Book contents
- Frontmatter
- Contents
- List of contributors
- 1 An introduction to systems genetics
- 2 Computational paradigms for analyzing genetic interaction networks
- 3 Mapping genetic interactions across many phenotypes in metazoan cells
- 4 Genetic interactions and network reliability
- 5 Synthetic lethality and chemoresistance in cancer
- 6 Joining the dots: network analysis of gene perturbation data
- 7 High-content screening in infectious diseases: new drugs against bugs
- 8 Inferring genetic architecture from systems genetics studies
- 9 Bayesian inference for model selection: an application to aberrant signalling pathways in chronic myeloid leukaemia
- 10 Dynamic network models of protein complexes
- 11 Phenotype state spaces and strategies for exploring them
- 12 Automated behavioural fingerprinting of Caenorhabditis elegans mutants
- Index
- Plate Section
- References
12 - Automated behavioural fingerprinting of Caenorhabditis elegans mutants
Published online by Cambridge University Press: 05 July 2015
- Frontmatter
- Contents
- List of contributors
- 1 An introduction to systems genetics
- 2 Computational paradigms for analyzing genetic interaction networks
- 3 Mapping genetic interactions across many phenotypes in metazoan cells
- 4 Genetic interactions and network reliability
- 5 Synthetic lethality and chemoresistance in cancer
- 6 Joining the dots: network analysis of gene perturbation data
- 7 High-content screening in infectious diseases: new drugs against bugs
- 8 Inferring genetic architecture from systems genetics studies
- 9 Bayesian inference for model selection: an application to aberrant signalling pathways in chronic myeloid leukaemia
- 10 Dynamic network models of protein complexes
- 11 Phenotype state spaces and strategies for exploring them
- 12 Automated behavioural fingerprinting of Caenorhabditis elegans mutants
- Index
- Plate Section
- References
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 GeneticsLinking Genotypes and Phenotypes, pp. 234 - 256Publisher: Cambridge University PressPrint publication year: 2015