Imperial College London


Faculty of MedicineInstitute of Clinical Sciences

Senior Lecturer



+44 (0)20 3313 8218andre.brown




5006Hammersmith HospitalHammersmith Campus





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.



Ding SS, Romenskyy M, Sarkisyan KS, et al., 2020, Measuring Caenorhabditis elegans Spatial Foraging and Food Intake Using Bioluminescent Bacteria, Genetics, ISSN:0016-6731, Pages:genetics.302804.2019-genetics.302804.2019

Ding SS, Muhle LS, Brown AEX, et al., 2019, Comparison of solitary and collective foraging strategies ofCaenorhabditis elegansin patchy food distributions

Ding SS, Schumacher L, Javer A, et al., 2019, Shared behavioral mechanisms underlie C. elegans aggregation and swarming, Elife, Vol:8, ISSN:2050-084X


Javer A, Brown AEX, Kokkinos I, et al., 2019, Identification of C. elegans strains using a fully convolutional neural network on behavioural dynamics, Pages:455-464, ISSN:0302-9743

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