 7, 554–559.CrossRefGoogle Scholar
Chalasani, S. H., Chronis, N., Tsunozaki, M., Gray, J. M., Ramot, D. et al. (2007), ‘Dissecting a circuit for olfactory behaviour in Caenorhabditis elegans’, Nature 450(7166), 63–70.CrossRefGoogle Scholar
Chalfie, M., Sulston, J. E., White, J. G., Southgate, E., Thomson, J. N. et al. (1985), ‘The neural circuit for touch sensitivity in Caenorhabditis elegans’, Journal of Neuroscience 5(4), 956–964.CrossRefGoogle Scholar
Chokshi, T. V., Bazopoulou, D. & Chronis, N. (2010), ‘An automated microfluidic platform for calcium imaging of chemosensory neurons in Caenorhabditis elegans’, Lab on a Chip 10(20), 2758.CrossRefGoogle Scholar
Chronis, N. (2010), ‘Worm chips: Microtools for C. elegans biology’, Lab on a Chip 10(4), 432.CrossRefGoogle Scholar
Chronis, N., Zimmer, M. & Bargmann, C. I. (2007), ‘Microfluidics for in vivo imaging of neuronal and behavioral activity in Caenorhabditis elegans’, Nature Methods 4(9), 727–731.CrossRefGoogle Scholar
Chung, K. & Lu, H. (2009), ‘Automated high-throughput cell microsurgery on-chip’, Lab on a Chip 9(19), 2764.CrossRefGoogle Scholar
Croll, N. A. (1975a), ‘Behavioural analysis of nematode movement’, Advances in Parasitology 13, 71–122.Google Scholar
Croll, N. A. (1975b), ‘Components and patterns in the behaviour of the nematode Caenorhabditis elegans’, Journal of Zoology 176, 159–176.Google Scholar
Croll, N. A. & Blair, A. (1973), ‘Inherent movement patterns of larval nematodes, with a stochastic model to simulate movement of infective hookworm larvae’, Parasitology 67, 53.CrossRefGoogle Scholar
Cronin, C. J., Feng, Z. & Schafer, W. R. (2006), ‘Automated imaging of C. elegans behavior’, Methods in Molecular Biology 351, 241–251.Google Scholar
de Bono, M. & Bargmann, C. I. (1998), ‘Natural variation in a neuropeptide Y receptor homolog modifies social behavior and food response in C. elegans’, Cell 94(5), 679–689.CrossRefGoogle Scholar
Dusenbery, D. B. (1985a), ‘Using a microcomputer and video camera to simultaneously track 25 animals’, Computers in Biology and Medicine 15, 169–175.CrossRefGoogle Scholar
Dusenbery, D. B. (1985b), ‘Video camera-computer tracking of nematode Caenorhabditis elegans to record behavioral responses’, Journal of Chemical Ecology 11, 1239–1247.Google Scholar
Fang-Yen, C., Wyart, M., Xie, J., Kawai, R., Kodger, T. et al. (2010), ‘Biomechanical analysis of gait adaptation in the nematode Caenorhabditis elegans’, Proceedings ofthe National Academy of Sciences of the USA 107(47), 20 323–20 328.Google Scholar
Faumont, S., Rondeau, G., Thiele, T. R., Lawton, K. J., McCormick, K. E. et al. (2011), ‘An image-free opto-mechanical system for creating virtual environments and imaging neuronal activity in freely moving Caenorhabditis elegans’, PLoS One 6(9), e24666.CrossRefGoogle Scholar
Feng, Z., Cronin, C. J., Wittig, J. H. J., Sternberg, P. W. & Schafer, W. R. (2004), ‘An imaging system for standardized quantitative analysis of C. elegans behavior’, BMC Bioinformatics 5, 115.CrossRefGoogle Scholar
Fenno, L., Yizhar, O. & Deisseroth, K. (2011), ‘The development and application of optogenetics’, Annual Review ofNeuroscience 34(1), 389–412.CrossRefGoogle Scholar
Fontaine, E., Burdick, J. & Barr, A. (2006), ‘Automated tracking of multiple C. elegans’, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3716–3719.Google Scholar
Fraser, A. G., Kamath, R. S., Zipperlen, P., Martinez-Campos, M., Sohrmann, M. et al. (2000), ‘Functional genomic analysis of C. elegans chromosome I by systematic RNA interference’, Nature 408(6810), 325–330.CrossRefGoogle Scholar
Frøkjaer-Jensen, C., Davis, M. W., Hollopeter, G., Taylor, J., Harris, T. W. et al. (2010), ‘Targeted gene deletions in C. elegans using transposon excision’, Nature Methods 7(6), 451–453.CrossRefGoogle Scholar
Gaugler, R. & Bilgrami, A. L. (2004), Nematode Behaviour, CABI Publications, Wallingford, UK.CrossRefGoogle Scholar
Geng, W., Cosman, P., Berry, C. C., Feng, Z. & Schafer, W. R. (2004), ‘Automatic tracking, feature extraction and classification of C. elegans phenotypes’, IEEE Transactions on Bio-Medical Engineering 51(10), 1811–1820.CrossRefGoogle Scholar
Guo, Z. V., Hart, A. C. & Ramanathan, S. (2009), ‘Optical interrogation of neural circuits in Caenorhabditis elegans’, Nature Methods 6(12), 891–896.CrossRefGoogle Scholar
Hardaker, L. A., Singer, E., Kerr, R., Zhou, G. & Schafer, W. R. (2001), ‘Serotonin modulates locomotory behavior and coordinates egg-laying and movement in Caenorhabditis elegans’, Journal of Neurobiology 49(4), 303–313.CrossRefGoogle Scholar
Haspel, G., O'Donovan, M. J. & Hart, A. C. (2010), ‘Motoneurons dedicated to either forward or backward locomotion in the nematode Caenorhabditis elegans’, Journal of Neuroscience 30(33), 11 151–11 156.CrossRefGoogle ScholarPubMed
Hilliard, M. A., Apicella, A. J., Kerr, R., Suzuki, H., Bazzicalupo, P. et al. (2005), ‘In vivo imaging of C. elegans ASH neurons: Cellular response and adaptation to chemical repellents’, EMBO Journal 24(1), 63–72.CrossRefGoogle Scholar
Huang, K. -M., Cosman, P. & Schafer, W. R. (2006), ‘Machine vision based detection of omega bends and reversals in C. elegans’, Journal of Neuroscience Methods 158(2), 323–336.CrossRefGoogle Scholar
Huang, K.-M., Cosman, P. & Schafer, W. R. (2008), ‘Automated detection and analysis of foraging behavior in Caenorhabditis elegans’, Journal of Neuroscience Methods 171(1), 153–164.CrossRefGoogle Scholar
Hulme, S. E., Shevkoplyas, S. S., Apfeld, J., Fontana, W. & Whitesides, G. M. (2007), ‘A microfabricated array of clamps for immobilizing and imaging C. elegans’, Lab on a Chip 7(11), 1515.CrossRefGoogle Scholar
Kamath, R. S., Fraser, A. G., Dong, Y., Poulin, G., Durbin, R. et al. (2003), ‘Systematic functional analysis of the Caenorhabditis elegans genome using RNAi’, Nature 421(6920), 231–237.CrossRefGoogle Scholar
Kang, L., Gao, J., Schafer, W. R., Xie, Z. & Xu, X. S. (2010), ‘C. elegans TRP family protein TRP-4 is a pore-forming subunit of a native mechanotransduction channel’, Neuron 67(3), 381–391.CrossRefGoogle Scholar
Kawano, T., Po, M. D., Gao, S., Leung, G., Ryu, W. S. et al. (2011), ‘An imbalancing act: Gap junctions reduce the backward motor circuit activity to bias C. elegans for forward locomotion’, Neuron 72(4), 572–586.CrossRefGoogle Scholar
Kennedy, S., Wang, D. & Ruvkun, G. (2004), ‘A conserved siRNA-degrading RNase negatively regulates RNA interference in C. elegans’, Nature 427(6975), 645–649.CrossRefGoogle Scholar
Kerr, R., Lev-Ram, V., Baird, G., Vincent, P., Tsien, R. Y. et al. (2000), ‘Optical imaging of calcium transients in neurons and pharyngeal muscle of C. elegans’, Neuron 26(3), 583–594.CrossRefGoogle Scholar
Kimura, K. D., Miyawaki, A., Matsumoto, K. & Mori, I. (2004), ‘The C. elegans thermosensory neuron AFD responds to warming’, Current Biology 14(14), 1291–1295.CrossRefGoogle Scholar
Kindt, K. S., Viswanath, V., Macpherson, L., Quast, K., Hu, H. et al. (2007), ‘Caenorhabditis elegans TRPA-1 functions in mechanosensation’, Nature Neuroscience 10(5), 568–577.CrossRefGoogle Scholar
Korta, J., Clark, D. A., Gabel, C. V., Mahadevan, L. & Samuel, A. D. T. (2007), ‘Mechanosensation and mechanical load modulate the locomotory gait of swimming C. elegans’, Journal of Experimental Biology 210(13), 2383–2389.CrossRefGoogle Scholar
Krajacic, P., Shen, X., Purohit, P. K., Arratia, P. & Lamitina, T. (2012), ‘Biomechanical profiling of Caenorhabditis elegans motility’, Genetics 191(3), 1015–1021.CrossRefGoogle Scholar
Kralj, J. M., Douglass, A. D., Hochbaum, D. R., Maclaurin, D. & Cohen, A. E. (2011), ‘Optical recording of action potentials in mammalian neurons using a microbial rhodopsin’, Nature Methods 9(1), 90–95.Google Scholar
Lebois, F., Sauvage, P., Py, C., Cardoso, O., Ladoux, B. et al. (2012), ‘Locomotion control of Caenorhabditis elegans through confinement’, Biophysical Journal 102(12), 2791–2798.CrossRefGoogle Scholar
Lee, H., Choi, M.-K., Lee, D., Kim, H.-S., Hwang, H. et al. (2011), ‘Nictation, a dispersal behavior of the nematode Caenorhabditis elegans, is regulated by IL2 neurons’, Nature Neuroscience 15(1), 107–112.Google Scholar
Lehner, B., Calixto, A., Crombie, C., Tischler, J., Fortunato, A. et al. (2006), ‘Loss of LIN-35, the Caenorhabditis elegans ortholog of the tumor suppressor p105Rb, results in enhanced RNA interference’, Genome Biology 7(1), R4.CrossRefGoogle Scholar
Leifer, A. M., Fang-Yen, C., Gershow, M., Alkema, M. J. & Samuel, A. D. T. (2011), ‘Optogenetic manipulation of neural activity in freely moving Caenorhabditis elegans’, Nature Methods 8, 147–152.CrossRefGoogle Scholar
Li, W., Feng, Z., Sternberg, P. W. & Shawn Xu, X. Z. (2006), ‘A C. elegans stretch receptor neuron revealed by a mechanosensitive TRP channel homologue’, Nature 440(7084), 684–687.CrossRefGoogle Scholar
Lockery, S. R., Lawton, K. J., Doll, J. C., Faumont, S., Coulthard, S. M. et al. (2008), ‘Artificial dirt: Microfluidic substrates for nematode neurobiology and behavior’, Journal of Neurophysiology 99(6), 3136–3143.CrossRefGoogle Scholar
Looger, L. L. & Griesbeck, O. (2012), ‘Genetically encoded neural activity indicators’, Current Opinion in Neurobiology 22(1), 18–23.CrossRefGoogle Scholar
Luo, L., Clark, D. A., Biron, D., Mahadevan, L. & Samuel, A. D. T. (2006), ‘Sensorimotor control during isothermal tracking in Caenorhabditis elegans’, Journal of Experimental Biology 209(23), 4652–4662.CrossRefGoogle Scholar
Macosko, E. Z., Pokala, N., Feinberg, E. H., Chalasani, S. H., Butcher, R. A. et al. (2009), ‘A hub-and-spoke circuit drives pheromone attraction and social behaviour in C. elegans’, Nature 458(7242), 1171–1175.CrossRefGoogle Scholar
Mailler, R., Avery, J., Graves, J. & Willy, N. (2010), ‘A biologically accurate 3D model of the locomotion of Caenorhabditis elegans’, International Conference on Biosciences (BIOSCIENCESWORLD), pp. 84–90.Google Scholar
Miyawaki, A., Llopis, J., Heim, R., McCaffery, J. M., Adams, J. A. et al. (1997), ‘Fluorescent indicators for Ca2+ based on green fluorescent proteins and calmodulin’, Nature 388(6645), 882–887.CrossRefGoogle Scholar
Nagel, G., Brauner, M., Liewald, J. F., Adeishvili, N., Bamberg, E. et al. (2005), ‘Light activation of channelrhodopsin-2 in excitable cells of Caenorhabditis elegans triggers rapid behavioral responses’, Current Biology 15(24), 2279–2284.CrossRefGoogle Scholar
Nakai, J., Ohkura, M. & Imoto, K. (2001), ‘A high signal-to-noise Ca2+ probe composed of a single green fluorescent protein’, Nature Biotechnology 19(2), 137–141.CrossRefGoogle Scholar
Park, E. C. & Horvitz, H. R. (1986), ‘Mutations with dominant effects on the behavior and morphology of the nematode Caenorhabditis elegans’, Genetics 113(4), 821–852.Google Scholar
Pierce-Shimomura, J. T., Morse, T. M. & Lockery, S. R. (1999), ‘The fundamental role of pirouettes in Caenorhabditis elegans chemotaxis’, Journal of Neuroscience 19(21), 9557–9569.CrossRefGoogle Scholar
Qin, J. & Wheeler, A. R. (2007), ‘Maze exploration and learning in C. elegans’, Lab on a Chip 7(2), 186–192.CrossRefGoogle Scholar
Ramot, D., Johnson, B. E., Berry, T. L., Carnell, L. & Goodman, M. B. (2008), ‘The parallel worm tracker: A platform for measuring average speed and drug-induced paralysis in nematodes’, PLoS One 3, e2208.CrossRefGoogle Scholar
Roussel, N., Morton, C. A., Finger, F. P. & Roysam, B. (2007), ‘A computational model for C. elegans locomotory behavior: Application to multiworm tracking’, IEEE Transactions on Bio-Medical Engineering 54(10), 1786–1797.CrossRefGoogle Scholar
Sauvage, P., Argentina, M., Drappier, J., Senden, T., Simon, J. et al. (2011), ‘An elasto-hydrodynamical model of friction for the locomotion of Caenorhabditis elegans’, Journal of Biomechanics 44(6), 1117–1122.CrossRefGoogle Scholar
Simmer, F., Tijsterman, M., Parrish, S., Koushika, S. P., Nonet, M. L. et al. (2002), ‘Loss of the putative RNA-directed RNA polymerase RRF-3 makes C. elegans hypersensitive to RNAi’, Current Biology 12(15), 1317–1319.CrossRefGoogle Scholar
Stephens, G. J., Bueno de Mesquita, M., Ryu, W. S. & Bialek, W. (2011a), ‘Emergence of long timescales and stereotyped behaviors in Caenorhabditis elegans’, Proceedings of the National Academy of Sciences of the USA 108(18), 7286–7289.CrossRefGoogle Scholar
Stephens, G. J., Johnson-Kerner, B., Bialek, W. & Ryu, W. S. (2008), ‘Dimensionality and dynamics in the behavior of C. elegans’, PLoS Computational Biology 4, e1000028.CrossRefGoogle Scholar
Stephens, G. J., Osborne, L. C. & Bialek, W. (2011b), ‘Colloquium paper: Searching for simplicity in the analysis of neurons and behavior’, Proceedings of the National Academy of Sciences of the USA 108(Supplement3), 15 565–15 571.CrossRefGoogle Scholar
Stirman, J. N., Crane, M. M., Husson, S. J., Wabnig, S., Schultheis, C. et al. (2011), ‘Real-time multimodal optical control of neurons and muscles in freely behaving Caenorhabditis elegans’, Nature Methods 8, 153–158.CrossRefGoogle Scholar
Sulston, J. (1977), ‘Post-embryonic cell lineages of the nematode, Caenorhabditis elegans’, Developmental Biology 56(1), 110–156.CrossRefGoogle Scholar
Suzuki, H., Kerr, R., Bianchi, L., Frøkjaer-Jensen, C., Slone, D. et al. (2003), ‘In vivo imaging of C. elegans mechanosensory neurons demonstrates a specific role for the MEC-4 channel in the process of gentle touch sensation’, Neuron 39(6), 1005–1017.CrossRefGoogle Scholar
Suzuki, H., Thiele, T. R., Faumont, S., Ezcurra, M., Lockery, S. R. et al. (2008), ‘Functional asymmetry in Caenorhabditis elegans taste neurons and its computational role in chemotaxis’, Nature 454(7200), 114–117.CrossRefGoogle Scholar
Swierczek, N. A., Giles, A. C., Rankin, C. H. & Kerr, R. A. (2011), ‘High-throughput behavioral analysis in C. elegans’, Nature Methods 8, 592–598.CrossRefGoogle Scholar
Sznitman, J., Purohit, P. K., Krajacic, P., Lamitina, T. & Arratia, P. (2010a), ‘Material properties of Caenorhabditis elegans swimming at low Reynolds number’, Biophysical Journal 98(4), 617–626.CrossRefGoogle Scholar
Sznitman, J., Shen, X., Purohit, P. K. & Arratia, P. E. (2010b), ‘The effects of fluid viscosity on the kinematics and material properties of C. elegans swimming at low Reynolds number’, Experimental Mechanics 50(9), 1303–1311.CrossRefGoogle Scholar
Sznitman, R., Gupta, M., Hager, G. D., Arratia, P. E. & Sznitman, J. (2010c), ‘Multi-environment model estimation for motility analysis of Caenorhabditis elegans’, PLoS One 5, e11631.CrossRefGoogle Scholar
Tavernarakis, N., Shreffler, W., Wang, S. & Driscoll, M. (1997), ‘unc-8, a DEG/ENaC family member, encodes a subunit of a candidate mechanically gated channel that modulates C. elegans locomotion’, Neuron 18(1), 107–119.CrossRefGoogle Scholar
Tsibidis, G. D. & Tavernarakis, N. (2007), ‘Nemo: A computational tool for analyzing nematode locomotion’, BMC Neuroscience 8, 86.CrossRefGoogle Scholar
Vidal-Gadea, A., Topper, S., Young, L., Crisp, A., Kressin, L. et al. (2011), ‘Caenorhabditis elegans selects distinct crawling and swimming gaits via dopamine and serotonin’, Proceedings of the National Academy of Sciences of the USA 108(42), 17 504–17 509.CrossRefGoogle Scholar
Waggoner, L. E., Zhou, G. T., Schafer, R. W. & Schafer, W. R. (1998), ‘Control of alternative behavioral states by serotonin in Caenorhabditis elegans’, Neuron 21(1), 203–214.CrossRefGoogle Scholar
Wallace, H. R. (1968), ‘The dynamics of nematode movement’, Annual Review of Phytopathology 6(1), 91–114.CrossRefGoogle Scholar
Wang, W., Sun, Y., Dixon, S. J., Alexander, M. & Roy, P. J. (2009), ‘An automated micropositioning system for investigating C. elegans locomotive behavior’, Journal of the Association for Laboratory Automation 14, 269–276.CrossRefGoogle Scholar
White, J. G., Southgate, E., Thomson, J. N. & Brenner, S. (1986), ‘The structure of the nervous system of the nematode Caenorhabditis elegans’, Philosophical Transactions of the Royal Society B: Biological Sciences 314(1165), 1–340.CrossRefGoogle Scholar
Williams, P. L. & Dusenbery, D. B. (1990), ‘A promising indicator of neurobehavioral toxicity using the nematode Caenorhabditis elegans and computer tracking’, Toxicology and Industrial Health 6(3–4), 425–440.CrossRefGoogle Scholar
Yanik, M. F., Cinar, H., Cinar, H. N., Chisholm, A. D., Jin, Y. et al. (2004), ‘Neurosurgery: Functional regeneration after laser axotomy’, Nature 432(7019), 822.CrossRefGoogle Scholar
Yemini, E., Kerr, R. A. & Schafer, W. R. (2011a), ‘Illumination for worm tracking and behavioral imaging’, Cold Spring Harbor Protocols 2011(12), pdb. prot067009–pdb. prot067009.CrossRefGoogle Scholar
Yemini, E., Kerr, R. A. & Schafer, W. R. (2011b), ‘Preparation of samples for single-worm tracking’, Cold Spring Harbor Protocols 2011(12), pdb. prot066993-pdb. prot066993.CrossRefGoogle Scholar
Yook, K., Harris, T. W., Bieri, T., Cabunoc, A., Chan, J. et al. (2012), ‘WormBase 2012: More genomes, more data, new website’, Nucleic Acids Research 40, D735–741.CrossRefGoogle Scholar

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