Africa project
Jet lag and chronobiology
Jet lag is a condition that occurs when individuals rapidly traverse multiple time zones, resulting in a disruption of their circadian rhythms. The circadian rhythm, also known as the body clock, is an internal biological system that regulates various physiological and behavioral processes on a roughly 24-hour cycle. It helps determine when we sleep, wake up, and carry out other bodily functions. When individuals engage in long-distance travel across time zones, their circadian rhythms become desynchronized from external environmental cues, leading to jet lag. This desynchronization causes a temporary misalignment between the body's internal timekeeping system and the local time at the destination.
In the context of traveling shift workers, similar challenges can arise. These individuals are frequently required to adapt to changing work schedules, disrupting their circadian rhythms. Their body clocks struggle to adjust to varying sleep and wake times, resulting in difficulties in maintaining optimal functioning and well-being.
Jet lag can manifest through a range of physical and psychological symptoms, including fatigue, insomnia, irritability, impaired concentration, gastrointestinal disturbances, and general discomfort. The severity and duration of these symptoms vary depending on factors such as the number of time zones crossed, the direction of travel, individual susceptibility, and the duration of the journey.
The primary objective of this project is to gain insights into the underlying mechanisms that contribute to the development of jet lag. Additionally, it aims to utilize and enhance mathematical models that effectively describe the phenomenon of jet lag. By employing these approaches, the project seeks to alleviate symptoms associated with jet lag and potentially improve the circumstances for individuals working in shifts.
The Groups within the project
In this project, the groups of Eva Winnebeck (University of Surrey \& Helmholtz Munich), Johannes Müller (TU München) and Atikunke Adabanji (KNUST Ghana) work together with their specific competences.
Eva Winnebeck, a biologist specializing in chronobiology, possesses expertise in both experimental and theoretical approaches, as well as statistical and modeling techniques. Under her guidance, Maximilian Ullrich, developed an extensive questionnaire administered to approximately 100 individuals undertaking long-distance flights. This questionnaire aimed to gather data on symptoms potentially related to jet lag, with participants reporting their experiences twice daily for one week before and one week after their flights. The comprehensive dataset generated from this study, combined with the Winnebeck laboratory's profound understanding of chronobiology, forms the experimental foundation of the project.
Atinuke Adebanji, a statistics professor, is focusing on the latest advancements in her field, specifically exploring the utilization of block statistics to extract information from intricate, high-dimensional datasets. Unlike traditional methods that treat all data as measurements within homogeneous populations stratified by certain variables, block analysis aims to maintain meaningful subgroups of the data together and compare outcomes within these subgroups. By adopting this approach, the level of noise is diminished, enabling the identification of hidden structures that may be challenging to discern using conventional methods.
Johannes Müller is specialized in applications of dynamical systems and stochastic processes on biological systems. In this project, the group of Müller aims to understand the existing chronobiological models, as the seminal Forger Jewett Kronauer model, particularlyconcerning jet lag. The aim is to adapt and improve these models, such that the tailored versions of the models could be of better help in understanding this phaenomenon.
At the moment, Franz Aschl (Müller group) and Emmanuel Owiredu (Adebanji group) are working together to get a better grip on the data from the statistical perspective, and to develop methods to validate mechanistic models for the data at hand. In that, TUM.Africa Talent is supporting the project by a grand.