ERC FUNDED PROJECTS

Mammalian organisms possess the remarkable ability to maintain internal body temperature (Tcore) within a narrow range close to 37°C despite wide environmental temperature variations. The brain’s neural “thermostat” is made up by central circuits in the hypothalamic preoptic area (POA), which orchestrate peripheral thermoregulatory responses to maintain Tcore. Thermogenesis requires metabolic fuel, suggesting intricate connections between the thermoregulatory centre and hypothalamic circuits controlling energy balance. How the POA detects and integrates temperature and metabolic information to achieve thermal balance is largely unknown. A major question is whether this circuitry could be harnessed therapeutically to treat obesity. We have recently identified the first known molecular temperature sensor in thermoregulatory neurons of the POA, transient receptor potential melastatin 2 (TRPM2), a thermo-sensitive ion channel. I aim to use TRPM2 as a molecular marker to gain access to and probe the function of thermoregulatory neurons in vivo. I propose a multidisciplinary approach, combining local, in vivo POA temperature stimulation with optogenetic circuit-mapping to uncover the molecular and cellular logic of the hypothalamic thermoregulatory centre and to assess its medical potential to counteract metabolic syndrome. Acclimation is a beneficial adaptive process that fortifies thermal responses upon environmental temperature challenges. Thermoregulatory neuron plasticity is thought to mediate acclimation. Conversely, maladaptive thermoregulatory changes affect obesity. The cell-type-specific neuronal plasticity mechanisms underlying these changes within the POA, however, are unknown. Using ex-vivo slice electrophysiology and in vivo imaging, I propose to characterize acclimation- and obesity-induced plasticity of thermoregulatory neurons. Ultimately, I aim to manipulate thermoregulatory neuron plasticity to test its potential counter-balancing effect on obesity.

1 902 500 €

Start date: 2018-09-01, End date: 2023-08-31

Gas turbines are an essential ingredient in the long-term energy and aviation mix. They are flexible, offer fast start-up and the ability to burn renewable-generated fuels. However, they generate NOx emissions, which cause air pollution and damage human health, and reducing these is an air quality imperative. A major hurdle to this is that lean premixed combustion, essential for further NOx emission reductions, is highly susceptible to thermoacoustic instability. This is caused by a two-way coupling between unsteady combustion and acoustic waves, and the resulting large pressure oscillations can cause severe mechanical damage. Computational methods for predicting thermoacoustic instability, fast and accurate enough to be used as part of the industrial design process, are urgently needed. The only computational methods with the prospect of being fast enough are those based on coupled treatment of the acoustic waves and unsteady combustion. These exploit the amenity of the acoustic waves to analytical modelling, allowing costly simulations to be directed only at the more complex flame. They show real promise: my group recently demonstrated the first accurate coupled predictions for lab-scale combustors. The method does not yet extend to industrial combustors, the more complex flow-fields in these rendering current acoustic models overly-simplistic. I propose to comprehensively overhaul acoustic models across the entirety of the combustor, accounting for real and important acoustic-flow interactions. These new models will offer the breakthrough prospect of extending efficient, accurate predictive capability to industrial combustors, which has a real chance of facilitating future, instability free, very low NOx gas turbines.

1 985 288 €

Start date: 2018-06-01, End date: 2023-05-31

Action potentials are not all equal. Despite shared biophysical principles and even similar action-potential shape, neurons with different spike generators can encode vastly different aspects of a stimulus and result in radically different behaviors of the embedding network. Differences between spike generators may be hard to discern because the information content of a spike train is not obvious to the naked eye. This is where computational analysis comes into play: theoretical research has shown that spike generation can be classified into a few dynamical types with qualitatively distinct computational properties. Among these, so-called homoclinic spikes – unlike the other commonly considered types – have been largely ignored. Yet, homoclinic spike generators are special because only they react with high sensitivity to inputs during the refractory period. Indeed, it is directly after a spike when homoclinic spikers “listen” best. As we recently demonstrated, this unique property has computationally exciting consequences: it can provoke a dramatic increase in network synchronization in response to minimal changes in physiological parameters, without requiring alterations in synaptic strength or connectivity. Supported by in-vitro evidence for homoclinic spiking in the rodent brain, ANewSpike explores the intriguing hypothesis that this “forgotten“ spike generator provides a unifying framework for the induction of epileptic activity by a wide range of physiological trigger parameters, from temperature to energy deprivation. Using a theory-experiment approach, we explore (i) the prevalence of homoclinic spiking in the brain, (ii) its ability to promote the transmission of high frequencies, and (iii) its ability to boost network synchronization. Our multi-scale study aims to add a novel dimension to our understanding of neural dynamics at the cellular and network level by revealing homoclinic spiking as an integral part of brain dynamics in both health and pathology.

2 000 000 €

Start date: 2021-01-01, End date: 2025-12-31

We propose focused theory developments and applications, which aim to substantially advance our ability to model and understand the behavior of molecules in complex environments. From a large repertoire of possible environments, we have chosen to concentrate on experimentally-relevant situations, including molecular fluctuations in electric and optical fields, disordered molecular crystals, solvated (bio)molecules, and molecular interactions at/through low-dimensional nanostructures. A challenging aspect of modeling such realistic environments is that both molecular electronic and nuclear fluctuations have to be treated efficiently at a robust quantum-mechanical level of theory for systems with 1000s of atoms. In contrast, the current state of the art in the modeling of complex molecular systems typically consists of Newtonian molecular dynamics employing classical force fields. We will develop radically new approaches for electronic and nuclear fluctuations that unify concepts and merge techniques from quantum-mechanical many-body Hamiltonians, statistical mechanics, density-functional theory, and machine learning. Our developments will be benchmarked using experimental measurements with terahertz (THz) spectroscopy, atomic-force and scanning tunneling microscopy (AFM/STM), time-of-flight (TOF) measurements, and molecular interferometry. Our final goal is to bridge the accuracy of quantum mechanics with the efficiency of force fields, enabling large-scale predictive quantum molecular dynamics simulations for complex systems containing 1000s of atoms, and leading to novel conceptual insights into quantum-mechanical fluctuations in large molecular systems. The project goes well beyond the presently possible applications and once successful will pave the road towards having a suite of first-principles-based modeling tools for a wide range of realistic materials, such as biomolecules, nanostructures, disordered solids, and organic/inorganic interfaces.

1 811 650 €

Start date: 2017-03-01, End date: 2022-08-31