Gut Microbiota is a key actor for human health, driving many physiological and pathological processes, including immune system development and modulation. How this massive population of microorganisms, most of which are bacteria, establishes commensal, mutualistic or pathogenic interactions with the human host despite the vigilance of the immune system, is still obscure and requires an in-depth study. The story gets more intricate considering that gut is home for a myriad of Gram-negative bacteria whose outer membrane main constituent is the lipopolysaccharide (LPS). Due to its chemical structure, LPS is considered a potent elicitor of immune inflammatory reactions in mammals, being usually associated to perilous bacteria and detrimental outcomes for human health. Nevertheless, LPS also decorates the membrane of harmless and beneficial Gram-negatives of gut microbiota. How LPS is tolerated and remains (apparently) silent in the gut is a major unsolved question representing a frontier in our understanding of innate immunity. DEBUGGING-LPS project will contribute to answer this question, starting from the assumption that the chemistry of LPS is the real message taken from human host of the bacterial interaction, either beneficial or harmful. Strategically based on my expertise in organic chemistry, and integrating synthetic chemistry and cellular immunology studies, DEBUGGING-LPS will decrypt the 'chemical language' spoken by LPS in the gut. This project will deliver a clear picture of the chemistry at the basis of the difference between 'good' and 'bad' LPS, providing tools for the exploitation of the acquired knowledge to create novel therapeutics for resolving/mitigating immune disorders. DEBUGGING-LPS has been conceived to go beyond the state-of-the-art, breaking the dogma of LPS as an enemy, leaving space for a new vision of this glycomolecule: i.e. no longer as a toxic bacterial product rather as an immune signal vital for the proper functioning of our body.

This project will develop the At-home, Maximised Potential DNA/RNA Detecting Device (AMP’D). AMP'D is a hand-held, equipment free point-of-care device that can detect DNA/RNA biomarkers in easily accessible bodily fluids - such as blood, nasal and saliva samples. It is intended for diagnosing both infectious diseases and cancer. Additionally, it can do it at-home and early in disease progression. Both infectious diseases and cancer are leading causes of death globally. In 2020, cancer accounted for 10 million deaths. Furthermore, Covid-19 alone has accounted for 6.5 million deaths since the start of the pandemic. AMP'D intends to significantly reduce the number of deaths for both diseases - by providing a platform that can allow for ultra-sensitive, low cost and convenient detection. First, AMP'D can detect extremely low concentrations of biomarkers - at the early stages of these diseases. Second, AMP'D can cut the cost of each test from US$200 (commonly observed with pathological lab tests) to US$10. Finally, AMP'D can enable at-home diagnosis with blood, nasal and saliva samples - thus preventing invasive/uncomfortable procedures that deter many people from getting tested until it is too late. The development of such a device can enable end-users anywhere in the world to detect these diseases - even before symptoms appear. This can allow for early intervention before prognosis worsens - thus saving lives.

Despite containing the vast majority of carbon on the planet, the Earth’s subsurface represents a mostly uncharacterized frontier for biogeochemistry and microbial ecology. While carbon fixation at the Earth’s surface has been drawing a lot of attention for decades, subsurface carbon fixation remains poorly understood. Yet, metabolically, it engages a rich diversity of non canonical pathways, contrasting to surface carbon fixation dominated by the canonical Calvin cycle. Also, its contribution to the global carbon cycle, though long overlooked, is now garnering increased attention. In recent years, several studies aiming to characterise the diversity of carbon-fixing organisms within the subsurface have emerged. However, these studies are isolated and do not accurately describe the dependence of these metabolisms on the geoenvironmental context. I propose here to provide a blueprint of subsurface carbon fixation on Earth. In a top-down approach, I will systematically sample various subsurface environments for metagenomic and metaproteomic analysis of carbon fixing metabolisms, in search of the main environmental factors determining the carbon fixation strategy used. In parallel to this, I will undertake a bottom-up experimental approach to explore and confirm this metabolic versatility in response to changing environments, through controlled culturing experiments. Finally, I will integrate the acquired results with stable isotope analysis to build a metabolic model, aiming to comprehensively predict carbon fixation fluxes in function of the geoenvironmental context. The proposed holistic approach will increase our understanding of the role of subsurface microbial metabolisms in global carbon cycling.

The design and evaluation of mechanisms for aggregating preferences is a central problem in Multi-Agent Systems (MAS). In such setting, we need to be able to aggregate individual preferences, which are conflicting when agents are self-interested. More importantly, the mechanism should choose a socially desirable (or "good") outcome and reach an equilibrium despite the fact that agents can lie about their preferences. The real-world applications of designing and verifying mechanisms for social choice are manifold, including fair division protocols, secure voting, and truth-tracking via approval voting. Although logic-based languages have been widely used for verification and synthesis of MAS, the use of formal methods for reasoning about auctions under strategic behavior as well as automated mechanism design has not been much explored yet. An advantage in adopting such perspective lies in the high expressivity and generality of logics for strategic reasoning. Moreover, by relying on precise semantics, formal methods provide tools for rigorously analyzing the correctness of systems, which is important to improve trust in mechanisms generated by machines. This project aims to design a logical framework based on Strategy Logic (SL) for formally verifying and designing mechanisms for social choice. More specifically, we aim at (i) proposing an approach addressing the probabilistic setting (with Bayesian information, stochastic transitions and mixed strategies); (ii) identifying fragments of SL that enjoy both good complexity and satisfying expressive power for being applied to classes of mechanisms; (iii) modeling and reasoning about relevant problems from the state-of-the-art in computational social choice using the proposed logical framework; and (iv) methodically studying the obtained fragments in relation to the expressivity, model-checking and satisfiability problems.