The study of C. elegans behavior as a biophysical science

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We are studying “the Physics of Behavior” through interdisciplinary and quantitative studies of the model organism C. elegans. The biological sciences have developed powerful and sophisticated molecular tools to help us understand how biological components (genes, proteins, and cells) form the complex networks underlying behavior. However, the study of behavior itself has lagged far behind, and our lab is trying to address this gap. Through the use of novel behavioral measurement, instrumentation, and analysis, we strive to produce interpretable, widely applicable models of sensory behavior. In this pursuit we study the sensory behavior of the roundworm C. elegans, an ideal model for this type of work. It is a relatively simple organism (e.g. ~300 neurons, ~1000 cells), but it can perform a number of complex behaviors including searching, escaping, and associative learning in response to measurements it makes of its environment (temperature, chemicals, etc).*** Here I describe the evolution of one experiment to illustrate our interdisciplinary style and approach. To study the thermal “pain” response of C. elegans we developed a novel assay using an infra-red laser to programmatically apply a precise level of thermal heating to specific parts of C. elegans' body. Using this laser-based heating assay, we demonstrated the power of multi-parameter measurement on the analysis of thermal “pain” behavior of a candidate mutant strain library (Ghosh, 2012). We then improved this instrument to programmatically stimulate parts of the worm's body with tightly focused beams of infrared laser light, to determine the spatial receptive field of the worm's thermal pain response. Using multi-parameter quantification of the “pain” behavior we showed that: 1) C. elegans can discriminate thermal stimuli to a precision of at least 80 microns when focused on its mid-body, 2) the neuron PVD is the sensory neuron that is required for the mid-body response, and 3) a number of genes encoding glutamate receptors and TRPV-like channels are likely involved in this nociceptive transduction of heat (Mohammadi, 2013). We then mapped this high-dimensional behavioral data and their dynamics onto a lower dimensional space to allow for coarse grain modeling. Using this data, we then produced a quantitative, statistical model for thermal pain transduction, where the worm's level of pain is inferred by reading the “body language” of its behavioral response. Using this model we objectively determined changes in pain transduction due to perturbations of genes, neurons, or experience (Leung, 2016). On the same data set, we took a different approach and used an automated inference technique to generate a set of precise, predictive, and interpretable differential equations of this pain behavior (Daniels, 2018). This successful discovery of the dynamical system underlying the escape response illustrates how machine learning techniques can assist in the discovery of “equations of behavior.”**