Career: Neuronal Data Assimilation Tools And Models For Understanding Circadian Rhythms

Human beings possess an internal timekeeping mechanism known as the circadian clock that aligns physiological processes with the appropriate time of day, for example by stimulating the release of the sleep-promoting hormone melatonin in the evening and the wake-promoting hormone cortisol in the morning. Misalignment of the circadian clock with respect to 24-hour environmental cycles occurs after rapid travel across time zones and leads to symptoms of jet lag including sleep disturbances, digestive problems, and decreased cognitive performance. The circadian clock is highly conserved across animal species, and there are many similarities in the genes and neural circuits involved in the circadian systems of humans and the fruit fly Drosophila. The goal of this project is to obtain a mathematical understanding of the biology underlying jet lag by developing a detailed model of the circadian clock in Drosophila and then systematically analyzing how the clock is disrupted by sudden changes in the timing of the external light-dark cycle. The predictions of the computational model will then be tested in flies through experiments that simulate transmeridian travel. This project will also create new tools for building mathematical models directly from observed data by bringing together techniques from applied mathematics, statistics, and neuroscience. A unique graduate-level course at the intersection of these fields will be developed. Graduate, undergraduate, and community college students will be involved in this research and obtain interdisciplinary training. The undergraduate and community college students will form integrated summer research project teams. Community college students will be recruited to enroll in an undergraduate-level mathematical modeling course at the New Jersey Institute of Technology and will receive mentorship on pursuing four-year STEM degree program opportunities.This project focuses on the interface between dynamical systems and statistical data analysis in the context of neuronal modeling. The research goal of this project is to develop novel data assimilation methodologies for inferring models of neuronal dynamics directly from time-course data that enable insights into biological mechanisms. Specifically, this project will create new data assimilation tools to identify and parameterize neurophysiological models from voltage traces recorded from the Drosophila circadian (~24-hour) clock network. This research effort will address two major gaps in the mathematical/computational neuroscience field: a scarcity of methods for inferring parameters of unobserved ionic currents from measurements of membrane voltage alone, and a lack of models that link molecular, cellular, and behavioral scales. This will be accomplished by developing a method to design stimulus protocols for use in data assimilation algorithms that optimally unmask the dynamics of unobservable neuronal variables and improve inference of electrophysiological models and parameters. The method will be used to build a model of the Drosophila clock network that links circadian rhythms in gene expression to changes in neuronal activity and behavioral outputs. These models will be used to analyze how the internal circadian oscillator re-entrains following phase shifts in the external light-dark cycle. More generally, this work aims to elucidate fundamental aspects of synchronization and entrainment in oscillatory systems.