Type 2 diabetes is a global epidemic, with prevalence of >500 million. The current obesogenic environment, favoring high caloric foods and physical inactivity, is a major driver of this epidemic. An evolutionarily conserved mechanism by which environmental factors impact whole body physiology is through internal biological clocks and the control of circadian rhythms. This machinery is a transcription/translation feedback loop that anticipates day/night cycles to optimize organismal physiology. However, the underlying mechanisms regulating metabolic rhythmicity and its role in type 2 diabetes pathogenesis remain enigmatic. Cellular energy sensors relay information about the environment to the circadian clock machinery, but the extent to which this biology can be modified to improve systemic metabolism is unknown. We will uncover mechanisms that underpin the relationship between the circadian clock, energy sensors, and metabolism and their dysfunction in type 2 diabetes. Our overarching hypothesis is that synchronizing energetic stressors to the molecular circadian clock may maximize the health benefits on metabolism. We will elucidate the mechanism by which the timing of energetic stressors acts on peripheral tissues controlling energy homeostasis. We will study temporal dynamics of cell and organ physiology, rather than snapshots in time. We will integrate “omics” analyses with rigorous physiological phenotyping of genetically modified mouse models, and clinical investigations in people with type 2 diabetes to temporally resolve dynamic networks of transcription, protein signaling, and metabolites, which synchronously control metabolism. In doing so, we will come closer to understanding the dynamic changes that occur with metabolic dysfunction. The work has the potential to make a breakthrough in clarifying underlying mechanisms for molecular regulation of metabolic rhythmicity, how this is perturbed in type 2 diabetes, and ultimately, insight into new treatments.

In vitro models—cells that are cultured outside the body, and studied as models of living organs or organisms—have been increasing their importance in a wide range of fields in biology and medicine. Yet they involve epistemic problems. How can simplified and experimentally modified cell culture systems serve as reliable models of living organs and organisms? How do they function in broader inferential practices in different contexts? How do experts of different fields, who have different aims and values, interact and collaborate to generate, study, and use in vitro models? This project will assess the prospects and limitations of in vitro models in comparative biology through answering the above questions. It will integrate philosophical analysis and empirical qualitative methods to examine a case study: in vitro comparative primatology. This is a rapidly developing interdisciplinary area of research, which studies different primate species by applying advance in vitro technology to serve research into human evolution, veterinary science, conservation, etc. Using this case study, this project will generate novel insights into how various experimental modifications, inferential practices, and social/institutional factors facilitate and/or constrain representational function of scientific models. This project will benefit from and contribute to the existing expertise at the Section for History and Philosophy of Science at the University of Copenhagen, which hosts and participates in research projects that examine epistemic and social implications of specific practices of biomedical research, including in vitro models by combining philosophical analysis and qualitative methods.

The overall objective of GALILEO is to rely on genuine Multi-Actor Approaches (MAA) to co-develop context-specific, people-centered agroforestry innovations in representative agro-pastoral, agroforestry, and agro-silvo-pastoral systems from Sub-Saharan Africa (SSA). The aim is to promote agroforestry as leverage to significantly improve agricultural, household, and climate change adaptation and mitigation performances and to enhance biodiversity in SSA. We build upon 8 agroforestry Living Labs (LLs: local scale and actors), 4 national and 1 regional Innovation Platforms (IPs), set up across 4 AU SSA countries. Our LLs are set in semi-arid zones of Senegal and Kenya and normally humid but drought-prone zones of Ghana and Cameroon thus comparing and covering a large range of SSA conditions. Through MMA, we co-construct potentially adoptable scenarios ex-ante with Innovator, Target, and Control actors in our LLs, then implement, assess, and compare performances in their pilot plots during the whole project. We use field observations also to calibrate process models, able to simulate under future CC scenarios. After full multi-criteria and trade-off analysis, we finally co-select the most effective scenarios ex-post. We thus rely on transdisciplinary research, providing qualitative and quantitative data on the biophysical, socio-economic, and environmental performances. Such adoptable agroforestry innovations will also enable farmers/pastoralists and stakeholders to diversify their incomes from new agroforestry value chains, of which 2 are GALILEO-original. They will also benefit from carbon farming and payment for ecosystem services opportunities. Through our IPs, we also engage in solid MAA collaborations and policy dialogues to first identify bottlenecks and second elaborate guidelines, and policy recommendations, helping towards strengthening their local innovation ecosystems, under a favorable institutional and policy framework.

In the brain, cells called astrocytes are as abundant as neurons, but are much less understood. Over the recent years, we have begun to appreciate how closely astrocytes support the development and function of synapses, the proposed substrate of memory. One emerging function of astrocytes is their direct involvement in learning and long-term memory. Indeed, astrocytic activity is affected in major brain disorders that affect memory, such as Alzheimer’s disease. The overarching aim of this fellowship is to study how astrocyte-neuron coordinated activity relates to learning and memory. I propose that astrocyte-neuron activity dynamics are shaped by learning over days to support memory formation. To test this hypothesis, I will use state-of-the-art microscopy and genetically encoded fluorescent proteins, both available in the host laboratory, to record the activity of large numbers of neurons and astrocytes while mice are trained in a perceptual learning task over days. In this way, I will be able to see how neurons and astrocytes change their activity in response to learning. I will then manipulate the activity of astrocytes using clever genetic tools and light, while the mice are being trained, and monitor the effects of this manipulation on the activity of neurons and memory performance. This step will allow me to establish causal links between astrocyte activity and memory. Finally, I will use modern statistical learning techniques to explore the contribution of astrocytes and astrocyte-neuron interactions to the computational capacity of the brain. This proposal capitalizes on my strong expertise in rodent behaviour and data analysis, my knowledge and strong interest on how experience shapes brain activity, and the established microscopy and genetic techniques in the host laboratory. Overall, this project will allow me to acquire new skills and knowledge in microscopy and astrocyte biology, and generate testable hypotheses for my future research.

Stellar streams are sensitive to both the distribution of dark matter and the population of dark matter subhalos in galaxies, which both vary depending on the nature of the dark matter particle. In galaxies beyond the Milky Way, extragalactic systems, we can apply a hierarchical inference approach, where we draw from expected distributions to look at thousands of stellar stream properties in a statistical sense. My proposed research will lay the theoretical groundwork and fill the missing gaps in our knowledge of streams in external galaxies to prepare for the wealth of incoming stellar stream data materializing over the next decade from the Nancy Grace Roman space telescope, the Vera C. Rubin Observatory and Euclid. The objectives of this proposal is: 1) to map where the missing thin stellar streams from globular clusters, which are most sensitive to perturbations from subhalos, are located in external galaxies, 2) to develop tools to recover dark matter potentials from stellar streams in external galaxies, and 3) to place constraints on dark matter substructure through statistical analyses of streams and underdensities in dwarf galaxies. To achieve these objectives, I will analyze publicly released catalogs of globular cluster formation and evolution models, develop numerical techniques to model multiple streams at once in external galaxies, and run state-of-the-art Nbody simulations of disrupting globular clusters in dwarfs. This work will facilitate direct comparisons between upcoming data and models from various dark matter particle candidate predictions. My proposed work provides a fundamental method of mapping the otherwise invisible dark matter, and will impact the interdisciplinary direction of dark matter research, in both particle physics and astrophysics.