Smell and survive: computational prediction of olfactory circuit activity and parasite-induced behavior in Drosophila

Abstract

While the fruit fly, Drosophila melanogaster, has been a celebrated model organism for over a century, this thesis leverages two advancements from the past decade: 1) the release of the “connectome” – a comprehensive wiring diagram between the ≈100,000 neurons of a full adult fly brain, and 2) the successful introduction into the laboratory of Entomophthora muscae, a parasitic fungus that manipulates the behavior of (and ultimately kills) flies. This work employs computational tools to make predictions at both the neural circuit level (Chapters 1 and 2, modelling the Drosophila olfactory circuit) and the behavioral level (Chapter 3, detecting flies exhibiting signs of parasitic infection). Chapter 1 explores how variation in neural circuit activity among individual flies underlies variation in behavioral responses to odors. We identify a site in the periphery of the fly odor-processing circuit in which calcium activity is predictive of an individual’s preference between a pair of odorants. By developing a connectome-based model of the olfactory circuit, I identify wiring variation strategies that result in patterns of simulated neural activity resembling patterns in empirical calcium. In Chapter 2, I introduce a Bayesian workflow for performing statistical inference using connectome-based biophysical models. Focusing again on odor processing, we study models of neuron dynamics with few fitting parameters and bring to bear the wealth of firing rate data for different components of the olfactory circuit. We compare classes of models with differing levels of biophysical detail, and assess their predictive power. Finally, in Chapter 3, I present a machine learning-based classifier that predicts with high precision whether a fly is exhibiting signs of infection by E. muscae. This tool enables real-time identification, enabling experiments that compare infected individuals with matched uninfected controls. On the whole, this thesis introduces computational techniques that provide insights into how individual brains differ and how to integrate data to constrain neural circuit models, and a method for behavioral phenotyping in a novel parasite-host laboratory system.