ERC FUNDED PROJECTS

"Bridging the fields of Computer Animation and Computational Fabrication, this proposal will establish the foundations for algorithmic design of physical structures that can generate lifelike movements. Driven by embedded actuators, these types of structures will enable an abundance of possibilities for a wide array of real-world technologies: animatronic characters whose organic motions will enhance their ability to awe, entertain and educate; soft robotic creatures that are both skilled and safe to be around; patient-specific prosthetics and wearable devices that match the soft touch of the human body, etc. Recent advances in additive manufacturing (AM) technologies are particularly exciting in this context, as they allow us to create designs of unparalleled geometric complexity using a constantly expanding range of materials. And if past developments are an indication, within the next decade we will be able to fabricate physical structures that approach, at least at the macro scale, the functional sophistication of their biological counterparts. However, while this unprecedented capability enables fascinating opportunities, it also leads to an explosion in the dimensionality of the space that must be explored during the design process. As AM technologies keep evolving, the gap between ""what we can produce"" and ""what we can design"" is therefore rapidly growing. To effectively leverage the extraordinary design possibilities enabled by AM, 3DPBio will develop the computational and mathematical foundations required to study a fundamental scientific question: how are physical deformations, mechanical movements and overall functional capabilities governed by geometric shape features, material compositions and the design of compliant actuation systems? By enabling computers to reason about this question, our work will establish new ways to algorithmically create digital designs that can be turned into mechanical lifeforms at the push of a button."

2 000 000 €

Start date: 2020-02-01, End date: 2025-01-31

A central problem in developmental biology is to understand how tissues are patterned in time and space - how do identical cells differentiate to form the adult body plan? Patterns often arise from prior asymmetries in developing embryos, but there is also increasing evidence for self-organizing mechanisms that can break the symmetry of an initially homogeneous cell population. These patterning processes are mediated by a small number of signaling molecules, including the TGF-β superfamily members BMP and Nodal. While we have begun to analyze how biophysical properties such as signal diffusion and stability contribute to axis formation and tissue allocation during vertebrate embryogenesis, three key questions remain. First, how does signaling cross-talk control robust patterning in developing tissues? Opposing sources of Nodal and BMP are sufficient to produce secondary zebrafish axes, but it is unclear how the signals interact to orchestrate this mysterious process. Second, how do signaling systems self-organize to pattern tissues in the absence of prior asymmetries? Recent evidence indicates that axis formation in mammalian embryos is independent of maternal and extra-embryonic tissues, but the mechanism underlying this self-organized patterning is unknown. Third, what are the minimal requirements to engineer synthetic self-organizing systems? Our theoretical analyses suggest that self-organizing reaction-diffusion systems are more common and robust than previously thought, but this has so far not been experimentally demonstrated. We will address these questions in zebrafish embryos, mouse embryonic stem cells, and bacterial colonies using a combination of quantitative imaging, optogenetics, mathematical modeling, and synthetic biology. In addition to providing insights into signaling and development, this high-risk/high-gain approach opens exciting new strategies for tissue engineering by providing asymmetric or temporally regulated signaling in organ precursors.

1 997 750 €

Start date: 2020-07-01, End date: 2025-06-30

Today, our daily diet typically contains dozens of food additives (e.g. colours, emulsifiers, sweeteners: ~350 substances allowed on the EU market). Safety assessment is performed by health agencies to protect consumers against potential adverse effects of each additive, yet such an assessment is only based on current available evidence, i.e., for most additives, only in-vitro/in-vivo toxicological studies and exposure simulations. Meanwhile, the long-term health impact of additives intake and any potential ‘cocktail’ effects remain largely unknown and have become a source of serious concern. Growing evidence link the consumption of ultra-processed foods, containing numerous additives, to adverse health outcomes, in particular our recent results on cancer (Fiolet BMJ 2018). While most additives allowed in the EU are likely to be neutral for health and some may even be beneficial, recent animal and cell-based studies have suggested detrimental effects of several such compounds. In humans, data is lacking. No epidemiological study has ever assessed individual-level exposure to a wide range of food additives and its association with health, hampered by unsuited traditional dietary assessment tools facing the high additive content variability across commercial brands. Hence, a major breakthrough will come from the novel and unique tools I developed with my team, notably within the NutriNet-Santé cohort (n=164,000), collecting precise and repeated data on foods and beverages usually consumed, including names and brands of industrial products. With this unique resource, I propose a project at the forefront of international research to provide answers to a question of major importance for public health. Built as a combination of epidemiological studies and in-vitro/in-vivo experiments, this project will shed light on individual exposure to food additive 'cocktails' in relation to obesity, cancer, cardiovascular diseases and mortality, while depicting underlying mechanisms.

2 000 000 €

Start date: 2020-05-01, End date: 2025-04-30

Densities in neutron star (NS) cores can reach up to ten times the density of a normal atomic nucleus, and the stabilising effect of gravitational confinement permits long-timescale weak interactions. This generates nucleonic matter that is extremely neutron-rich, and the exciting possibility of stable states of strange matter (hyperons or deconfined quarks). Our uncertainty about the nature of cold ultradense matter is encoded in the Equation of State (EOS), which can be mapped via the stellar structure equations to quantities like mass M and radius R that determine the exterior space-time. One very promising technique for measuring the EOS exploits hotspots that form on the NS surface due to the pulsar mechanism, accretion streams, or during thermonuclear explosions in the stellar ocean. As the NS rotates, the hotspot gives rise to a pulsation and relativistic effects encode information about the EOS into the pulse profile. Pulse Profile Modelling (PPM), which employs relativistic ray-tracing and Bayesian inference codes to measure M-R and the EOS, is being pioneered by NASA’s NICER telescope, which is poised to deliver its first results in 2019. Complexities, that have only become apparent with exposure to real data, mean that there is work to be done if we are to have confidence in the nominal 5-10% accuracy of NICER’s M-R results. AEONS will deliver this. The project will also look ahead to the next generation of large-area X-ray timing telescopes, since it is only then that PPM will place tight constraints on dense matter models. The sources these missions target, accreting neutron stars, pose challenges for PPM such as variability, surface pattern uncertainty, and polarimetric signatures. AEONS will develop a robust pipeline for accreting NS PPM and embed it in a multi-messenger EOS inference framework with radio and gravitational wave constraints. This will ensure that PPM delivers major advances in our understanding of the nature of matter.

2 425 000 €

Start date: 2020-06-01, End date: 2025-05-31