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.
Keaveny EE, Brown AEX, 2017, Predicting path from undulations for C. elegans using linear and nonlinear resistive force theory, Physical Biology, Vol:14, ISSN:1478-3967
Gyenes B, Brown AEX, 2016, Deriving Shape-Based Features for C. elegans Locomotion Using Dimensionality Reduction Methods, Frontiers in Behavioral Neuroscience, Vol:10, ISSN:1662-5153
Gomez-Marin A, Stephens GJ, Brown AEX, 2016, Hierarchical compression of Caenorhabditis elegans locomotion reveals phenotypic differences in the organization of behaviour, Journal of the Royal Society Interface, Vol:13, ISSN:1742-5689
et al., 2015, Changes in Postural Syntax Characterize Sensory Modulation and Natural Variation of C.elegans Locomotion, Plos Computational Biology, Vol:11, ISSN:1553-734X
et al., Recurrent Neural Networks with Interpretable Cells Predict and Classify Worm Behaviour, Twenty-ninth Annual Conference on Neural Information Processing Systems (NIPS)