“We explore the molecular mechanisms underlying nervous system function by finding genes and gene networks that affect behaviour”
The goal of behavioural genomics is to understand the mapping between genome variation and behaviour. However, technology for sequencing and perturbing genomes is advancing more rapidly than our ability to assess all of the consequences of genetic perturbation. To help redress the imbalance between measures of genotype and phenotype, we are developing high-throughput imaging platforms to capture complex behavioural sequences and automated algorithms to interpret them.
Motor behaviour is a useful phenotype because it is the principal output of the nervous system and has previously been used to find genes with roles in synaptic transmission, neural development, and many kinds of sensation among other things. The nematode worm C. elegans is a great model for behavioural genomics in part because of its relatively simple and exceptionally well-characterised nervous system. Its locomotion is sufficiently complex to reliably identify subtle differences between mutants yet simple enough to quantify nearly completely. Well-developed reagents for imaging gene expression and neural activity make for a tight loop between hypothesis generating screens and hypothesis testing functional experiments.
A worm’s posture can be represented as a point in a compact “shape space”. In this representation, the series of postures a worm adopts while crawling traces out a line (the colour represents the position in the fourth dimension of the shape space). Each ‘bird’s nest’ in the image represents 15 minutes of crawling behaviour for a single worm. Some mutant worms cannot crawl smoothly or spend more time paused and their shape trajectories can be irregular or show spots. By quantitatively comparing worm shapes over time, it is possible to relate mutants to each other and make hypotheses for new genetic relationships.
Li K, Javer A, Keaveny EE, Brown AEX. (2017). Recurrent Neural Networks with Interpretable Cells Predict and Classify Worm Behaviour. BioRxiv 222208.
Gomez-Marin A, Stephens GJ, Brown AEX. (2016). Hierarchical compression of C. elegans locomotion reveals phenotypic differences in the organisation of behaviour. Journal of the Royal Society Interface , DOI: 10.1098/rsif.2016.0466.
Schwarz RF, Branicky R, Grundy LJ, Schafer WR, Brown AEX. (2015). Changes in Postural Syntax Characterize Sensory Modulation and Natural Variation of C. elegans Locomotion. PLOS Computational Biology DOI: 10.1371/journal.pcbi.1004322.
Brown AE, Yemini EI, Grundy LJ, Jucikas T, Schafer WR. (2013). A dictionary of behavioral motifs reveals clusters of genes affecting caenorhabditis elegans locomotion. PNAS 110(2), 791–796.
Yemini E, Jucikas T, Grundy LJ, Brown AE, Schafer WR. (2013). A database of caenorhabditis elegans behavioral phenotypes. Nature Methods 10(9), 877–879.